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McKinsey Problem Solving: Six steps to solve any problem and tell a persuasive story
The McKinsey problem solving process is a series of mindset shifts and structured approaches to thinking about and solving challenging problems. It is a useful approach for anyone working in the knowledge and information economy and needs to communicate ideas to other people.
Over the past several years of creating StrategyU, advising an undergraduates consulting group and running workshops for clients, I have found over and over again that the principles taught on this site and in this guide are a powerful way to improve the type of work and communication you do in a business setting.
When I first set out to teach these skills to the undergraduate consulting group at my alma mater, I was still working at BCG. I was spending my day building compelling presentations, yet was at a loss for how to teach these principles to the students I would talk with at night.
Through many rounds of iteration, I was able to land on a structured process and way of framing some of these principles such that people could immediately apply them to their work.
While the “official” McKinsey problem solving process is seven steps, I have outline my own spin on things – from experience at McKinsey and Boston Consulting Group. Here are six steps that will help you solve problems like a McKinsey Consultant:
Step #1: School is over, stop worrying about “what” to make and worry about the process, or the “how”
When I reflect back on my first role at McKinsey, I realize that my biggest challenge was unlearning everything I had learned over the previous 23 years. Throughout school you are asked to do specific things. For example, you are asked to write a 5 page paper on Benjamin Franklin — double spaced, 12 font and answering two or three specific questions.
In school, to be successful you follow these rules as close as you can. However, in consulting there are no rules on the “what.” Typically the problem you are asked to solve is ambiguous and complex — exactly why they hire you. In consulting, you are taught the rules around the “how” and have to then fill in the what.
The “how” can be taught and this entire site is founded on that belief. Here are some principles to get started:
Step #2: Thinking like a consultant requires a mindset shift
There are two pre-requisites to thinking like a consultant. Without these two traits you will struggle:
- A healthy obsession looking for a “better way” to do things
- Being open minded to shifting ideas and other approaches
In business school, I was sitting in one class when I noticed that all my classmates were doing the same thing — everyone was coming up with reasons why something should should not be done.
As I’ve spent more time working, I’ve realized this is a common phenomenon. The more you learn, the easier it becomes to come up with reasons to support the current state of affairs — likely driven by the status quo bias — an emotional state that favors not changing things. Even the best consultants will experience this emotion, but they are good at identifying it and pushing forward.
Key point : Creating an effective and persuasive consulting like presentation requires a comfort with uncertainty combined with a slightly delusional belief that you can figure anything out.
Step #3: Define the problem and make sure you are not solving a symptom
Before doing the work, time should be spent on defining the actual problem. Too often, people are solutions focused when they think about fixing something. Let’s say a company is struggling with profitability. Someone might define the problem as “we do not have enough growth.” This is jumping ahead to solutions — the goal may be to drive more growth, but this is not the actual issue. It is a symptom of a deeper problem.
Consider the following information:
- Costs have remained relatively constant and are actually below industry average so revenue must be the issue
- Revenue has been increasing, but at a slowing rate
- This company sells widgets and have had no slowdown on the number of units it has sold over the last five years
- However, the price per widget is actually below where it was five years ago
- There have been new entrants in the market in the last three years that have been backed by Venture Capital money and are aggressively pricing their products below costs
In a real-life project there will definitely be much more information and a team may take a full week coming up with a problem statement . Given the information above, we may come up with the following problem statement:
Problem Statement : The company is struggling to increase profitability due to decreasing prices driven by new entrants in the market. The company does not have a clear strategy to respond to the price pressure from competitors and lacks an overall product strategy to compete in this market.
Step 4: Dive in, make hypotheses and try to figure out how to “solve” the problem
Now the fun starts!
There are generally two approaches to thinking about information in a structured way and going back and forth between the two modes is what the consulting process is founded on.
First is top-down . This is what you should start with, especially for a newer “consultant.” This involves taking the problem statement and structuring an approach. This means developing multiple hypotheses — key questions you can either prove or disprove.
Given our problem statement, you may develop the following three hypotheses:
- Company X has room to improve its pricing strategy to increase profitability
- Company X can explore new market opportunities unlocked by new entrants
- Company X can explore new business models or operating models due to advances in technology
As you can see, these three statements identify different areas you can research and either prove or disprove. In a consulting team, you may have a “workstream leader” for each statement.
Once you establish the structure you you may shift to the second type of analysis: a bottom-up approach . This involves doing deep research around your problem statement, testing your hypotheses, running different analysis and continuing to ask more questions. As you do the analysis, you will begin to see different patterns that may unlock new questions, change your thinking or even confirm your existing hypotheses. You may need to tweak your hypotheses and structure as you learn new information.
A project vacillates many times between these two approaches. Here is a hypothetical timeline of a project:
![analytics methodology and problem solving framework Strategy consulting process](https://i0.wp.com/strategyu.co/wp-content/uploads/2017/07/Consulting-Process.png?resize=1024%2C569&ssl=1)
Step 5: Make a slides like a consultant
The next step is taking the structure and research and turning it into a slide. When people see slides from McKinsey and BCG, they see something that is compelling and unique, but don’t really understand all the work that goes into those slides. Both companies have a healthy obsession (maybe not to some people!) with how things look, how things are structured and how they are presented.
They also don’t understand how much work is spent on telling a compelling “story.” The biggest mistake people make in the business world is mistaking showing a lot of information versus telling a compelling story. This is an easy mistake to make — especially if you are the one that did hours of analysis. It may seem important, but when it comes down to making a slide and a presentation, you end up deleting more information rather than adding. You really need to remember the following:
Data matters, but stories change hearts and minds
Here are four quick ways to improve your presentations:
Tip #1 — Format, format, format
Both McKinsey and BCG had style templates that were obsessively followed. Some key rules I like to follow:
- Make sure all text within your slide body is the same font size (harder than you would think)
- Do not go outside of the margins into the white space on the side
- All titles throughout the presentation should be 2 lines or less and stay the same font size
- Each slide should typically only make one strong point
Tip #2 — Titles are the takeaway
The title of the slide should be the key insight or takeaway and the slide area should prove the point. The below slide is an oversimplification of this:
![analytics methodology and problem solving framework Example of a single slide](https://i0.wp.com/strategyu.co/wp-content/uploads/2017/07/square.png?resize=558%2C313&ssl=1)
Even in consulting, I found that people struggled with simplifying a message to one key theme per slide. If something is going to be presented live, the simpler the better. In reality, you are often giving someone presentations that they will read in depth and more information may make sense.
To go deeper, check out these 20 presentation and powerpoint tips .
Tip #3 — Have “MECE” Ideas for max persuasion
“MECE” means mutually exclusive, collectively exhaustive — meaning all points listed cover the entire range of ideas while also being unique and differentiated from each other.
An extreme example would be this:
- Slide title: There are seven continents
- Slide content: The seven continents are North America, South America, Europe, Africa Asia, Antarctica, Australia
The list of continents provides seven distinct points that when taken together are mutually exclusive and collectively exhaustive . The MECE principle is not perfect — it is more of an ideal to push your logic in the right direction. Use it to continually improve and refine your story.
Applying this to a profitability problem at the highest level would look like this:
Goal: Increase profitability
2nd level: We can increase revenue or decrease costs
3rd level: We can increase revenue by selling more or increasing prices
Each level is MECE. It is almost impossible to argue against any of this (unless you are willing to commit accounting fraud!).
Tip #4 — Leveraging the Pyramid Principle
The pyramid principle is an approach popularized by Barbara Minto and essential to the structured problem solving approach I learned at McKinsey. Learning this approach has changed the way I look at any presentation since.
Here is a rough outline of how you can think about the pyramid principle as a way to structure a presentation:
![analytics methodology and problem solving framework pyramid principle structure](https://i0.wp.com/strategyu.co/wp-content/uploads/2019/08/Pyramid.png?resize=586%2C204&ssl=1)
As you build a presentation, you may have three sections for each hypothesis. As you think about the overall story, the three hypothesis (and the supporting evidence) will build on each other as a “story” to answer the defined problem. There are two ways to think about doing this — using inductive or deductive reasoning:
![analytics methodology and problem solving framework deductive versus inductive reasoning in powerpoint arguments](https://i0.wp.com/cdn-images-1.medium.com/max/800/0*DjtUkjVvWJ9fWT0C.png?resize=578%2C295&ssl=1)
If we go back to our profitability example from above, you would say that increasing profitability was the core issue we developed. Lets assume that through research we found that our three hypotheses were true. Given this, you may start to build a high level presentation around the following three points:
![analytics methodology and problem solving framework example of hypotheses confirmed as part of consulting problem solving](https://i0.wp.com/cdn-images-1.medium.com/max/800/0*SFVWepOPhxf5xAgX.png?w=1170&ssl=1)
These three ideas not only are distinct but they also build on each other. Combined, they tell a story of what the company should do and how they should react. Each of these three “points” may be a separate section in the presentation followed by several pages of detailed analysis. There may also be a shorter executive summary version of 5–10 pages that gives the high level story without as much data and analysis.
Step 6: The only way to improve is to get feedback and continue to practice
Ultimately, this process is not something you will master overnight. I’ve been consulting, either working for a firm or on my own for more than 10 years and am still looking for ways to make better presentations, become more persuasive and get feedback on individual slides.
The process never ends.
The best way to improve fast is to be working on a great team . Look for people around you that do this well and ask them for feedback. The more feedback, the more iterations and more presentations you make, the better you will become. Good luck!
If you enjoyed this post, you’ll get a kick out of all the free lessons I’ve shared that go a bit deeper. Check them out here .
Do you have a toolkit for business problem solving? I created Think Like a Strategy Consultant as an online course to make the tools of strategy consultants accessible to driven professionals, executives, and consultants. This course teaches you how to synthesize information into compelling insights, structure your information in ways that help you solve problems, and develop presentations that resonate at the C-Level. Click here to learn more or if you are interested in getting started now, enroll in the self-paced version ($497) or hands-on coaching version ($997). Both versions include lifetime access and all future updates.
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Top 9 Business Analysis Frameworks: Most Popular Techniques
- Business Analysis
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Key takeaways
- The top 9 business analysis frameworks offer a diverse set of tools and methodologies to address complex business challenges, uncover opportunities, and drive strategic initiatives
- The frameworks offer structured approaches to problem-solving , enabling organizations to address complex issues and drive continuous improvement.
- The diverse nature of these frameworks allows organizations to adapt their analytical approaches to various business scenarios and industry contexts.
Navigating the complex world of business can often feel like trying to solve a giant jigsaw puzzle, with each piece representing a different challenge or opportunity. That’s where business analysis frameworks come in handy!
If you are a business analyst, you know that understanding the ins and outs of the business is crucial for achieving successful results in business analysis activities.
In this article, we will provide you with a list of the 9 best business analysis framework techniques that will help you achieve clarity and most importantly, aid your professional development.
What Is a Business Analysis Framework?
A Business Analysis Framework is a structured approach used by Business Analysts to understand, assess, and improve various aspects of a business . It provides a systematic way to identify, define, and address business needs and challenges.
A Business Analysis Framework is designed to help organizations analyze, strategize, and execute their operations. It provides a framework for thinking and communicating about various aspects of the business . It is a conceptual and real blueprint that involves and describes all the vital requirements of running a business.
A Business Analysis Framework typically includes the following steps:
- Identifying the problem: This involves identifying the business problem or opportunity that needs to be addressed. This step is critical as it sets the stage for the rest of the analysis.
- Defining the scope: This involves defining the boundaries of the analysis and determining what is included and what is not included in the analysis.
- Gathering information: This involves collecting data and information related to the problem or opportunity. This step is critical as it provides the basis for the analysis.
- Analyzing the information: This involves analyzing the data and information collected in the previous step. This step is critical as it provides insights into the problem or opportunity.
- Developing solutions: This involves developing potential solutions to the problem or opportunity. This step is critical as it provides options for addressing the problem or opportunity.
- Implementing solutions: This involves implementing the selected solution(s) to the problem or opportunity. This step is critical as it turns the analysis into action.
- Monitoring and evaluating: This involves monitoring and evaluating the effectiveness of the solution(s) implemented. This step is critical as it ensures that the problem or opportunity has been fully addressed and that the solution(s) implemented are effective.
A Business Analysis Framework can be used for various purposes, such as:
- Improving business processes
- Developing new products or services
- Evaluating and selecting software solutions
- Conducting feasibility studies
- Developing business cases
![Top 9 Business Analysis Frameworks: Most Popular Techniques 1 A group of business analysts sitting at a table in front of a large screen, using business analysis frameworks](https://datarundown.com/wp-content/uploads/2024/01/Business-presentation.jpg)
List of the 9 Best Business Analysis Framework Techniques
Let’s have a closer look at the 9 techniques that are widely used and recognized
1. Business Process Modelling (BPM)
Business process modelling is a technique used to analyze existing or potential business processes in order to improve them.
![Top 9 Business Analysis Frameworks: Most Popular Techniques 2 An isometric diagram of a group of people standing on a staircase, illustrating a business process modelling framework](https://datarundown.com/wp-content/uploads/2023/11/Planning-and-timelines-icon.jpg)
A process model serves as a reference so that everyone involved in the organization’s operations can have a common understanding of how things work and what needs to be done in order to reach desired objectives.
How is Business Process Modeling (BPM) Used?
- Visualizing Processes : BPM allows organizations to visually represent their processes, providing a clear understanding of how activities, resources, and information flow within the organization.
- Identifying Bottlenecks : By modeling business processes , organizations can identify potential bottlenecks, inefficiencies, and areas for improvement, enabling them to streamline operations.
- Improving Communication : BPM facilitates improved communication and collaboration among stakeholders by providing a common visual language for discussing and understanding processes.
- Supporting Decision-Making : BPM provides valuable insights for informed decision-making, enabling organizations to make data-driven changes to their processes.
2. Use Case Modelling
Use case modelling is a technique that lets you look at your system from the perspective of your customer.
Use Case Modeling is a technique used to capture and define the functional requirements of a system by illustrating how users interact with it. It involves identifying and documenting various scenarios that depict the interactions between users and the system to accomplish specific goals.
![Top 9 Business Analysis Frameworks: Most Popular Techniques 3 An isometric diagram of a group of people standing on a staircase, illustrating a use case modelling](https://datarundown.com/wp-content/uploads/2023/11/Team-Planning-Isometric-Icon.jpg)
It’s a great way to make sure you’re answering all their questions and making sure they get what they need out of your service or product. You can also use it to keep track of all the possible ways that different people and groups might interact with your company.
How is Use Case Modeling Used?
- Requirement Uncovering : Use Case Modeling serves as a tool for uncovering and defining functional requirements through the identification of various use cases, scenarios, and user interactions.
- System Design : Use Case Modeling aids in the design and development of systems by providing a clear understanding of user-system interactions and requirements, guiding the creation of user interfaces and system functionalities.
- Communication Tool : Use Case models act as a communication tool between business stakeholders and technical teams , ensuring a common understanding of user-system interactions and requirements.
- Testing and Validation : Use Case Modeling supports testing and validation activities by providing test scenarios and expected system behaviors, aiding in the verification of system functionality.
3. SWOT Analysis
SWOT Analysis is a strategic planning technique used to identify and evaluate an organization’s internal strengths and weaknesses, as well as external opportunities and threats. It involves the systematic analysis of these factors to inform strategic decision-making.
![Top 9 Business Analysis Frameworks: Most Popular Techniques 4 A group of people using a SWOT analysis to discuss business analysis.](https://datarundown.com/wp-content/uploads/2024/01/Meeting-business-analysts-e1705946312647.jpg)
Strengths, weaknesses, and opportunities are internal factors that affect the company, while threats are external factors that affect the company. The SWOT analysis is particularly useful in identifying areas that need improvement and areas where the business is doing well.
How is SWOT Analysis Used?
- Strategic Planning : It provides a structured framework for organizations to assess their current position and make informed decisions about future strategies.
- Business Assessment : SWOT Analysis helps in evaluating the internal capabilities and limitations of the organization, as well as the external factors that may impact its performance.
- Risk Management : It aids in identifying potential risks and challenges in the external environment, allowing organizations to develop proactive risk management strategies.
- Market Positioning : SWOT Analysis assists in understanding the organization’s competitive position and identifying opportunities to differentiate itself in the market.
4. MOST (Mission, Objectives, Strategies, and Tactics) Analysis
- MOST Analysis is a tool that can be used to determine an organization’s interest in entering a new market space or in determining if an industry is a good fit for its organization’s culture.
MOST Analysis focuses on four key elements:
- Mission : The overarching purpose or reason for an organization’s existence, defining its fundamental role and aspirations.
- Objectives : Clear and measurable goals that an organization aims to achieve within a specific timeframe, guiding its strategic direction.
- Strategy : The approach or plan designed to achieve the defined objectives and fulfill the organization’s mission.
- Tactics : The specific actions and initiatives undertaken to implement the chosen strategy and achieve the established objectives.
![Top 9 Business Analysis Frameworks: Most Popular Techniques 5 Icon for a business analysis framework discussions](https://datarundown.com/wp-content/uploads/2024/01/Meeting-analysts-e1705946292354.jpg)
How is MOST Analysis Used?
MOST Analysis finds diverse applications in strategic planning and organizational development:
- Strategic Alignment : It helps in aligning organizational activities with its mission, ensuring that all efforts contribute to its overarching purpose.
- Goal Setting and Evaluation : MOST Analysis aids in setting clear and measurable objectives and evaluating progress towards their achievement.
- Resource Allocation : It supports effective resource allocation by ensuring that strategies and tactics are aligned with the organization’s mission and objectives.
- Decision-Making : MOST Analysis provides a structured framework for making strategic decisions, ensuring that they are in line with the organization’s mission and long-term objectives.
5. PESTLE Analysis
PESTLE Analysis is a framework used to analyze the impact of external factors on an organization. The acronym PESTLE stands for:
![Top 9 Business Analysis Frameworks: Most Popular Techniques 6 An isometric image of a man looking at a PESTLE analysis on a screen](https://datarundown.com/wp-content/uploads/2024/01/Isometric-icon-business-analysts.jpg)
- Political : Factors related to government policies, stability, and potential impacts on business operations.
- Economic : Economic conditions, trends, and indicators that can affect the organization’s financial performance.
- Social : Social and cultural factors, including demographics, lifestyle changes, and consumer behavior.
- Technological : Technological advancements, innovation, and the impact of technology on the industry and organization.
- Legal : Legal and regulatory factors, including laws, regulations, and compliance requirements.
- Environmental : Environmental concerns, sustainability efforts, and their impact on business operations and reputation.
The components of the PESTLE analysis are then analyzed and given a score from 0-10, where 10 is considered favorable for the business and 0 is unfavorable. This technique is best used to identify existing threats and opportunities as well as to help understand what would need to change for the business to be more successful.
How is PESTLE Analysis Used?
- Risk Assessment : It helps in identifying potential risks and opportunities arising from the external environment, guiding risk management strategies.
- Strategic Planning : PESTLE Analysis informs strategic planning by providing insights into external factors that may impact the organization’s performance and objectives.
- Market Research : It aids in understanding the broader market environment, including regulatory changes, technological trends, and societal shifts.
- Business Expansion : PESTLE Analysis supports organizations in evaluating the feasibility and risks associated with entering new markets or expanding operations.
6. Brainstorming
Brainstorming is a technique to be used when generating requirements and writing what-ifs.
![Top 9 Business Analysis Frameworks: Most Popular Techniques 7 Business Analysts brainstorm ideas](https://datarundown.com/wp-content/uploads/2023/10/decision-making-icon.jpg)
Brainstorming is a group problem-solving method that involves the spontaneous contribution of creative ideas and solutions. It encourages participants to think freely and express their thoughts without judgment, with the aim of generating innovative solutions and insights.
How is Brainstorming Used?
- Idea Generation : It serves as a platform for generating a multitude of ideas to address business challenges, explore new opportunities, or innovate products and services.
- Problem-Solving : Brainstorming helps in identifying and exploring potential solutions to complex problems by leveraging the collective creativity and expertise of participants.
- Requirement Elicitation : It aids in eliciting diverse perspectives and requirements from stakeholders, ensuring a comprehensive understanding of user needs and business objectives.
- Strategy Development : Brainstorming supports the development of strategic plans and initiatives by exploring various approaches and considering different viewpoints.
7. MoSCoW (Must or Should, Could or Would)
MoSCoW is a prioritization method that categorizes requirements into four distinct groups:
![Top 9 Business Analysis Frameworks: Most Popular Techniques 8 A group of business analysts engaged in a framework for business analysis](https://datarundown.com/wp-content/uploads/2024/01/Analyst-meeting-.jpg)
- Must Have : Essential requirements that are critical for the success of the project or business objective.
- Should Have : Important requirements that are desirable but not critical for the initial release or project delivery.
- Could Have : Requirements that are considered as potential enhancements, but their inclusion is not critical at the current time.
- Won’t Have (or Would Have) : Requirements that are explicitly identified as not being included in the current scope.
This technique is used to determine which requirements must be met, which requirements should be met, which requirements could be met, and which requirements would be met if there is time and resources available.
How is MoSCoW Used?
- Requirement Prioritization : It helps in prioritizing requirements based on their criticality and impact on project success, guiding resource allocation and planning.
- Scope Management : MoSCoW assists in defining and managing the project scope by categorizing requirements into distinct priority levels.
- Stakeholder Communication : It facilitates clear communication with stakeholders regarding the prioritization of requirements and the project’s focus areas.
- Decision-Making : MoSCoW supports decision-making by providing a structured approach to evaluating and categorizing requirements based on their importance.
8. The 5 Whys
The 5 Whys is a technique used to identify the root cause of a problem. The technique involves asking “why” five times to get to the root cause of the problem. This technique is particularly useful in identifying areas where the business can improve.
![Top 9 Business Analysis Frameworks: Most Popular Techniques 9 A group of people discussing and planning business analysis frameworks](https://datarundown.com/wp-content/uploads/2023/11/Communication-icon.jpg)
How is The 5 Whys Used?
The 5 Whys problem-solving technique finds diverse applications in business analysis and process improvement:
- Root Cause Analysis : It helps in identifying the root cause of a problem by systematically asking “why” to trace the issue back to its origin.
- Issue Resolution : The 5 Whys aids in addressing recurring problems by digging deeper into the underlying causes rather than addressing surface-level symptoms.
- Process Improvement : It supports continuous improvement efforts by uncovering weaknesses in processes or systems and addressing them at their core.
- Decision-Making : It assists in making informed decisions by providing a structured approach to understanding the factors contributing to a problem.
9. Gap Analysis
Gap Analysis is a method of assessing the performance of a business unit to determine whether business requirements or objectives are being met. It involves comparing the current state of the organization with its ideal state, highlighting shortcomings and opportunities for improvement.
![Top 9 Business Analysis Frameworks: Most Popular Techniques 10 Isometric icon for a GAP analysis in Business Analyst Frameworks](https://datarundown.com/wp-content/uploads/2024/01/Company-business-analytics.jpg)
How is Gap Analysis Used?
Gap Analysis finds diverse applications in strategic planning and organizational development:
- Performance Assessment : It helps in evaluating the current performance of the organization against its goals and objectives.
- Identifying Opportunities : Gap Analysis aids in identifying areas where the organization can make improvements to bridge the gap between its current and desired state.
- Resource Allocation : It supports effective resource allocation by identifying areas that require additional resources to meet the desired objectives.
- Process Improvement : It facilitates the identification of inefficiencies and areas for process improvement within the organization.
Tips : If you are curios to learn more about business analysis and related topics, then check out all of our posts related to business analysis
Framework for Business Analysis: The Essentials
Leveraging the right frameworks and techniques is crucial for gaining valuable insights, making informed decisions, and driving organizational success.
The top 9 business analysis frameworks offer a diverse set of tools and methodologies to address complex business challenges, uncover opportunities, and drive strategic initiatives.
By understanding and applying these frameworks, organizations can enhance their analytical capabilities, improve decision-making, and achieve sustainable growth in dynamic business environments.
Key Takeaways: Business Analyst Frameworks
In short, the 9 frameworks we have looked at:
- Business process modelling (BPM) is a technique used to analyze existing or potential business processes in order to improve them.
- Use case modeling is a technique for visually representing how users interact with a system, helping to define system requirements and validate the functionality of a solution.
- SWOT analysis is a strategic planning tool that evaluates an organization’s strengths, weaknesses, opportunities, and threats, providing valuable insights for strategic decision-making.
- PESTLE analysis evaluates the political, economic, social, technological, legal, and environmental factors that impact an organization, providing a comprehensive understanding of the external business environment.
- Brainstorming is a collaborative technique that encourages the generation of diverse ideas and solutions to address business challenges and explore new opportunities.
- MoSCoW is a prioritization method that categorizes requirements into four distinct groups: Must have, Should have, Could have, Would have
- The 5 Why technique involves asking “why” five times to get to the root cause of the problem.
- Gap Analysis is a method of assessing the performance of a business unit to determine whether business requirements or objectives are being met.
FAQ: Business Analysis Techniques
What are the key steps involved in the business analysis process.
The business analysis process typically involves the following key steps: Defining the problem: This involves identifying the problem or opportunity that the business is facing. Gathering information: This involves collecting data and information about the problem or opportunity. Analyzing the information: This involves analyzing the data and information to identify patterns, trends, and insights. Identifying solutions: Based on the analysis, potential solutions are identified. Evaluating solutions: The potential solutions are evaluated to determine the best course of action. Implementing the solution: The chosen solution is implemented, and the results are monitored to ensure success.
What tools are essential for effective business analysis?
There are several tools that are essential for effective business analysis. Some examples: Microsoft Excel: Excel is a powerful tool for data analysis and modeling. Flowcharting software: Flowcharting software is used to create process flow diagrams, which are often used in business analysis. Requirements management software: This software is used to manage and track requirements throughout the project lifecycle. Mind mapping software: This software is used to organize and visualize complex information.
Can you provide an example of a business analysis framework?
One example of a business analysis framework is Michael Porter’s Five Forces Model. This model is used for analyzing industries and considers the competitiveness of an industry in the market based on five different factors or forces, including rivalry among existing competitors, the bargaining power of buyers and suppliers, the threat of new entrants, and the threat of substitute products or services.
What are some common techniques used in business analysis?
Some common techniques used in business analysis include: SWOT analysis: This technique is used to identify the strengths, weaknesses, opportunities, and threats of a business or project. Use case modeling: This technique is used to identify the actors, goals, and scenarios of a system or process. Data modeling: This technique is used to create a visual representation of the data and information used in a system or process. Process modeling: This technique is used to create a visual representation of the steps involved in a process.
How can I create my own business analysis framework?
To create your own business analysis framework, you should: Identify the problem or opportunity: Define the problem or opportunity that the framework will address. Gather information: Collect data and information about the problem or opportunity. Analyze the information: Analyze the data and information to identify patterns, trends, and insights. Identify potential solutions: Based on the analysis, identify potential solutions. Evaluate the solutions: Evaluate the potential solutions to determine the best course of action. Implement the solution: Implement the chosen solution and monitor the results.
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Meet Eric, the data "guru" behind Datarundown. When he's not crunching numbers, you can find him running marathons, playing video games, and trying to win the Fantasy Premier League using his predictions model (not going so well).
Eric passionate about helping businesses make sense of their data and turning it into actionable insights. Follow along on Datarundown for all the latest insights and analysis from the data world.
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Framework for Problem-Solving: 5 Best Examples for Product Teams
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What is a framework for problem-solving? And how can product managers use them to tackle the challenges they face?
If you are after the answers to these questions, we’ve got you covered! We also look at examples of different frameworks and the main steps in the problem-solving process.
Are you ready to dive in?
- A framework for problem-solving allows product teams to find the causes of the problems and generate solutions in an organized way.
- Root Cause Analysis enables problem solvers to get to the bottom of the problem and find the main reason why the problem occurs.
- Many companies like Google use the CIRCLES framework for problem-solving. The process consists of 7 steps and helps the product manager to take stock of the situation, identify user needs, prioritize them, and produce and assess solutions.
- The CIA created the Pheonix Checklist with a list of questions to help the problem solver dissect the issue and guide them through the process.
- Lightning Decision Jam (LDJ) allows remote teams to come up with solutions quickly and within the constraints of the online working environment.
- The acronym DMAIC stands for Define, Measure, Analyze, Implement and Control. They are stages in Six Sigma, a popular quality improvement methodology.
- All the problem-solving frameworks share certain processes: identifying and understanding the problem or the needs of the customer, brainstorming solutions, choosing and implementing the solutions, and monitoring their effectiveness.
- Userpilot can help you to collect user feedback and track usage data to understand the problems your users are facing or set the baseline. Once you implement the solutions, you can use them to collect more data to evaluate their impact .
What is a problem-solving framework?
The problem-solving framework is a set of tools and techniques to identify the causes of the problem and find adequate solutions.
Problem-solving frameworks rely on both data analysis and heuristics.
What are heuristics?
We use them every day. In short, it’s mental shortcuts that allow us to apply what we already know in a new situation. They are particularly useful when detailed research is not practical. An educated guess or generalization may be good enough but the solutions won’t be perfect or cover all the eventualities.
Problem-solving framework example
Let’s look at some of the best-known problem-solving frameworks.
Root Cause Analysis
Managers usually use Root Cause Analysis to deal with problems that have already occurred. It consists of six main steps.
![analytics methodology and problem solving framework Root cause analysis framework. Source: EDUPristine](https://images.storychief.com/account_6827/problem-solving-framework-Root-Cause-Analysis-Process_5b11f5b19681f5bffd31298b207eb91e_800.jpg)
The process starts by defining the problem, followed by data collection .
Based on the data, the team generates a list of possible causes. Next, they can use techniques like 5 Why’s or the Fishbone diagram for more in-depth analysis to identify the actual problem – the root cause.
Once they know it, they can move on to recommend and implement relevant solutions.
CIRCLES method for problem-solving
The CIRCLES method is a problem-solving framework that was created by Lewis C. Lin, who is known for his best-selling book Decode and Conquer.
The framework is particularly suitable for product management. That’s because it allows managers to solve any kind of problem, no matter where it comes from. As a result, it’s a go-to framework for companies like Google.
CIRCLES stands for the 7 steps it takes to solve a problem:
- C omprehend the situation
- I dentify the Customer
- R eport the customer’s needs
- C ut, through prioritization
- L ist solutions
- E valuate tradeoffs
- S ummarize recommendation
![analytics methodology and problem solving framework CIRCLES framework for problem-solving](https://images.storychief.com/account_6827/Circles-Framework-problem-solving_ab948ea372aac0b0218eb47b72161c34_800.png)
Comprehend the situation
At this step, the team tries to understand the context of the problem.
The easiest way to do that is by asking Wh- questions, like ‘What is it?’, ‘Who is it for?’, ‘Why do they need it?’, ‘When is it available?’, ‘Where is it available?’ and ‘How does it work?’
Identify the customer
The who question is particularly important because you need to know who you are building the product for.
At this step, you focus on the user in more detail. You can do it by creating user personas and empathy maps which allow you to understand your users’ experiences, behaviors, and goals.
![analytics methodology and problem solving framework User Persona Example](https://images.storychief.com/account_6827/problem-solving-framework-product-persona-example_d8da02f1a05cbf0b591b4766962c8d83_800.jpg)
Report customer’s needs
Next, the focus shifts to specific user needs and requirements.
Teams often use user stories for this purpose. These look like this:
As a <type of user> , I want <output> so that <outcome>.
For example:
As a product manager, I want to be able to customize the dashboard so that I can easily track the performance of my KPIs.
Reporting user needs in this way forces you to look at the problem from a user perspective and express ideas in plain accessible language.
Cut through prioritization
Now that you have a list of use cases or user stories, it’s time to prioritize them.
This stage is very important as we never have enough resources to build all the possible features. As the Pareto rule states, users only use about 20 percent of the available functionality.
However, many teams fall into the build trap and create bloated products that have tons of features but are not particularly great at solving any of the customer problems.
There are a bunch of techniques that product managers or owners can use to prioritize the backlog items, like MoSCoW or Kano analysis.
![analytics methodology and problem solving framework Kano Analysis helps to organize solutions according to their priority](https://images.storychief.com/account_6827/framework-problem-solving-kano_6869ee8b6edef84aecaf237c74c605fd_800.jpg)
List solutions
Now, that you have the most urgent user needs, it’s time to generate possible solutions.
There are different ways of solving each problem, so resist the temptation to jump at the first idea your team comes up with. Instead, try to brainstorm at least 3 solutions to a particular problem.
It’s extremely important to be non-judgemental at this stage and refrain from dismissing any ideas. Just list them all and don’t worry about evaluating their suitability. There will be time for it in the next stages.
Evaluate tradeoffs
At this step, you assess the pros and cons of each potential solution.
To aid the process, you may want to create a checklist with criteria like cost or ease of implementation, or riskiness.
Summarize your recommendation
The last step is to summarize the solutions and provide a recommendation, based on what you’ve found out by this stage.
Ideally, the customer should be involved at every stage of the process but if for some reason this hasn’t been the case this is the time to ask them for their opinion about the solutions you’ve chosen.
The Phoenix Checklist
The Phoenix Checklist is another solid framework.
It was developed by the CIA and it consists of sets of questions grouped into different categories.
Going through the checklist allows the agent… I mean the product manager to break down the problem and come up with the best solution.
Here are some of the questions:
- Why is it necessary to solve this particular problem?
- What benefits will you receive by solving it?
- What is the information you have?
- Is the information you have sufficient?
- What are the unknowns?
- Can you describe the problem in a chart?
- Where are the limits for the problem?
- Can you distinguish the different parts of the problem?
- What are the relationships between the different parts of the problem?
- Have you seen this problem before?
- Can you use solutions to similar problems to solve this problem?
Lightning Decision Jam – problem-solving framework for remote teams
Lightning Decision Jam (LDJ) is a very effective problem-solving framework for dispersed teams.
It consists of 9 steps that allow the team members to list and reframe the issues they face, choose the most pressing ones to address, generate, prioritize and select solutions, and turn them into actionable tasks.
Each of the steps is time-boxed so that the team moves through the process quickly and efficiently.
DMAIC – The Six Sigma’s Problem-Solving Method
Six Sigma was initially developed for the needs of the automotive industry in Japan to help it deal with high defect rates. It is now one of the best quality-improvement frameworks and it is used in different sectors.
There are 5 main stages of Six Sigma projects.
During the Definition stage, the team identifies the problem they would like to solve, prepares the project charter, brings the right people on board, and ensures there are adequate resources available.
One of the key tasks during this stage is capturing the Voice of the Customer . After all, the definition of good quality is very much dependent on the needs of the customers and what they are ready to pay for, so their input is essential.
During the Measure phase, the team describes the process and measures its current performance to establish the baseline.
At the Analyze stage, they use the data to identify the root causes and waste, or activities that don’t bring any value.
The Improve stage focuses on generating, evaluating, and optimizing solutions. This is also when the team tests the ideas. If they are successful, they plan how to implement them.
Finally, the project champion must ensure that people stick to the new ways of doing things. That’s what the Control phase is about. The team also uses this stage to assess the outcomes and benefits of the project.
![analytics methodology and problem solving framework DMAIC framework for problem-solving](https://images.storychief.com/account_6827/problem-solving-framework-DMAIC_4a4a41855409f33d7db6d3630374a51a_800.png)
Problem-solving process recurring steps
Now, that we have looked at a few of the most popular frameworks for solving problems, why don’t we look at the steps that they have in common?
Identify and understand the problem with user research
First, it’s necessary to identify and understand the problem.
To do that, your team should conduct solid user research and capture the Voice of the Customer (VoC) .
How to do that?
You can track user in-app behavior , run in-app surveys , conduct interviews and analyze user social media feedback and online reviews.
To get a complete picture, try to collect both quantitative and qualitative data.
![analytics methodology and problem solving framework Collect Feedback with microsurveys](https://images.storychief.com/account_6827/problem-solving-framework-survey_a06127af774ffff1a38e2c130a60b035_800.png)
Brainstorm solutions
There’s no problem-solving framework out there that wouldn’t include brainstorming of some sort. And there’s a good reason for that: it’s one of the most effective ways to generate a lot of different solutions in a short time.
To make the brainstorming sessions as effective as possible, make sure all your team members have a chance to contribute. Your software engineer may not be the most vocal team member but it doesn’t mean she has nothing to offer, and not recognizing it can be costly.
The Delphi method or silent brainstorming are good techniques that prevent groupthink and the less outspoken team members from being talked over.
No matter how ridiculous or outrageous some ideas may seem, don’t discard any unless they’re completely irrelevant. It’s not the time to evaluate ideas, just come up with as many of them as possible.
Decide on a solution and implement
Some of the solutions will be better than others, so it’s always necessary to assess them and choose the one solution that solves the problem better than others.
Even the best ideas are not worth much if you don’t manage to implement them, so pay attention to this stage.
Often big changes are necessary to solve difficult problems so you need to prepare your team or your customers. Take your time, and focus on explaining the rationale for change and the benefits that it brings.
Make sure to provide the right training to your staff and support your users with onboarding and product education to reduce friction once the new solution goes live.
Collect feedback and evaluate
Once you implement the solution, keep collecting feedback to assess its effectiveness.
Is it solving the problem? Does it help you achieve the objectives? If not, how can you modify it to improve its success? If yes, is there anything else that would provide even more value?
You can do this by actively asking your users for feedback, for example via a survey.
![analytics methodology and problem solving framework A survey to collect feedback to the new solution](https://images.storychief.com/account_6827/problem-solving-framework-active-feedback_ae8339e847f6f57dde0272c5b0cc15d9_800.png)
In addition to asking for feedback actively, give your users a chance to submit passive feedback whenever they feel like it.
![analytics methodology and problem solving framework Opportunities to give passive feedback](https://images.storychief.com/account_6827/problem-solving-framework-passive-feedback_5070a472fc4cc1b646e52bab4f169f3a_800.png)
In case of organizational changes, it’s important to monitor whether the new processes or tools are used in the first place, because as creatures of habit we tend to relapse to our old ways quite easily, often without realizing it.
There are a few useful frameworks for problem-solving. They can guide a product manager through the process of defining the problem, identifying causes, generating and implementing solutions, and assessing their impact.
If you’d like to learn how Userpilot can help you capture the voice of the customer, analyze the data to identify root causes, help design user-centered solutions and collect both active and passive feedback to test their effectiveness, book a demo !
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At the core of our methodology is the Foresight Framework, a proven and proprietary methodology to find future innovation opportunities and to create forward-looking organizations. The methodology began at Stanford University and has been tested with various teams and companies since 2004. Comprised of 15 core methods for problem scoping and problem solving, the Foresight Framework is designed to address complex problems and future planning as systematically, creatively, and efficiently as possible.
By using this framework, participants prepare successfully for the future by answering three fundamental questions:
- How do I begin looking for future opportunities?
- How can I create a path to these opportunities that anticipates the inevitable changes along the way?
- What can I start doing today that will help me get there first?
![analytics methodology and problem solving framework Foresight Playbook cover](https://foresight.stanford.edu/sites/g/files/sbiybj17636/files/styles/card_1900x950/public/media/image/playbook_collage.png?h=90be0a93&itok=RXR0bVy3)
Playbook for Strategic Foresight and Innovation
Looking for practical foresight methods? Download a free PDF of our playbook (or buy a print version ) as your in-house guide.
Phase I: Perspective
Methods in the Perspective phase give you a broad frame of reference, holding up a mirror to the past so you may better anticipate the future.
METHOD | GOAL | OUTCOME |
---|---|---|
| Retain complexity of topic, while beginning to converge on priority areas | A visual map of the top 8 dimensions in a problem |
| Connect multiple related events and highlights precedents | A set of related timelines showing past developments and their timing |
| Uncover indirect influences and events within an era | A large-scale pattern map of past major decisions affecting current planning |
Phase II: Opportunity
By understanding future customer changes, methods in the Opportunity phase help you develop an ability to see growth opportunities that exist today and extend into the future.
METHOD | GOAL | OUTCOME |
---|---|---|
| Identify relevant population group and shared values | A diagram of the target user population(s) with size and attributes |
| Describe future user needs without extrapolating biases from today’s users | A comparison of two customer/user profiles, including anticipated needs |
| Convey nonverbal and contextual details about a future use case | A short performance of user needs, either captured as a live skit or via video |
Phase III: Solution
Methods in the Solution phase seek to define the questions that exist along different paths to innovation, which are specific to your industry, customers, organization, and team skills.
METHOD | GOAL | OUTCOME |
---|---|---|
| Determine future focus of opportunity through iterative filters | A visual map of the competitive landscape and emerging market areas |
| Produce system models that show interactions and related components | A 3D prototype of a potential innovation solution |
| Prioritize top decisions based on direct path to desired future | A visual high-level roadmap for future action |
Phase IV: Team
Methods in the Team phase focus on finding the talent and leadership your team needs to take an idea forward as a new opportunity or innovation.
METHOD | GOAL | OUTCOME |
---|---|---|
| Let you quickly filter promising innovation partners and teammates | Team rehearsal for communicating and receiving new ideas for target reactions |
| Identify your team’s aptitude for radical innovation | A team score on five dimensions of innovation culture with a related set of supporting activities |
| Map your personal or team's innovation network | A stakeholder map of the innovation players to bring an idea to life |
Phase V: Vision
Methods in the Vision phase sharpen your team’s vision so that it may take on a life of its own and guide everyone’s actions forward.
METHOD | GOAL | OUTCOME |
---|---|---|
| Provide a simple formula to tell a future vision | A short inspirational description about an idea’s future value |
| Evaluate future vision in terms of its breakthrough potential | A quick scoring about an idea’s visionary potential |
| Chart the most efficient success path for an innovation idea through an organization | A visual planning map revealing what to do (or not do) for optimal internal alignment and success |
General Non-Commercial Use
- Under the creative commons license ( CC BY-NC-SA 3.0 ), the Playbook for Strategic Foresight and Innovation is available for free download to individuals, companies, and organizations for non-commercial purposes only.
- Parties interested in applying the foresight methods from the Foresight Framework for internal development, university teaching or educational reasons, or other purposes may do so with attribution and with no intention to profit.
- Other third party trademarks referenced on the website and in the Playbook for Strategic Foresight and Innovation are the property of their respective owners.
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- Commercial usage of the Foresight Framework and all related methods are restricted and full copyright are retained by the book's authors. You may not use trademarks, methods, or other intellectual property assets in connection with web sites, products, packaging, manuals, promotional/advertising materials, or for any other purpose except pursuant to an express written licensing agreement from the publisher. Contact the Innovation Leadership Group LLC's licensing team [email protected] for more information.
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A guide to problem-solving techniques, steps, and skills
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You might associate problem-solving with the math exercises that a seven-year-old would do at school. But problem-solving isn’t just about math — it’s a crucial skill that helps everyone make better decisions in everyday life or work.
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Problem-solving involves finding effective solutions to address complex challenges, in any context they may arise.
Unfortunately, structured and systematic problem-solving methods aren’t commonly taught. Instead, when solving a problem, PMs tend to rely heavily on intuition. While for simple issues this might work well, solving a complex problem with a straightforward solution is often ineffective and can even create more problems.
In this article, you’ll learn a framework for approaching problem-solving, alongside how you can improve your problem-solving skills.
The 7 steps to problem-solving
When it comes to problem-solving there are seven key steps that you should follow: define the problem, disaggregate, prioritize problem branches, create an analysis plan, conduct analysis, synthesis, and communication.
1. Define the problem
Problem-solving begins with a clear understanding of the issue at hand. Without a well-defined problem statement, confusion and misunderstandings can hinder progress. It’s crucial to ensure that the problem statement is outcome-focused, specific, measurable whenever possible, and time-bound.
Additionally, aligning the problem definition with relevant stakeholders and decision-makers is essential to ensure efforts are directed towards addressing the actual problem rather than side issues.
2. Disaggregate
Complex issues often require deeper analysis. Instead of tackling the entire problem at once, the next step is to break it down into smaller, more manageable components.
Various types of logic trees (also known as issue trees or decision trees) can be used to break down the problem. At each stage where new branches are created, it’s important for them to be “MECE” – mutually exclusive and collectively exhaustive. This process of breaking down continues until manageable components are identified, allowing for individual examination.
The decomposition of the problem demands looking at the problem from various perspectives. That is why collaboration within a team often yields more valuable results, as diverse viewpoints lead to a richer pool of ideas and solutions.
3. Prioritize problem branches
The next step involves prioritization. Not all branches of the problem tree have the same impact, so it’s important to understand the significance of each and focus attention on the most impactful areas. Prioritizing helps streamline efforts and minimize the time required to solve the problem.
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4. Create an analysis plan
For prioritized components, you may need to conduct in-depth analysis. Before proceeding, a work plan is created for data gathering and analysis. If work is conducted within a team, having a plan provides guidance on what needs to be achieved, who is responsible for which tasks, and the timelines involved.
5. Conduct analysis
Data gathering and analysis are central to the problem-solving process. It’s a good practice to set time limits for this phase to prevent excessive time spent on perfecting details. You can employ heuristics and rule-of-thumb reasoning to improve efficiency and direct efforts towards the most impactful work.
6. Synthesis
After each individual branch component has been researched, the problem isn’t solved yet. The next step is synthesizing the data logically to address the initial question. The synthesis process and the logical relationship between the individual branch results depend on the logic tree used.
7. Communication
The last step is communicating the story and the solution of the problem to the stakeholders and decision-makers. Clear effective communication is necessary to build trust in the solution and facilitates understanding among all parties involved. It ensures that stakeholders grasp the intricacies of the problem and the proposed solution, leading to informed decision-making.
Exploring problem-solving in various contexts
While problem-solving has traditionally been associated with fields like engineering and science, today it has become a fundamental skill for individuals across all professions. In fact, problem-solving consistently ranks as one of the top skills required by employers.
Problem-solving techniques can be applied in diverse contexts:
- Individuals — What career path should I choose? Where should I live? These are examples of simple and common personal challenges that require effective problem-solving skills
- Organizations — Businesses also face many decisions that are not trivial to answer. Should we expand into new markets this year? How can we enhance the quality of our product development? Will our office accommodate the upcoming year’s growth in terms of capacity?
- Societal issues — The biggest world challenges are also complex problems that can be addressed with the same technique. How can we minimize the impact of climate change? How do we fight cancer?
Despite the variation in domains and contexts, the fundamental approach to solving these questions remains the same. It starts with gaining a clear understanding of the problem, followed by decomposition, conducting analysis of the decomposed branches, and synthesizing it into a result that answers the initial problem.
Real-world examples of problem-solving
Let’s now explore some examples where we can apply the problem solving framework.
Problem: In the production of electronic devices, you observe an increasing number of defects. How can you reduce the error rate and improve the quality?
![analytics methodology and problem solving framework Electric Devices](https://blog.logrocket.com/wp-content/uploads/2024/04/Electric-devices.jpg)
Before delving into analysis, you can deprioritize branches that you already have information for or ones you deem less important. For instance, while transportation delays may occur, the resulting material degradation is likely negligible. For other branches, additional research and data gathering may be necessary.
Once results are obtained, synthesis is crucial to address the core question: How can you decrease the defect rate?
While all factors listed may play a role, their significance varies. Your task is to prioritize effectively. Through data analysis, you may discover that altering the equipment would bring the most substantial positive outcome. However, executing a solution isn’t always straightforward. In prioritizing, you should consider both the potential impact and the level of effort needed for implementation.
By evaluating impact and effort, you can systematically prioritize areas for improvement, focusing on those with high impact and requiring minimal effort to address. This approach ensures efficient allocation of resources towards improvements that offer the greatest return on investment.
Problem : What should be my next job role?
![analytics methodology and problem solving framework Next Job](https://blog.logrocket.com/wp-content/uploads/2024/04/Next-job.jpg)
When breaking down this problem, you need to consider various factors that are important for your future happiness in the role. This includes aspects like the company culture, our interest in the work itself, and the lifestyle that you can afford with the role.
However, not all factors carry the same weight for us. To make sense of the results, we can assign a weight factor to each branch. For instance, passion for the job role may have a weight factor of 1, while interest in the industry may have a weight factor of 0.5, because that is less important for you.
By applying these weights to a specific role and summing the values, you can have an estimate of how suitable that role is for you. Moreover, you can compare two roles and make an informed decision based on these weighted indicators.
Key problem-solving skills
This framework provides the foundation and guidance needed to effectively solve problems. However, successfully applying this framework requires the following:
- Creativity — During the decomposition phase, it’s essential to approach the problem from various perspectives and think outside the box to generate innovative ideas for breaking down the problem tree
- Decision-making — Throughout the process, decisions must be made, even when full confidence is lacking. Employing rules of thumb to simplify analysis or selecting one tree cut over another requires decisiveness and comfort with choices made
- Analytical skills — Analytical and research skills are necessary for the phase following decomposition, involving data gathering and analysis on selected tree branches
- Teamwork — Collaboration and teamwork are crucial when working within a team setting. Solving problems effectively often requires collective effort and shared responsibility
- Communication — Clear and structured communication is essential to convey the problem solution to stakeholders and decision-makers and build trust
How to enhance your problem-solving skills
Problem-solving requires practice and a certain mindset. The more you practice, the easier it becomes. Here are some strategies to enhance your skills:
- Practice structured thinking in your daily life — Break down problems or questions into manageable parts. You don’t need to go through the entire problem-solving process and conduct detailed analysis. When conveying a message, simplify the conversation by breaking the message into smaller, more understandable segments
- Regularly challenging yourself with games and puzzles — Solving puzzles, riddles, or strategy games can boost your problem-solving skills and cognitive agility.
- Engage with individuals from diverse backgrounds and viewpoints — Conversing with people who offer different perspectives provides fresh insights and alternative solutions to problems. This boosts creativity and helps in approaching challenges from new angles
Final thoughts
Problem-solving extends far beyond mathematics or scientific fields; it’s a critical skill for making informed decisions in every area of life and work. The seven-step framework presented here provides a systematic approach to problem-solving, relevant across various domains.
Now, consider this: What’s one question currently on your mind? Grab a piece of paper and try to apply the problem-solving framework. You might uncover fresh insights you hadn’t considered before.
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Mckinsey approach to problem solving, a guide to the 7-step mckinsey problem solving process.
McKinsey and Company is recognized for its rigorous approach to problem solving. They train their consultants on their seven-step process that anyone can learn.
This resource guides you through that process, largely informed by the McKinsey Staff Paper 66. It also includes a PowerPoint Toolkit with slide templates of each step of the process that you can download and customize for your own use.
In this guide you'll learn:
Overview of the mckinsey approach to problem solving, problem solving process, problem definition.
- Problem Statement
Stakeholder Analysis Worksheet
Structure the problem, hypothesis trees, issue trees, analyses and workplan, synthesize findings, craft recommendations, communicate, distinctiveness practices, harness the power of collaboration, sources and additional reading, request the mckinsey approach to problem solving.
Problem solving — finding the optimal solution to a given business opportunity or challenge — is the very heart of how consultants create client impact, and considered the most important skill for success at McKinsey.
The characteristic “McKinsey method” of problem solving is a structured, inductive approach that can be used to solve any problem. Using this standardized process saves us from reinventing the problem-solving wheel, and allows for greater focus on distinctiveness in the solution. Every new McKinsey associate must learn this method on his or her first day with the firm.
There are four fundamental disciplines of the McKinsey method:
1. Problem definition
A thorough understanding and crisp definition of the problem.
2. The problem-solving process
Structuring the problem, prioritizing the issues, planning analyses, conducting analyses, synthesizing findings, and developing recommendations.
3. Distinctiveness practices
Constructing alternative perspectives; identifying relationships; distilling the essence of an issue, analysis, or recommendation; and staying ahead of others in the problem-solving process.
4. Collaboratio n
Actively seeking out client, customer, and supplier perspectives, as well as internal and external expert insight and knowledge.
Once the problem has been defined, the problem-solving process proceeds with a series of steps:
- Structure the problem
- Prioritize the issues
- Plan analyses
- Conduct analyses
- Synthesize findings
- Develop recommendations
Not all problems require strict adherence to the process. Some steps may be truncated, such as when specific knowledge or analogies from other industries make it possible to construct hypotheses and associated workplans earlier than their formal place in the process. Nonetheless, it remains important to be capable of executing every step in the basic process.
When confronted with a new and complex problem, this process establishes a path to defining and disaggregating the problem in a way that will allow the team to move to a solution. The process also ensures nothing is missed and concentrates efforts on the highest-impact areas. Adhering to the process gives the client clear steps to follow, building confidence, credibility, and long-term capability.
The most important step in your entire project is to first carefully define the problem. The problem definition will serve the guide all of the team’s work, so it is critical to ensure that all key stakeholders agree that it is the right problem to be solving.
The problem definition will serve the guide all of the team’s work, so it is critical to ensure that all key stakeholders agree that it is the right problem to be solving.
There are often dozens of issues that a team could focus on, and it is often not obvious how to define the problem.
In any real-life situation, there are many possible problem statements. Your choice of problem statement will serve to constrain the range of possible solutions.
Constraints can be a good thing (e.g., limit solutions to actions within the available budget.) And constraints can be a bad thing (e.g., eliminating the possibility of creative ideas.) So choose wisely.
The problem statement may ignore many issues to focus on the priority that should be addressed. The problem statement should be phrased as a question, such that the answer will be the solution.
Example scenario – A family on Friday evening :
A mother, a father, and their two teenage children have all arrived home on a Friday at 6 p.m. The family has not prepared dinner for Friday evening. The daughter has lacrosse practice on Saturday and an essay to write for English class due on Monday. The son has theatre rehearsal on both Saturday and Sunday and will need one parent to drive him to the high school both days, though he can get a ride home with a friend.
The family dog, a poodle, must be taken to the groomer on Saturday morning. The mother will need to spend time this weekend working on assignments for her finance class she is taking as part of her Executive MBA. The father plans to go on a 100-mile bike ride, which he can do either Saturday or Sunday. The family has two cars, but one is at the body shop. They are trying to save money to pay for an addition to their house.
Potential problem definitions – A family on Friday evening :
The problem definition should not be vague, without clear measures of success. Rather, it should be a SMART definition:
- Action-oriented
Given one set of facts, it is possible to come up with many possible problem statements. The choice of problem statement constrains the range of possible solutions.
Before starting to solve the problem, the family first needs to agree on what problem they want to solve.
- What should the family do for dinner on Friday night?
- How can the family schedule their activities this weekend to accomplish everything planned given that they only have one vehicle available?
- How can the family increase income or reduce expenses to allow them to save $75K over the next 12 months to pay for the planned addition to their house?
Problem Statement Worksheet
This is a helpful tool to use to clearly define the problem. There are often dozens of issues that a team could focus on, and it is often not obvious how to define the problem. In any real-life situation, there are many possible problem statements. Your choice of problem statement will serve to constrain the range of possible solutions.
- Use a question . The problem statement should be phrased as a question, such that the answer will be the solution. Make the question SMART: specific, measurable, action-oriented, relevant, and time-bound. Example: “How can XYZ Bank close the $100 million profitability gap in two years?”
- Context . What are the internal and external situations and complications facing the client, such as industry trends, relative position within the industry, capability gaps, financial flexibility, and so on?
- Success criteria . Understand how the client and the team define success and failure. In addition to any quantitative measures identified in the basic question, identify other important quantitative or qualitative measures of success, including timing of impact, visibility of improvement, client capability building required, necessary mindset shifts, and so on.
- Scope and constraints . Scope most commonly covers the markets or segments of interest, whereas constraints govern restrictions on the nature of solutions within those markets or segments.
- Stakeholders . Explore who really makes the decisions — who decides, who can help, and who can block.
- Key sources of insight . What best-practice expertise, knowledge, and engagement approaches already exist? What knowledge from the client, suppliers, and customers needs to be accessed? Be as specific as possible: who, what, when, how, and why.
In completing the Problem Statement Worksheet, you are prompted to define the key stakeholders.
As you become involved in the problem-solving process, you should expand the question of key stakeholders to include what the team wants from them and what they want from the team, their values and motivations (helpful and unhelpful), and the communications mechanisms that will be most effective for each of them.
Using the Stakeholder Analysis Worksheet allows you to comprehensively identify:
- Stakeholders
- What you need from them
- Where they are
- What they need from you
The two most helpful techniques for rigorously structuring any problem are hypothesis trees and issue trees. Each of these techniques disaggregates the primary question into a cascade of issues or hypotheses that, when addressed, will together answer the primary question.
A hypothesis tree might break down the same question into two or more hypotheses.
The aim at this stage is to structure the problem into discrete, mutually exclusive pieces that are small enough to yield to analysis and that, taken together, are collectively exhaustive.
Articulating the problem as hypotheses, rather than issues, is the preferred approach because it leads to a more focused analysis of the problem. Questions to ask include:
- Is it testable – can you prove or disprove it?
- It is open to debate? If it cannot be wrong, it is simply a statement of fact and unlikely to produce keen insight.
- If you reversed your hypothesis – literally, hypothesized that the exact opposite were true – would you care about the difference it would make to your overall logic?
- If you shared your hypothesis with the CEO, would it sound naive or obvious?
- Does it point directly to an action or actions that the client might take?
Quickly developing a powerful hypothesis tree enables us to develop solutions more rapidly that will have real impact. This can sometimes seem premature to clients, who might find the “solution” reached too quickly and want to see the analysis behind it.
Take care to explain the approach (most important, that a hypothesis is not an answer) and its benefits (that a good hypothesis is the basis of a proven means of successful problem solving and avoids “boiling the ocean”).
Example: Alpha Manufacturing, Inc.
Problem Statement: How can Alpha increase EBITDA by $13M (to $50M) by 2025?
The hypotheses might be:
- Alpha can add $125M revenues by expanding to new customers, adding $8M of EBITDA
- Alpha can reduce costs to improve EBITDA by $5M
These hypotheses will be further disaggregated into subsidiary hypotheses at the next level of the tree.
Often, the team has insufficient knowledge to build a complete hypothesis tree at the start of an engagement. In these cases, it is best to begin by structuring the problem using an issue tree.
An issue tree is best set out as a series of open questions in sentence form. For example, “How can the client minimize its tax burden?” is more useful than “Tax.” Open questions – those that begin with what, how, or why– produce deeper insights than closed ones. In some cases, an issue tree can be sharpened by toggling between issue and hypothesis – working forward from an issue to identify the hypothesis, and back from the hypothesis to sharpen the relevant open question.
Once the problem has been structured, the next step is to prioritize the issues or hypotheses on which the team will focus its work. When prioritizing, it is common to use a two-by-two matrix – e.g., a matrix featuring “impact” and “ease of impact” as the two axes.
Applying some of these prioritization criteria will knock out portions of the issue tree altogether. Consider testing the issues against them all, albeit quickly, to help drive the prioritization process.
Once the criteria are defined, prioritizing should be straightforward: Simply map the issues to the framework and focus on those that score highest against the criteria.
As the team conducts analysis and learns more about the problem and the potential solution, make sure to revisit the prioritization matrix so as to remain focused on the highest-priority issues.
The issues might be:
- How can Alpha increase revenue?
- How can Alpha reduce cost?
Each of these issues is then further broken down into deeper insights to solutions.
If the prioritization has been carried out effectively, the team will have clarified the key issues or hypotheses that must be subjected to analysis. The aim of these analyses is to prove the hypotheses true or false, or to develop useful perspectives on each key issue. Now the task is to design an effective and efficient workplan for conducting the analyses.
Transforming the prioritized problem structure into a workplan involves two main tasks:
- Define the blocks of work that need to be undertaken. Articulate as clearly as possible the desired end products and the analysis necessary to produce them, and estimate the resources and time required.
- Sequence the work blocks in a way that matches the available resources to the need to deliver against key engagement milestones (e.g., important meetings, progress reviews), as well as to the overall pacing of the engagement (i.e., weekly or twice-weekly meetings, and so on).
A good workplan will detail the following for each issue or hypothesis: analyses, end products, sources, and timing and responsibility. Developing the workplan takes time; doing it well requires working through the definition of each element of the workplan in a rigorous and methodical fashion.
It’s useful to match the workplan to three horizons:
- What is expected at the end of the engagement
- What is expected at key progress reviews
- What is due at daily and/or weekly team meetings
The detail in the workplan will typically be greater for the near term (the next week) than for the long term (the study horizon), especially early in a new engagement when considerable ambiguity about the end state remains.
Here are three different templates for a workplan:
This is the most difficult element of the problem-solving process. After a period of being immersed in the details, it is crucial to step back and distinguish the important from the merely interesting. Distinctive problem solvers seek the essence of the story that will underpin a crisp recommendation for action.
Although synthesis appears, formally speaking, as the penultimate step in the process, it should happen throughout. Ideally, after you have made almost any analytical progress, you should attempt to articulate the “Day 1” or “Week 1” answer. Continue to synthesize as you go along. This will remind the team of the question you are trying to answer, assist prioritization, highlight the logical links of the emerging solution, and ensure that you have a story ready to articulate at all times during the study.
McKinsey’s primary tool for synthesizing is the pyramid principle. Essentially, this principle asserts that every synthesis should explain a single concept, per the “governing thought.” The supporting ideas in the synthesis form a thought hierarchy proceeding in a logical structure from the most detailed facts to the governing thought, ruthlessly excluding the interesting but irrelevant.
While this hierarchy can be laid out as a tree (like with issue and hypothesis trees), the best problem solvers capture it by creating dot-dash storylines — the Pyramid Structure for Grouping Arguments.
Pyramid Structure for Grouping Arguments
- Focus on action. Articulate the thoughts at each level of the pyramid as declarative sentences, not as topics. For example, “expansion” is a topic; “We need to expand into the European market” is a declarative sentence.
- Use storylines. PowerPoint is poor at highlighting logical connections, therefore is not a good tool for synthesis. A storyline will clarify elements that may be ambiguous in the PowerPoint presentation.
- Keep the emerging storyline visible. Many teams find that posting the storyline or story- board on the team-room wall helps keep the thinking focused. It also helps in bringing the client along.
- Use the situation-complication-resolution structure. The situation is the reason there is action to be taken. The com- plication is why the situation needs thinking through – typically an industry or client challenge. The resolution is the answer.
- Down the pyramid: does each governing thought pose a single question that is answered completely by the group of boxes below it?
- Across: is each level within the pyramid MECE?
- Up: does each group of boxes, taken together, provide one answer – one “so what?” – that is essentially the governing thought above it?
- Test the solution. What would it mean if your hypotheses all came true?
It is at this point that we address the client’s questions: “What do I do, and how do I do it?” This means not offering actionable recommendations, along with a plan and client commitment for implementation.
The essence of this step is to translate the overall solution into the actions required to deliver sustained impact. A pragmatic action plan should include:
- Relevant initiatives, along with a clear sequence, timing, and mapping of activities required
- Clear owners for each initiative
- Key success factors and the challenges involved in delivering on the initiatives
Crucial questions to ask as you build recommendations for organizational change are:
- Does each person who needs to change (from the CEO to the front line) understand what he or she needs to change and why, and is he or she committed to it?
- Are key leaders and role models throughout the organization personally committed to behaving differently?
- Has the client set in place the necessary formal mechanisms to reinforce the desired change?
- Does the client have the skills and confidence to behave in the desired new way?
Once the recommendations have been crafted in the problem-solving process, it’s vital to effectively communicate those findings and recommendations.
An executive summary is a great slide to use for this. See more on executive summary slides, including 30 templates, at our Ultimate Guide to Executive Summary Slides .
Great problem solvers identify unique disruptions and discontinuities, novel insights, and step-out opportunities that lead to truly distinctive impact. This is done by applying a number of practices throughout the problem-solving process to help develop these insights.
Expand: Construct multiple perspectives
Identifying alternative ways of looking at the problem expands the range of possibilities, opens you up to innovative ideas, and allows you to formulate more powerful hypotheses. Questions that help here include:
- What changes if I think from the perspective of a customer, or a supplier, or a frontline employee, or a competitor?
- How have other industries viewed and addressed this same problem?
- What would it mean if the client sought to run the company like a low-cost airline or a cosmetics manufacturer?
Link: Identify relationships
Strong problem solvers discern connections and recognize patterns in two different ways:
- They seek out the ways in which different problem elements – issues, hypotheses, analyses, work elements, findings, answers, and recommendations – relate to one another.
- They use these relationships throughout the basic problem-solving process to identify efficient problem-solving approaches, novel solutions, and more powerful syntheses.
Distill: Find the essence
Cutting through complexity to identify the heart of the problem and its solution is a critical skill.
- Identify the critical problem elements. Are there some issues, approaches, or options that can be eliminated completely because they won’t make a significant difference to the solution?
- Consider how complex the different elements are and how long it will take to complete them. Wherever possible, quickly advance simpler parts of the problem that can inform more complex or time-consuming elements.
Lead: Stay ahead/step back
Without getting ahead of the client, you cannot be distinctive. Paradoxically, to get ahead – and stay ahead – it is often necessary to step back from the problem to validate or revalidate the approach and the solution.
- Spend time thinking one or more steps ahead of the client and team.
- Constantly check and challenge the rigor of the underlying data and analysis.
- Stress-test the whole emerging recommendation
- Challenge the solution against a set of hurdles. Does it satisfy the criteria for success as set out on the Problem Statement Worksheet?
No matter how skilled, knowledgeable, or experienced you are, you will never create the most distinctive solution on your own. The best problem solvers know how to leverage the power of their team, clients, the Firm, and outside parties. Seeking the right expertise at the right time, and leveraging it in the right way, are ultimately how we bring distinctiveness to our work, how we maximize efficiency, and how we learn.
When solving a problem, it is important to ask, “Have I accessed all the sources of insight that are available?” Here are the sources you should consider:
- Your core team
- The client’s suppliers and customers
- Internal experts and knowledge
- External sources of knowledge
- Communications specialists
The key here is to think open, not closed. Opening up to varied sources of data and perspectives furthers our mission to develop truly innovative and distinctive solutions for our clients.
- McKinsey Staff Paper 66 — not published by McKinsey but possibly found through an internet search
- The McKinsey Way , 1999, by Ethan M. Rasiel
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Problem Solving with Advanced Analytics
Learn a scientific approach to solving problems with data, how to select the correct analytical methodology for an analysis, and how to use linear regression to solve business problems.
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The analytical problem solving framework.
Learn a structured framework for solving problems with advanced analytics.
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Patrick Nussbaumer is Technical Activation Director at Alteryx, Inc. Prior to Alteryx, Patrick has spent the past 20 years in a variety of roles focused on data analysis, telecommunications, and financial services industries.
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A Step-by-Step Guide to the Data Analysis Process
Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it’s important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.
In this post, we’ll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we’ll also use examples and highlight a few tools to make the journey easier. When you’re done, you’ll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.
Here are the steps we’ll take you through:
- Defining the question
- Collecting the data
- Cleaning the data
- Analyzing the data
- Sharing your results
- Embracing failure
On popular request, we’ve also developed a video based on this article. Scroll further along this article to watch that.
Ready? Let’s get started with step one.
1. Step one: Defining the question
The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’.
Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as: “Why are we losing customers?” It’s possible, though, that this doesn’t get to the core of the problem. A data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.
Let’s say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, “Why are we losing customers?” but, “Which factors are negatively impacting the customer experience?” or better yet: “How can we boost customer retention while minimizing costs?”
Now you’ve defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you’ve noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don’t come back? Which sources of data will help you answer this question?
Tools to help define your objective
Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you’ll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and DashThis . However, you’ll also find open-source software like Grafana , Freeboard , and Dashbuilder . These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.
2. Step two: Collecting the data
Once you’ve established your objective, you’ll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let’s explore each one.
What is first-party data?
First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company’s customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.
What is second-party data?
To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.
What is third-party data?
Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data .
Tools to help you collect data
Once you’ve devised a data strategy (i.e. you’ve identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you’ll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP , SAS , and the data integration platform, Xplenty . If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm .
Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics .
3. Step three: Cleaning the data
Once you’ve collected your data, the next step is to get it ready for analysis. This means cleaning, or ‘scrubbing’ it, and is crucial in making sure that you’re working with high-quality data . Key data cleaning tasks include:
- Removing major errors, duplicates, and outliers —all of which are inevitable problems when aggregating data from numerous sources.
- Removing unwanted data points —extracting irrelevant observations that have no bearing on your intended analysis.
- Bringing structure to your data —general ‘housekeeping’, i.e. fixing typos or layout issues, which will help you map and manipulate your data more easily.
- Filling in major gaps —as you’re tidying up, you might notice that important data are missing. Once you’ve identified gaps, you can go about filling them.
A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one…so don’t rush it! You’ll find a step-by-step guide to data cleaning here . You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.
Carrying out an exploratory analysis
Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let’s use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning’s clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.
Tools to help you clean your data
Cleaning datasets manually—especially large ones—can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine , are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder , which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?
4. Step four: Analyzing the data
Finally, you’ve cleaned your data. Now comes the fun bit—analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.
Descriptive analysis
Descriptive analysis identifies what has already happened . It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let’s refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they’ll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.
Learn more: What is descriptive analytics?
Diagnostic analysis
Diagnostic analytics focuses on understanding why something has happened . It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. Remember TopNotch Learning’s business problem? ‘Which factors are negatively impacting the customer experience?’ A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let’s imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they’re losing customers because they lack expertise in this sector. And that’s a useful insight!
Predictive analysis
Predictive analysis allows you to identify future trends based on historical data . In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn’t stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they’ll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.
Prescriptive analysis
Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It’s also the most complex. This is because it incorporates aspects of all the other analyses we’ve described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.
Learn more: What are the different types of data analysis?
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5. Step five: Sharing your results
You’ve finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization’s stakeholders!) This is more complex than simply sharing the raw results of your work—it involves interpreting the outcomes, and presenting them in a manner that’s digestible for all types of audiences. Since you’ll often present information to decision-makers, it’s very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.
How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That’s why it’s very important to provide all the evidence that you’ve gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it’s important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!
Tools for interpreting and sharing your findings
There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts , Tableau , Datawrapper , and Infogram . If you’re familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly , Seaborn , and Matplotlib . Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!
You can learn more about storytelling with data in this free, hands-on tutorial . We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it’s really like to work as a data analyst!
6. Step six: Embrace your failures
The last ‘step’ in the data analytics process is to embrace your failures. The path we’ve described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you’d never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.
While these pitfalls can feel like failures, don’t be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What’s important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn’t be as interesting. Use the steps we’ve outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.
In this post, we’ve covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst’s work:
- Define the question —What business problem are you trying to solve? Frame it as a question to help you focus on finding a clear answer.
- Collect data —Create a strategy for collecting data. Which data sources are most likely to help you solve your business problem?
- Clean the data —Explore, scrub, tidy, de-dupe, and structure your data as needed. Do whatever you have to! But don’t rush…take your time!
- Analyze the data —Carry out various analyses to obtain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
- Share your results —How best can you share your insights and recommendations? A combination of visualization tools and communication is key.
- Embrace your mistakes —Mistakes happen. Learn from them. This is what transforms a good data analyst into a great one.
What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we’ve described, you can create a tailored technique that works for you.
To learn more, check out our free, 5-day data analytics short course . You might also be interested in the following:
- These are the top 9 data analytics tools
- 10 great places to find free datasets for your next project
- How to build a data analytics portfolio
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The Art of Effective Problem Solving: A Step-by-Step Guide
Whether we realise it or not, problem solving skills are an important part of our daily lives. From resolving a minor annoyance at home to tackling complex business challenges at work, our ability to solve problems has a significant impact on our success and happiness. However, not everyone is naturally gifted at problem-solving, and even those who are can always improve their skills. In this blog post, we will go over the art of effective problem-solving step by step.
Problem Solving Methodologies
Methodology of 8D (Eight Discipline) Problem Solving:
A3 Problem Solving Method:
The A3 problem solving technique is a visual, team-based problem-solving approach that is frequently used in Lean Six Sigma projects. The A3 report is a one-page document that clearly and concisely outlines the problem, root cause analysis, and proposed solution.
Subsequently, in the Lean Six Sigma framework, the 8D and A3 problem solving methodologies are two popular approaches to problem solving. Both methodologies provide a structured, team-based problem-solving approach that guides individuals through a comprehensive and systematic process of identifying, analysing, and resolving problems in an effective and efficient manner.
Step 1 – Define the Problem
By repeatedly asking “ why ,” you’ll eventually get to the bottom of the problem. This is an important step in the problem-solving process because it ensures that you’re dealing with the root cause rather than just the symptoms.
Step 2 – Gather Information and Brainstorm Ideas
Gathering information and brainstorming ideas is the next step in effective problem solving. This entails researching the problem and relevant information, collaborating with others, and coming up with a variety of potential solutions. This increases your chances of finding the best solution to the problem.
Next, work with others to gather a variety of perspectives. Brainstorming with others can be an excellent way to come up with new and creative ideas. Encourage everyone to share their thoughts and ideas when working in a group, and make an effort to actively listen to what others have to say. Be open to new and unconventional ideas and resist the urge to dismiss them too quickly.
Once you’ve compiled a list of potential solutions, it’s time to assess them and select the best one. This is the next step in the problem-solving process, which we’ll go over in greater detail in the following section.
Step 3 – Evaluate Options and Choose the Best Solution
Once you’ve compiled a list of potential solutions, it’s time to assess them and select the best one. This is the third step in effective problem solving, and it entails weighing the advantages and disadvantages of each solution, considering their feasibility and practicability, and selecting the solution that is most likely to solve the problem effectively.
You’ll be able to tell which solutions are likely to succeed and which aren’t by assessing their feasibility and practicability.
Step 4 – Implement and Monitor the Solution
When you’ve decided on the best solution, it’s time to put it into action. The fourth and final step in effective problem solving is to put the solution into action, monitor its progress, and make any necessary adjustments.
Finally, make any necessary modifications to the solution. This could entail changing the solution, altering the plan of action, or delegating different tasks. Be willing to make changes if they will improve the solution or help it solve the problem more effectively.
You can increase your chances of success in problem solving by following these steps and considering factors such as the pros and cons of each solution, their feasibility and practicability, and making any necessary adjustments. Furthermore, keep in mind that problem solving is an iterative process, and there may be times when you need to go back to the beginning and restart. Maintain your adaptability and try new solutions until you find the one that works best for you.
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40 problem-solving techniques and processes
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All teams and organizations encounter challenges. Approaching those challenges without a structured problem solving process can end up making things worse.
Proven problem solving techniques such as those outlined below can guide your group through a process of identifying problems and challenges , ideating on possible solutions , and then evaluating and implementing the most suitable .
In this post, you'll find problem-solving tools you can use to develop effective solutions. You'll also find some tips for facilitating the problem solving process and solving complex problems.
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What is problem solving?
Problem solving is a process of finding and implementing a solution to a challenge or obstacle. In most contexts, this means going through a problem solving process that begins with identifying the issue, exploring its root causes, ideating and refining possible solutions before implementing and measuring the impact of that solution.
For simple or small problems, it can be tempting to skip straight to implementing what you believe is the right solution. The danger with this approach is that without exploring the true causes of the issue, it might just occur again or your chosen solution may cause other issues.
Particularly in the world of work, good problem solving means using data to back up each step of the process, bringing in new perspectives and effectively measuring the impact of your solution.
Effective problem solving can help ensure that your team or organization is well positioned to overcome challenges, be resilient to change and create innovation. In my experience, problem solving is a combination of skillset, mindset and process, and it’s especially vital for leaders to cultivate this skill.
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What is the seven step problem solving process?
A problem solving process is a step-by-step framework from going from discovering a problem all the way through to implementing a solution.
With practice, this framework can become intuitive, and innovative companies tend to have a consistent and ongoing ability to discover and tackle challenges when they come up.
You might see everything from a four step problem solving process through to seven steps. While all these processes cover roughly the same ground, I’ve found a seven step problem solving process is helpful for making all key steps legible.
We’ll outline that process here and then follow with techniques you can use to explore and work on that step of the problem solving process with a group.
The seven-step problem solving process is:
1. Problem identification
The first stage of any problem solving process is to identify the problem(s) you need to solve. This often looks like using group discussions and activities to help a group surface and effectively articulate the challenges they’re facing and wish to resolve.
Be sure to align with your team on the exact definition and nature of the problem you’re solving. An effective process is one where everyone is pulling in the same direction – ensure clarity and alignment now to help avoid misunderstandings later.
2. Problem analysis and refinement
The process of problem analysis means ensuring that the problem you are seeking to solve is the right problem . Choosing the right problem to solve means you are on the right path to creating the right solution.
At this stage, you may look deeper at the problem you identified to try and discover the root cause at the level of people or process. You may also spend some time sourcing data, consulting relevant parties and creating and refining a problem statement.
Problem refinement means adjusting scope or focus of the problem you will be aiming to solve based on what comes up during your analysis. As you analyze data sources, you might discover that the root cause means you need to adjust your problem statement. Alternatively, you might find that your original problem statement is too big to be meaningful approached within your current project.
Remember that the goal of any problem refinement is to help set the stage for effective solution development and deployment. Set the right focus and get buy-in from your team here and you’ll be well positioned to move forward with confidence.
3. Solution generation
Once your group has nailed down the particulars of the problem you wish to solve, you want to encourage a free flow of ideas connecting to solving that problem. This can take the form of problem solving games that encourage creative thinking or techniquess designed to produce working prototypes of possible solutions.
The key to ensuring the success of this stage of the problem solving process is to encourage quick, creative thinking and create an open space where all ideas are considered. The best solutions can often come from unlikely places and by using problem solving techniques that celebrate invention, you might come up with solution gold.
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4. Solution development
No solution is perfect right out of the gate. It’s important to discuss and develop the solutions your group has come up with over the course of following the previous problem solving steps in order to arrive at the best possible solution. Problem solving games used in this stage involve lots of critical thinking, measuring potential effort and impact, and looking at possible solutions analytically.
During this stage, you will often ask your team to iterate and improve upon your front-running solutions and develop them further. Remember that problem solving strategies always benefit from a multitude of voices and opinions, and not to let ego get involved when it comes to choosing which solutions to develop and take further.
Finding the best solution is the goal of all problem solving workshops and here is the place to ensure that your solution is well thought out, sufficiently robust and fit for purpose.
5. Decision making and planning
Nearly there! Once you’ve got a set of possible, you’ll need to make a decision on which to implement. This can be a consensus-based group decision or it might be for a leader or major stakeholder to decide. You’ll find a set of effective decision making methods below.
Once your group has reached consensus and selected a solution, there are some additional actions that also need to be decided upon. You’ll want to work on allocating ownership of the project, figure out who will do what, how the success of the solution will be measured and decide the next course of action.
Set clear accountabilities, actions, timeframes, and follow-ups for your chosen solution. Make these decisions and set clear next-steps in the problem solving workshop so that everyone is aligned and you can move forward effectively as a group.
Ensuring that you plan for the roll-out of a solution is one of the most important problem solving steps. Without adequate planning or oversight, it can prove impossible to measure success or iterate further if the problem was not solved.
6. Solution implementation
This is what we were waiting for! All problem solving processes have the end goal of implementing an effective and impactful solution that your group has confidence in.
Project management and communication skills are key here – your solution may need to adjust when out in the wild or you might discover new challenges along the way. For some solutions, you might also implement a test with a small group and monitor results before rolling it out to an entire company.
You should have a clear owner for your solution who will oversee the plans you made together and help ensure they’re put into place. This person will often coordinate the implementation team and set-up processes to measure the efficacy of your solution too.
7. Solution evaluation
So you and your team developed a great solution to a problem and have a gut feeling it’s been solved. Work done, right? Wrong. All problem solving strategies benefit from evaluation, consideration, and feedback.
You might find that the solution does not work for everyone, might create new problems, or is potentially so successful that you will want to roll it out to larger teams or as part of other initiatives.
None of that is possible without taking the time to evaluate the success of the solution you developed in your problem solving model and adjust if necessary.
Remember that the problem solving process is often iterative and it can be common to not solve complex issues on the first try. Even when this is the case, you and your team will have generated learning that will be important for future problem solving workshops or in other parts of the organization.
It’s also worth underlining how important record keeping is throughout the problem solving process. If a solution didn’t work, you need to have the data and records to see why that was the case. If you go back to the drawing board, notes from the previous workshop can help save time.
What does an effective problem solving process look like?
Every effective problem solving process begins with an agenda . In our experience, a well-structured problem solving workshop is one of the best methods for successfully guiding a group from exploring a problem to implementing a solution.
The format of a workshop ensures that you can get buy-in from your group, encourage free-thinking and solution exploration before making a decision on what to implement following the session.
This Design Sprint 2.0 template is an effective problem solving process from top agency AJ&Smart. It’s a great format for the entire problem solving process, with four-days of workshops designed to surface issues, explore solutions and even test a solution.
Check it for an example of how you might structure and run a problem solving process and feel free to copy and adjust it your needs!
For a shorter process you can run in a single afternoon, this remote problem solving agenda will guide you effectively in just a couple of hours.
Whatever the length of your workshop, by using SessionLab, it’s easy to go from an idea to a complete agenda . Start by dragging and dropping your core problem solving activities into place . Add timings, breaks and necessary materials before sharing your agenda with your colleagues.
The resulting agenda will be your guide to an effective and productive problem solving session that will also help you stay organized on the day!
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Complete problem-solving methods
In this section, we’ll look at in-depth problem-solving methods that provide a complete end-to-end process for developing effective solutions. These will help guide your team from the discovery and definition of a problem through to delivering the right solution.
If you’re looking for an all-encompassing method or problem-solving model, these processes are a great place to start. They’ll ask your team to challenge preconceived ideas and adopt a mindset for solving problems more effectively.
Six Thinking Hats
Individual approaches to solving a problem can be very different based on what team or role an individual holds. It can be easy for existing biases or perspectives to find their way into the mix, or for internal politics to direct a conversation.
Six Thinking Hats is a classic method for identifying the problems that need to be solved and enables your team to consider them from different angles, whether that is by focusing on facts and data, creative solutions, or by considering why a particular solution might not work.
Like all problem-solving frameworks, Six Thinking Hats is effective at helping teams remove roadblocks from a conversation or discussion and come to terms with all the aspects necessary to solve complex problems.
The Six Thinking Hats #creative thinking #meeting facilitation #problem solving #issue resolution #idea generation #conflict resolution The Six Thinking Hats are used by individuals and groups to separate out conflicting styles of thinking. They enable and encourage a group of people to think constructively together in exploring and implementing change, rather than using argument to fight over who is right and who is wrong.
Lightning Decision Jam
Featured courtesy of Jonathan Courtney of AJ&Smart Berlin, Lightning Decision Jam is one of those strategies that should be in every facilitation toolbox. Exploring problems and finding solutions is often creative in nature, though as with any creative process, there is the potential to lose focus and get lost.
Unstructured discussions might get you there in the end, but it’s much more effective to use a method that creates a clear process and team focus.
In Lightning Decision Jam, participants are invited to begin by writing challenges, concerns, or mistakes on post-its without discussing them before then being invited by the moderator to present them to the group.
From there, the team vote on which problems to solve and are guided through steps that will allow them to reframe those problems, create solutions and then decide what to execute on.
By deciding the problems that need to be solved as a team before moving on, this group process is great for ensuring the whole team is aligned and can take ownership over the next stages.
Lightning Decision Jam (LDJ) #action #decision making #problem solving #issue analysis #innovation #design #remote-friendly It doesn’t matter where you work and what your job role is, if you work with other people together as a team, you will always encounter the same challenges: Unclear goals and miscommunication that cause busy work and overtime Unstructured meetings that leave attendants tired, confused and without clear outcomes. Frustration builds up because internal challenges to productivity are not addressed Sudden changes in priorities lead to a loss of focus and momentum Muddled compromise takes the place of clear decision- making, leaving everybody to come up with their own interpretation. In short, a lack of structure leads to a waste of time and effort, projects that drag on for too long and frustrated, burnt out teams. AJ&Smart has worked with some of the most innovative, productive companies in the world. What sets their teams apart from others is not better tools, bigger talent or more beautiful offices. The secret sauce to becoming a more productive, more creative and happier team is simple: Replace all open discussion or brainstorming with a structured process that leads to more ideas, clearer decisions and better outcomes. When a good process provides guardrails and a clear path to follow, it becomes easier to come up with ideas, make decisions and solve problems. This is why AJ&Smart created Lightning Decision Jam (LDJ). It’s a simple and short, but powerful group exercise that can be run either in-person, in the same room, or remotely with distributed teams.
Problem Definition Process
While problems can be complex, the problem-solving methods you use to identify and solve those problems can often be simple in design.
By taking the time to truly identify and define a problem before asking the group to reframe the challenge as an opportunity, this method is a great way to enable change.
Begin by identifying a focus question and exploring the ways in which it manifests before splitting into five teams who will each consider the problem using a different method: escape, reversal, exaggeration, distortion or wishful. Teams develop a problem objective and create ideas in line with their method before then feeding them back to the group.
This method is great for enabling in-depth discussions while also creating space for finding creative solutions too!
Problem Definition #problem solving #idea generation #creativity #online #remote-friendly A problem solving technique to define a problem, challenge or opportunity and to generate ideas.
The 5 Whys
Sometimes, a group needs to go further with their strategies and analyze the root cause at the heart of organizational issues. An RCA or root cause analysis is the process of identifying what is at the heart of business problems or recurring challenges.
The 5 Whys is a simple and effective method of helping a group go find the root cause of any problem or challenge and conduct analysis that will deliver results.
By beginning with the creation of a problem statement and going through five stages to refine it, The 5 Whys provides everything you need to truly discover the cause of an issue.
The 5 Whys #hyperisland #innovation This simple and powerful method is useful for getting to the core of a problem or challenge. As the title suggests, the group defines a problems, then asks the question “why” five times, often using the resulting explanation as a starting point for creative problem solving.
World Cafe is a simple but powerful facilitation technique to help bigger groups to focus their energy and attention on solving complex problems.
World Cafe enables this approach by creating a relaxed atmosphere where participants are able to self-organize and explore topics relevant and important to them which are themed around a central problem-solving purpose. Create the right atmosphere by modeling your space after a cafe and after guiding the group through the method, let them take the lead!
Making problem-solving a part of your organization’s culture in the long term can be a difficult undertaking. More approachable formats like World Cafe can be especially effective in bringing people unfamiliar with workshops into the fold.
World Cafe #hyperisland #innovation #issue analysis World Café is a simple yet powerful method, originated by Juanita Brown, for enabling meaningful conversations driven completely by participants and the topics that are relevant and important to them. Facilitators create a cafe-style space and provide simple guidelines. Participants then self-organize and explore a set of relevant topics or questions for conversation.
Discovery & Action Dialogue (DAD)
One of the best approaches is to create a safe space for a group to share and discover practices and behaviors that can help them find their own solutions.
With DAD, you can help a group choose which problems they wish to solve and which approaches they will take to do so. It’s great at helping remove resistance to change and can help get buy-in at every level too!
This process of enabling frontline ownership is great in ensuring follow-through and is one of the methods you will want in your toolbox as a facilitator.
Discovery & Action Dialogue (DAD) #idea generation #liberating structures #action #issue analysis #remote-friendly DADs make it easy for a group or community to discover practices and behaviors that enable some individuals (without access to special resources and facing the same constraints) to find better solutions than their peers to common problems. These are called positive deviant (PD) behaviors and practices. DADs make it possible for people in the group, unit, or community to discover by themselves these PD practices. DADs also create favorable conditions for stimulating participants’ creativity in spaces where they can feel safe to invent new and more effective practices. Resistance to change evaporates as participants are unleashed to choose freely which practices they will adopt or try and which problems they will tackle. DADs make it possible to achieve frontline ownership of solutions.
Design Sprint 2.0
Want to see how a team can solve big problems and move forward with prototyping and testing solutions in a few days? The Design Sprint 2.0 template from Jake Knapp, author of Sprint, is a complete agenda for a with proven results.
Developing the right agenda can involve difficult but necessary planning. Ensuring all the correct steps are followed can also be stressful or time-consuming depending on your level of experience.
Use this complete 4-day workshop template if you are finding there is no obvious solution to your challenge and want to focus your team around a specific problem that might require a shortcut to launching a minimum viable product or waiting for the organization-wide implementation of a solution.
Open space technology
Open space technology- developed by Harrison Owen – creates a space where large groups are invited to take ownership of their problem solving and lead individual sessions. Open space technology is a great format when you have a great deal of expertise and insight in the room and want to allow for different takes and approaches on a particular theme or problem you need to be solved.
Start by bringing your participants together to align around a central theme and focus their efforts. Explain the ground rules to help guide the problem-solving process and then invite members to identify any issue connecting to the central theme that they are interested in and are prepared to take responsibility for.
Once participants have decided on their approach to the core theme, they write their issue on a piece of paper, announce it to the group, pick a session time and place, and post the paper on the wall. As the wall fills up with sessions, the group is then invited to join the sessions that interest them the most and which they can contribute to, then you’re ready to begin!
Everyone joins the problem-solving group they’ve signed up to, record the discussion and if appropriate, findings can then be shared with the rest of the group afterward.
Open Space Technology #action plan #idea generation #problem solving #issue analysis #large group #online #remote-friendly Open Space is a methodology for large groups to create their agenda discerning important topics for discussion, suitable for conferences, community gatherings and whole system facilitation
Techniques to identify and analyze problems
Using a problem-solving method to help a team identify and analyze a problem can be a quick and effective addition to any workshop or meeting.
While further actions are always necessary, you can generate momentum and alignment easily, and these activities are a great place to get started.
We’ve put together this list of techniques to help you and your team with problem identification, analysis, and discussion that sets the foundation for developing effective solutions.
Let’s take a look!
Fishbone Analysis
Organizational or team challenges are rarely simple, and it’s important to remember that one problem can be an indication of something that goes deeper and may require further consideration to be solved.
Fishbone Analysis helps groups to dig deeper and understand the origins of a problem. It’s a great example of a root cause analysis method that is simple for everyone on a team to get their head around.
Participants in this activity are asked to annotate a diagram of a fish, first adding the problem or issue to be worked on at the head of a fish before then brainstorming the root causes of the problem and adding them as bones on the fish.
Using abstractions such as a diagram of a fish can really help a team break out of their regular thinking and develop a creative approach.
Fishbone Analysis #problem solving ##root cause analysis #decision making #online facilitation A process to help identify and understand the origins of problems, issues or observations.
Problem Tree
Encouraging visual thinking can be an essential part of many strategies. By simply reframing and clarifying problems, a group can move towards developing a problem solving model that works for them.
In Problem Tree, groups are asked to first brainstorm a list of problems – these can be design problems, team problems or larger business problems – and then organize them into a hierarchy. The hierarchy could be from most important to least important or abstract to practical, though the key thing with problem solving games that involve this aspect is that your group has some way of managing and sorting all the issues that are raised.
Once you have a list of problems that need to be solved and have organized them accordingly, you’re then well-positioned for the next problem solving steps.
Problem tree #define intentions #create #design #issue analysis A problem tree is a tool to clarify the hierarchy of problems addressed by the team within a design project; it represents high level problems or related sublevel problems.
SWOT Analysis
Chances are you’ve heard of the SWOT Analysis before. This problem-solving method focuses on identifying strengths, weaknesses, opportunities, and threats is a tried and tested method for both individuals and teams.
Start by creating a desired end state or outcome and bare this in mind – any process solving model is made more effective by knowing what you are moving towards. Create a quadrant made up of the four categories of a SWOT analysis and ask participants to generate ideas based on each of those quadrants.
Once you have those ideas assembled in their quadrants, cluster them together based on their affinity with other ideas. These clusters are then used to facilitate group conversations and move things forward.
SWOT analysis #gamestorming #problem solving #action #meeting facilitation The SWOT Analysis is a long-standing technique of looking at what we have, with respect to the desired end state, as well as what we could improve on. It gives us an opportunity to gauge approaching opportunities and dangers, and assess the seriousness of the conditions that affect our future. When we understand those conditions, we can influence what comes next.
Agreement-Certainty Matrix
Not every problem-solving approach is right for every challenge, and deciding on the right method for the challenge at hand is a key part of being an effective team.
The Agreement Certainty matrix helps teams align on the nature of the challenges facing them. By sorting problems from simple to chaotic, your team can understand what methods are suitable for each problem and what they can do to ensure effective results.
If you are already using Liberating Structures techniques as part of your problem-solving strategy, the Agreement-Certainty Matrix can be an invaluable addition to your process. We’ve found it particularly if you are having issues with recurring problems in your organization and want to go deeper in understanding the root cause.
Agreement-Certainty Matrix #issue analysis #liberating structures #problem solving You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic . A problem is simple when it can be solved reliably with practices that are easy to duplicate. It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably. A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail. Chaotic is when the context is too turbulent to identify a path forward. A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.” The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.
Organizing and charting a team’s progress can be important in ensuring its success. SQUID (Sequential Question and Insight Diagram) is a great model that allows a team to effectively switch between giving questions and answers and develop the skills they need to stay on track throughout the process.
Begin with two different colored sticky notes – one for questions and one for answers – and with your central topic (the head of the squid) on the board. Ask the group to first come up with a series of questions connected to their best guess of how to approach the topic. Ask the group to come up with answers to those questions, fix them to the board and connect them with a line. After some discussion, go back to question mode by responding to the generated answers or other points on the board.
It’s rewarding to see a diagram grow throughout the exercise, and a completed SQUID can provide a visual resource for future effort and as an example for other teams.
SQUID #gamestorming #project planning #issue analysis #problem solving When exploring an information space, it’s important for a group to know where they are at any given time. By using SQUID, a group charts out the territory as they go and can navigate accordingly. SQUID stands for Sequential Question and Insight Diagram.
To continue with our nautical theme, Speed Boat is a short and sweet activity that can help a team quickly identify what employees, clients or service users might have a problem with and analyze what might be standing in the way of achieving a solution.
Methods that allow for a group to make observations, have insights and obtain those eureka moments quickly are invaluable when trying to solve complex problems.
In Speed Boat, the approach is to first consider what anchors and challenges might be holding an organization (or boat) back. Bonus points if you are able to identify any sharks in the water and develop ideas that can also deal with competitors!
Speed Boat #gamestorming #problem solving #action Speedboat is a short and sweet way to identify what your employees or clients don’t like about your product/service or what’s standing in the way of a desired goal.
The Journalistic Six
Some of the most effective ways of solving problems is by encouraging teams to be more inclusive and diverse in their thinking.
Based on the six key questions journalism students are taught to answer in articles and news stories, The Journalistic Six helps create teams to see the whole picture. By using who, what, when, where, why, and how to facilitate the conversation and encourage creative thinking, your team can make sure that the problem identification and problem analysis stages of the are covered exhaustively and thoughtfully. Reporter’s notebook and dictaphone optional.
The Journalistic Six – Who What When Where Why How #idea generation #issue analysis #problem solving #online #creative thinking #remote-friendly A questioning method for generating, explaining, investigating ideas.
Individual and group perspectives are incredibly important, but what happens if people are set in their minds and need a change of perspective in order to approach a problem more effectively?
Flip It is a method we love because it is both simple to understand and run, and allows groups to understand how their perspectives and biases are formed.
Participants in Flip It are first invited to consider concerns, issues, or problems from a perspective of fear and write them on a flip chart. Then, the group is asked to consider those same issues from a perspective of hope and flip their understanding.
No problem and solution is free from existing bias and by changing perspectives with Flip It, you can then develop a problem solving model quickly and effectively.
Flip It! #gamestorming #problem solving #action Often, a change in a problem or situation comes simply from a change in our perspectives. Flip It! is a quick game designed to show players that perspectives are made, not born.
LEGO Challenge
Now for an activity that is a little out of the (toy) box. LEGO Serious Play is a facilitation methodology that can be used to improve creative thinking and problem-solving skills.
The LEGO Challenge includes giving each member of the team an assignment that is hidden from the rest of the group while they create a structure without speaking.
What the LEGO challenge brings to the table is a fun working example of working with stakeholders who might not be on the same page to solve problems. Also, it’s LEGO! Who doesn’t love LEGO!
LEGO Challenge #hyperisland #team A team-building activity in which groups must work together to build a structure out of LEGO, but each individual has a secret “assignment” which makes the collaborative process more challenging. It emphasizes group communication, leadership dynamics, conflict, cooperation, patience and problem solving strategy.
What, So What, Now What?
If not carefully managed, the problem identification and problem analysis stages of the problem-solving process can actually create more problems and misunderstandings.
The What, So What, Now What? problem-solving activity is designed to help collect insights and move forward while also eliminating the possibility of disagreement when it comes to identifying, clarifying, and analyzing organizational or work problems.
Facilitation is all about bringing groups together so that might work on a shared goal and the best problem-solving strategies ensure that teams are aligned in purpose, if not initially in opinion or insight.
Throughout the three steps of this game, you give everyone on a team to reflect on a problem by asking what happened, why it is important, and what actions should then be taken.
This can be a great activity for bringing our individual perceptions about a problem or challenge and contextualizing it in a larger group setting. This is one of the most important problem-solving skills you can bring to your organization.
W³ – What, So What, Now What? #issue analysis #innovation #liberating structures You can help groups reflect on a shared experience in a way that builds understanding and spurs coordinated action while avoiding unproductive conflict. It is possible for every voice to be heard while simultaneously sifting for insights and shaping new direction. Progressing in stages makes this practical—from collecting facts about What Happened to making sense of these facts with So What and finally to what actions logically follow with Now What . The shared progression eliminates most of the misunderstandings that otherwise fuel disagreements about what to do. Voila!
Journalists
Problem analysis can be one of the most important and decisive stages of all problem-solving tools. Sometimes, a team can become bogged down in the details and are unable to move forward.
Journalists is an activity that can avoid a group from getting stuck in the problem identification or problem analysis stages of the process.
In Journalists, the group is invited to draft the front page of a fictional newspaper and figure out what stories deserve to be on the cover and what headlines those stories will have. By reframing how your problems and challenges are approached, you can help a team move productively through the process and be better prepared for the steps to follow.
Journalists #vision #big picture #issue analysis #remote-friendly This is an exercise to use when the group gets stuck in details and struggles to see the big picture. Also good for defining a vision.
Problem-solving techniques for brainstorming solutions
Now you have the context and background of the problem you are trying to solving, now comes the time to start ideating and thinking about how you’ll solve the issue.
Here, you’ll want to encourage creative, free thinking and speed. Get as many ideas out as possible and explore different perspectives so you have the raw material for the next step.
Looking at a problem from a new angle can be one of the most effective ways of creating an effective solution. TRIZ is a problem-solving tool that asks the group to consider what they must not do in order to solve a challenge.
By reversing the discussion, new topics and taboo subjects often emerge, allowing the group to think more deeply and create ideas that confront the status quo in a safe and meaningful way. If you’re working on a problem that you’ve tried to solve before, TRIZ is a great problem-solving method to help your team get unblocked.
Making Space with TRIZ #issue analysis #liberating structures #issue resolution You can clear space for innovation by helping a group let go of what it knows (but rarely admits) limits its success and by inviting creative destruction. TRIZ makes it possible to challenge sacred cows safely and encourages heretical thinking. The question “What must we stop doing to make progress on our deepest purpose?” induces seriously fun yet very courageous conversations. Since laughter often erupts, issues that are otherwise taboo get a chance to be aired and confronted. With creative destruction come opportunities for renewal as local action and innovation rush in to fill the vacuum. Whoosh!
Mindspin
Brainstorming is part of the bread and butter of the problem-solving process and all problem-solving strategies benefit from getting ideas out and challenging a team to generate solutions quickly.
With Mindspin, participants are encouraged not only to generate ideas but to do so under time constraints and by slamming down cards and passing them on. By doing multiple rounds, your team can begin with a free generation of possible solutions before moving on to developing those solutions and encouraging further ideation.
This is one of our favorite problem-solving activities and can be great for keeping the energy up throughout the workshop. Remember the importance of helping people become engaged in the process – energizing problem-solving techniques like Mindspin can help ensure your team stays engaged and happy, even when the problems they’re coming together to solve are complex.
MindSpin #teampedia #idea generation #problem solving #action A fast and loud method to enhance brainstorming within a team. Since this activity has more than round ideas that are repetitive can be ruled out leaving more creative and innovative answers to the challenge.
The Creativity Dice
One of the most useful problem solving skills you can teach your team is of approaching challenges with creativity, flexibility, and openness. Games like The Creativity Dice allow teams to overcome the potential hurdle of too much linear thinking and approach the process with a sense of fun and speed.
In The Creativity Dice, participants are organized around a topic and roll a dice to determine what they will work on for a period of 3 minutes at a time. They might roll a 3 and work on investigating factual information on the chosen topic. They might roll a 1 and work on identifying the specific goals, standards, or criteria for the session.
Encouraging rapid work and iteration while asking participants to be flexible are great skills to cultivate. Having a stage for idea incubation in this game is also important. Moments of pause can help ensure the ideas that are put forward are the most suitable.
The Creativity Dice #creativity #problem solving #thiagi #issue analysis Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.
Idea and Concept Development
Brainstorming without structure can quickly become chaotic or frustrating. In a problem-solving context, having an ideation framework to follow can help ensure your team is both creative and disciplined.
In this method, you’ll find an idea generation process that encourages your group to brainstorm effectively before developing their ideas and begin clustering them together. By using concepts such as Yes and…, more is more and postponing judgement, you can create the ideal conditions for brainstorming with ease.
Idea & Concept Development #hyperisland #innovation #idea generation Ideation and Concept Development is a process for groups to work creatively and collaboratively to generate creative ideas. It’s a general approach that can be adapted and customized to suit many different scenarios. It includes basic principles for idea generation and several steps for groups to work with. It also includes steps for idea selection and development.
Problem-solving techniques for developing and refining solutions
The success of any problem-solving process can be measured by the solutions it produces. After you’ve defined the issue, explored existing ideas, and ideated, it’s time to develop and refine your ideas in order to bring them closer to a solution that actually solves the problem.
Use these problem-solving techniques when you want to help your team think through their ideas and refine them as part of your problem solving process.
Improved Solutions
After a team has successfully identified a problem and come up with a few solutions, it can be tempting to call the work of the problem-solving process complete. That said, the first solution is not necessarily the best, and by including a further review and reflection activity into your problem-solving model, you can ensure your group reaches the best possible result.
One of a number of problem-solving games from Thiagi Group, Improved Solutions helps you go the extra mile and develop suggested solutions with close consideration and peer review. By supporting the discussion of several problems at once and by shifting team roles throughout, this problem-solving technique is a dynamic way of finding the best solution.
Improved Solutions #creativity #thiagi #problem solving #action #team You can improve any solution by objectively reviewing its strengths and weaknesses and making suitable adjustments. In this creativity framegame, you improve the solutions to several problems. To maintain objective detachment, you deal with a different problem during each of six rounds and assume different roles (problem owner, consultant, basher, booster, enhancer, and evaluator) during each round. At the conclusion of the activity, each player ends up with two solutions to her problem.
Four Step Sketch
Creative thinking and visual ideation does not need to be confined to the opening stages of your problem-solving strategies. Exercises that include sketching and prototyping on paper can be effective at the solution finding and development stage of the process, and can be great for keeping a team engaged.
By going from simple notes to a crazy 8s round that involves rapidly sketching 8 variations on their ideas before then producing a final solution sketch, the group is able to iterate quickly and visually. Problem-solving techniques like Four-Step Sketch are great if you have a group of different thinkers and want to change things up from a more textual or discussion-based approach.
Four-Step Sketch #design sprint #innovation #idea generation #remote-friendly The four-step sketch is an exercise that helps people to create well-formed concepts through a structured process that includes: Review key information Start design work on paper, Consider multiple variations , Create a detailed solution . This exercise is preceded by a set of other activities allowing the group to clarify the challenge they want to solve. See how the Four Step Sketch exercise fits into a Design Sprint
Ensuring that everyone in a group is able to contribute to a discussion is vital during any problem solving process. Not only does this ensure all bases are covered, but its then easier to get buy-in and accountability when people have been able to contribute to the process.
1-2-4-All is a tried and tested facilitation technique where participants are asked to first brainstorm on a topic on their own. Next, they discuss and share ideas in a pair before moving into a small group. Those groups are then asked to present the best idea from their discussion to the rest of the team.
This method can be used in many different contexts effectively, though I find it particularly shines in the idea development stage of the process. Giving each participant time to concretize their ideas and develop them in progressively larger groups can create a great space for both innovation and psychological safety.
1-2-4-All #idea generation #liberating structures #issue analysis With this facilitation technique you can immediately include everyone regardless of how large the group is. You can generate better ideas and more of them faster than ever before. You can tap the know-how and imagination that is distributed widely in places not known in advance. Open, generative conversation unfolds. Ideas and solutions are sifted in rapid fashion. Most importantly, participants own the ideas, so follow-up and implementation is simplified. No buy-in strategies needed! Simple and elegant!
15% Solutions
Some problems are simpler than others and with the right problem-solving activities, you can empower people to take immediate actions that can help create organizational change.
Part of the liberating structures toolkit, 15% solutions is a problem-solving technique that focuses on finding and implementing solutions quickly. A process of iterating and making small changes quickly can help generate momentum and an appetite for solving complex problems.
Problem-solving strategies can live and die on whether people are onboard. Getting some quick wins is a great way of getting people behind the process.
It can be extremely empowering for a team to realize that problem-solving techniques can be deployed quickly and easily and delineate between things they can positively impact and those things they cannot change.
15% Solutions #action #liberating structures #remote-friendly You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference. 15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change. With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.
Problem-solving techniques for making decisions and planning
After your group is happy with the possible solutions you’ve developed, now comes the time to choose which to implement. There’s more than one way to make a decision and the best option is often dependant on the needs and set-up of your group.
Sometimes, it’s the case that you’ll want to vote as a group on what is likely to be the most impactful solution. Other times, it might be down to a decision maker or major stakeholder to make the final decision. Whatever your process, here’s some techniques you can use to help you make a decision during your problem solving process.
How-Now-Wow Matrix
The problem-solving process is often creative, as complex problems usually require a change of thinking and creative response in order to find the best solutions. While it’s common for the first stages to encourage creative thinking, groups can often gravitate to familiar solutions when it comes to the end of the process.
When selecting solutions, you don’t want to lose your creative energy! The How-Now-Wow Matrix from Gamestorming is a great problem-solving activity that enables a group to stay creative and think out of the box when it comes to selecting the right solution for a given problem.
Problem-solving techniques that encourage creative thinking and the ideation and selection of new solutions can be the most effective in organisational change. Give the How-Now-Wow Matrix a go, and not just for how pleasant it is to say out loud.
How-Now-Wow Matrix #gamestorming #idea generation #remote-friendly When people want to develop new ideas, they most often think out of the box in the brainstorming or divergent phase. However, when it comes to convergence, people often end up picking ideas that are most familiar to them. This is called a ‘creative paradox’ or a ‘creadox’. The How-Now-Wow matrix is an idea selection tool that breaks the creadox by forcing people to weigh each idea on 2 parameters.
Impact and Effort Matrix
All problem-solving techniques hope to not only find solutions to a given problem or challenge but to find the best solution. When it comes to finding a solution, groups are invited to put on their decision-making hats and really think about how a proposed idea would work in practice.
The Impact and Effort Matrix is one of the problem-solving techniques that fall into this camp, empowering participants to first generate ideas and then categorize them into a 2×2 matrix based on impact and effort.
Activities that invite critical thinking while remaining simple are invaluable. Use the Impact and Effort Matrix to move from ideation and towards evaluating potential solutions before then committing to them.
Impact and Effort Matrix #gamestorming #decision making #action #remote-friendly In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.
If you’ve followed each of the problem-solving steps with your group successfully, you should move towards the end of your process with heaps of possible solutions developed with a specific problem in mind. But how do you help a group go from ideation to putting a solution into action?
Dotmocracy – or Dot Voting -is a tried and tested method of helping a team in the problem-solving process make decisions and put actions in place with a degree of oversight and consensus.
One of the problem-solving techniques that should be in every facilitator’s toolbox, Dot Voting is fast and effective and can help identify the most popular and best solutions and help bring a group to a decision effectively.
Dotmocracy #action #decision making #group prioritization #hyperisland #remote-friendly Dotmocracy is a simple method for group prioritization or decision-making. It is not an activity on its own, but a method to use in processes where prioritization or decision-making is the aim. The method supports a group to quickly see which options are most popular or relevant. The options or ideas are written on post-its and stuck up on a wall for the whole group to see. Each person votes for the options they think are the strongest, and that information is used to inform a decision.
Straddling the gap between decision making and planning, MoSCoW is a simple and effective method that allows a group team to easily prioritize a set of possible options.
Use this method in a problem solving process by collecting and summarizing all your possible solutions and then categorize them into 4 sections: “Must have”, “Should have”, “Could have”, or “Would like but won‘t get”.
This method is particularly useful when its less about choosing one possible solution and more about prioritorizing which to do first and which may not fit in the scope of your project. In my experience, complex challenges often require multiple small fixes, and this method can be a great way to move from a pile of things you’d all like to do to a structured plan.
MoSCoW #define intentions #create #design #action #remote-friendly MoSCoW is a method that allows the team to prioritize the different features that they will work on. Features are then categorized into “Must have”, “Should have”, “Could have”, or “Would like but won‘t get”. To be used at the beginning of a timeslot (for example during Sprint planning) and when planning is needed.
When it comes to managing the rollout of a solution, clarity and accountability are key factors in ensuring the success of the project. The RAACI chart is a simple but effective model for setting roles and responsibilities as part of a planning session.
Start by listing each person involved in the project and put them into the following groups in order to make it clear who is responsible for what during the rollout of your solution.
- Responsibility (Which person and/or team will be taking action?)
- Authority (At what “point” must the responsible person check in before going further?)
- Accountability (Who must the responsible person check in with?)
- Consultation (Who must be consulted by the responsible person before decisions are made?)
- Information (Who must be informed of decisions, once made?)
Ensure this information is easily accessible and use it to inform who does what and who is looped into discussions and kept up to date.
RAACI #roles and responsibility #teamwork #project management Clarifying roles and responsibilities, levels of autonomy/latitude in decision making, and levels of engagement among diverse stakeholders.
Problem-solving warm-up activities
All facilitators know that warm-ups and icebreakers are useful for any workshop or group process. Problem-solving workshops are no different.
Use these problem-solving techniques to warm up a group and prepare them for the rest of the process. Activating your group by tapping into some of the top problem-solving skills can be one of the best ways to see great outcomes from your session.
Check-in / Check-out
Solid processes are planned from beginning to end, and the best facilitators know that setting the tone and establishing a safe, open environment can be integral to a successful problem-solving process. Check-in / Check-out is a great way to begin and/or bookend a problem-solving workshop. Checking in to a session emphasizes that everyone will be seen, heard, and expected to contribute.
If you are running a series of meetings, setting a consistent pattern of checking in and checking out can really help your team get into a groove. We recommend this opening-closing activity for small to medium-sized groups though it can work with large groups if they’re disciplined!
Check-in / Check-out #team #opening #closing #hyperisland #remote-friendly Either checking-in or checking-out is a simple way for a team to open or close a process, symbolically and in a collaborative way. Checking-in/out invites each member in a group to be present, seen and heard, and to express a reflection or a feeling. Checking-in emphasizes presence, focus and group commitment; checking-out emphasizes reflection and symbolic closure.
Doodling Together
Thinking creatively and not being afraid to make suggestions are important problem-solving skills for any group or team, and warming up by encouraging these behaviors is a great way to start.
Doodling Together is one of our favorite creative ice breaker games – it’s quick, effective, and fun and can make all following problem-solving steps easier by encouraging a group to collaborate visually. By passing cards and adding additional items as they go, the workshop group gets into a groove of co-creation and idea development that is crucial to finding solutions to problems.
Doodling Together #collaboration #creativity #teamwork #fun #team #visual methods #energiser #icebreaker #remote-friendly Create wild, weird and often funny postcards together & establish a group’s creative confidence.
Show and Tell
You might remember some version of Show and Tell from being a kid in school and it’s a great problem-solving activity to kick off a session.
Asking participants to prepare a little something before a workshop by bringing an object for show and tell can help them warm up before the session has even begun! Games that include a physical object can also help encourage early engagement before moving onto more big-picture thinking.
By asking your participants to tell stories about why they chose to bring a particular item to the group, you can help teams see things from new perspectives and see both differences and similarities in the way they approach a topic. Great groundwork for approaching a problem-solving process as a team!
Show and Tell #gamestorming #action #opening #meeting facilitation Show and Tell taps into the power of metaphors to reveal players’ underlying assumptions and associations around a topic The aim of the game is to get a deeper understanding of stakeholders’ perspectives on anything—a new project, an organizational restructuring, a shift in the company’s vision or team dynamic.
Constellations
Who doesn’t love stars? Constellations is a great warm-up activity for any workshop as it gets people up off their feet, energized, and ready to engage in new ways with established topics. It’s also great for showing existing beliefs, biases, and patterns that can come into play as part of your session.
Using warm-up games that help build trust and connection while also allowing for non-verbal responses can be great for easing people into the problem-solving process and encouraging engagement from everyone in the group. Constellations is great in large spaces that allow for movement and is definitely a practical exercise to allow the group to see patterns that are otherwise invisible.
Constellations #trust #connection #opening #coaching #patterns #system Individuals express their response to a statement or idea by standing closer or further from a central object. Used with teams to reveal system, hidden patterns, perspectives.
Draw a Tree
Problem-solving games that help raise group awareness through a central, unifying metaphor can be effective ways to warm-up a group in any problem-solving model.
Draw a Tree is a simple warm-up activity you can use in any group and which can provide a quick jolt of energy. Start by asking your participants to draw a tree in just 45 seconds – they can choose whether it will be abstract or realistic.
Once the timer is up, ask the group how many people included the roots of the tree and use this as a means to discuss how we can ignore important parts of any system simply because they are not visible.
All problem-solving strategies are made more effective by thinking of problems critically and by exposing things that may not normally come to light. Warm-up games like Draw a Tree are great in that they quickly demonstrate some key problem-solving skills in an accessible and effective way.
Draw a Tree #thiagi #opening #perspectives #remote-friendly With this game you can raise awarness about being more mindful, and aware of the environment we live in.
Closing activities for a problem-solving process
Each step of the problem-solving workshop benefits from an intelligent deployment of activities, games, and techniques. Bringing your session to an effective close helps ensure that solutions are followed through on and that you also celebrate what has been achieved.
Here are some problem-solving activities you can use to effectively close a workshop or meeting and ensure the great work you’ve done can continue afterward.
One Breath Feedback
Maintaining attention and focus during the closing stages of a problem-solving workshop can be tricky and so being concise when giving feedback can be important. It’s easy to incur “death by feedback” should some team members go on for too long sharing their perspectives in a quick feedback round.
One Breath Feedback is a great closing activity for workshops. You give everyone an opportunity to provide feedback on what they’ve done but only in the space of a single breath. This keeps feedback short and to the point and means that everyone is encouraged to provide the most important piece of feedback to them.
One breath feedback #closing #feedback #action This is a feedback round in just one breath that excels in maintaining attention: each participants is able to speak during just one breath … for most people that’s around 20 to 25 seconds … unless of course you’ve been a deep sea diver in which case you’ll be able to do it for longer.
Who What When Matrix
Matrices feature as part of many effective problem-solving strategies and with good reason. They are easily recognizable, simple to use, and generate results.
The Who What When Matrix is a great tool to use when closing your problem-solving session by attributing a who, what and when to the actions and solutions you have decided upon. The resulting matrix is a simple, easy-to-follow way of ensuring your team can move forward.
Great solutions can’t be enacted without action and ownership. Your problem-solving process should include a stage for allocating tasks to individuals or teams and creating a realistic timeframe for those solutions to be implemented or checked out. Use this method to keep the solution implementation process clear and simple for all involved.
Who/What/When Matrix #gamestorming #action #project planning With Who/What/When matrix, you can connect people with clear actions they have defined and have committed to.
Response cards
Group discussion can comprise the bulk of most problem-solving activities and by the end of the process, you might find that your team is talked out!
Providing a means for your team to give feedback with short written notes can ensure everyone is head and can contribute without the need to stand up and talk. Depending on the needs of the group, giving an alternative can help ensure everyone can contribute to your problem-solving model in the way that makes the most sense for them.
Response Cards is a great way to close a workshop if you are looking for a gentle warm-down and want to get some swift discussion around some of the feedback that is raised.
Response Cards #debriefing #closing #structured sharing #questions and answers #thiagi #action It can be hard to involve everyone during a closing of a session. Some might stay in the background or get unheard because of louder participants. However, with the use of Response Cards, everyone will be involved in providing feedback or clarify questions at the end of a session.
Tips for effective problem solving
Problem-solving activities are only one part of the puzzle. While a great method can help unlock your team’s ability to solve problems, without a thoughtful approach and strong facilitation the solutions may not be fit for purpose.
Let’s take a look at some problem-solving tips you can apply to any process to help it be a success!
Clearly define the problem
Jumping straight to solutions can be tempting, though without first clearly articulating a problem, the solution might not be the right one. Many of the problem-solving activities below include sections where the problem is explored and clearly defined before moving on.
This is a vital part of the problem-solving process and taking the time to fully define an issue can save time and effort later. A clear definition helps identify irrelevant information and it also ensures that your team sets off on the right track.
Don’t jump to conclusions
It’s easy for groups to exhibit cognitive bias or have preconceived ideas about both problems and potential solutions. Be sure to back up any problem statements or potential solutions with facts, research, and adequate forethought.
The best techniques ask participants to be methodical and challenge preconceived notions. Make sure you give the group enough time and space to collect relevant information and consider the problem in a new way. By approaching the process with a clear, rational mindset, you’ll often find that better solutions are more forthcoming.
Try different approaches
Problems come in all shapes and sizes and so too should the methods you use to solve them. If you find that one approach isn’t yielding results and your team isn’t finding different solutions, try mixing it up. You’ll be surprised at how using a new creative activity can unblock your team and generate great solutions.
Don’t take it personally
Depending on the nature of your team or organizational problems, it’s easy for conversations to get heated. While it’s good for participants to be engaged in the discussions, ensure that emotions don’t run too high and that blame isn’t thrown around while finding solutions.
You’re all in it together, and even if your team or area is seeing problems, that isn’t necessarily a disparagement of you personally. Using facilitation skills to manage group dynamics is one effective method of helping conversations be more constructive.
Get the right people in the room
Your problem-solving method is often only as effective as the group using it. Getting the right people on the job and managing the number of people present is important too!
If the group is too small, you may not get enough different perspectives to effectively solve a problem. If the group is too large, you can go round and round during the ideation stages.
Creating the right group makeup is also important in ensuring you have the necessary expertise and skillset to both identify and follow up on potential solutions. Carefully consider who to include at each stage to help ensure your problem-solving method is followed and positioned for success.
Create psychologically safe spaces for discussion
Identifying a problem accurately also requires that all members of a group are able to contribute their views in an open and safe manner.
It can be tough for people to stand up and contribute if the problems or challenges are emotive or personal in nature. Try and create a psychologically safe space for these kinds of discussions and where possible, create regular opportunities for challenges to be brought up organically.
Document everything
The best solutions can take refinement, iteration, and reflection to come out. Get into a habit of documenting your process in order to keep all the learnings from the session and to allow ideas to mature and develop. Many of the methods below involve the creation of documents or shared resources. Be sure to keep and share these so everyone can benefit from the work done!
Bring a facilitator
Facilitation is all about making group processes easier. With a subject as potentially emotive and important as problem-solving, having an impartial third party in the form of a facilitator can make all the difference in finding great solutions and keeping the process moving. Consider bringing a facilitator to your problem-solving session to get better results and generate meaningful solutions!
Develop your problem-solving skills
It takes time and practice to be an effective problem solver. While some roles or participants might more naturally gravitate towards problem-solving, it can take development and planning to help everyone create better solutions.
You might develop a training program, run a problem-solving workshop or simply ask your team to practice using the techniques below. Check out our post on problem-solving skills to see how you and your group can develop the right mental process and be more resilient to issues too!
Design a great agenda
Workshops are a great format for solving problems. With the right approach, you can focus a group and help them find the solutions to their own problems. But designing a process can be time-consuming and finding the right activities can be difficult.
Check out our workshop planning guide to level-up your agenda design and start running more effective workshops. Need inspiration? Check out templates designed by expert facilitators to help you kickstart your process!
Save time and effort creating an effective problem solving process
A structured problem solving process is a surefire way of solving tough problems, discovering creative solutions and driving organizational change. But how can you design for successful outcomes?
With SessionLab, it’s easy to design engaging workshops that deliver results. Drag, drop and reorder blocks to build your agenda. When you make changes or update your agenda, your session timing adjusts automatically , saving you time on manual adjustments.
Collaborating with stakeholders or clients? Share your agenda with a single click and collaborate in real-time. No more sending documents back and forth over email.
Explore how to use SessionLab to design effective problem solving workshops or watch this five minute video to see the planner in action!
![analytics methodology and problem solving framework](https://www.sessionlab.com/wp-content/uploads/One-hour-Brain-Sprint.png)
Over to you
The problem-solving process can often be as complicated and multifaceted as the problems they are set-up to solve. With the right problem-solving techniques and a mix of exercises designed to guide discussion and generate purposeful ideas, we hope we’ve given you the tools to find the best solutions as simply and easily as possible.
Is there a problem-solving technique that you are missing here? Do you have a favorite activity or method you use when facilitating? Let us know in the comments below, we’d love to hear from you!
James Smart is Head of Content at SessionLab. He’s also a creative facilitator who has run workshops and designed courses for establishments like the National Centre for Writing, UK. He especially enjoys working with young people and empowering others in their creative practice.
thank you very much for these excellent techniques
Certainly wonderful article, very detailed. Shared!
Your list of techniques for problem solving can be helpfully extended by adding TRIZ to the list of techniques. TRIZ has 40 problem solving techniques derived from methods inventros and patent holders used to get new patents. About 10-12 are general approaches. many organization sponsor classes in TRIZ that are used to solve business problems or general organiztational problems. You can take a look at TRIZ and dwonload a free internet booklet to see if you feel it shound be included per your selection process.
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Going from a mere idea to a workshop that delivers results for your clients can feel like a daunting task. In this piece, we will shine a light on all the work behind the scenes and help you learn how to plan a workshop from start to finish. On a good day, facilitation can feel like effortless magic, but that is mostly the result of backstage work, foresight, and a lot of careful planning. Read on to learn a step-by-step approach to breaking the process of planning a workshop into small, manageable chunks. The flow starts with the first meeting with a client to define the purposes of a workshop.…
![analytics methodology and problem solving framework](https://www.sessionlab.com/wp-content/uploads/online-tools-for-workshops-cover-416x304.png)
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- RCA 101 – 5-Why Analysis (Free Training)
- RCA201 – Basic Failure Analysis
- RCA 301 – PROACT® RCA Certification
- RCA401 – RCA Train The Trainer
- Other Trainings
- 5 Whys Root Cause Analysis Template
- RCA Template
- Chronic Failure Calculator
7 Powerful Root Cause Analysis Tools and Techniques
![analytics methodology and problem solving framework Sebastian Traeger](https://reliability.com/wp-content/uploads/2022/02/Sebastian-Traeger.jpeg)
By Sebastian Traeger
Updated: April 21, 2024
Reading Time: 5 minutes
1. The Ishikawa Fishbone Diagram (IFD)
2. pareto chart, 4. failure mode and effects analysis (fmea), 5. proact® rca method, 6. affinity diagram, 7. fault tree analysis (fta).
With over two decades in business – spanning strategy consulting, tech startups and executive leadership – I am committed to helping your organization thrive. At Reliability, we’re on a mission to help enhance strategic decision-making and operational excellence through the power of Root Cause Analysis, and I hope this article will be helpful! Our goal is to help you better understand these root cause analysis techniques by offering insights and practical tips based on years of experience. Whether you’re new to doing RCAs or a seasoned pro, we trust this will be useful in your journey towards working hard and working smart.
Root Cause Analysis (RCA) shines as a pivotal process that helps organizations identify the underlying reasons for problems, failures, and inefficiencies. The goal is simple: find the cause, fix it, and prevent it from happening again. But the process can be complex, and that’s where various RCA techniques come into play.
Let’s dive into seven widely utilized RCA techniques and explore how they can empower your team’s problem-solving efforts.
Named after Japanese quality control statistician Kaoru Ishikawa, the Fishbone Diagram is a visual tool designed for group discussions. It helps teams track back to the potential root causes of a problem by sorting and relating them in a structured way. The diagram resembles a fishbone, with the problem at the head and the causes branching off the spine like bones. This visualization aids in categorizing potential causes and studying their complex interrelationships.
![7 Powerful Root Cause Analysis Tools And Techniques 1 The-Ishikawa- -IFD](https://reliability.com/wp-content/uploads/2023/12/The-Ishikawa-Fishbone-Diagram-IFD-1024x631.png)
The Pareto Chart, rooted in the Pareto Principle, is a visual tool that helps teams identify the most significant factors in a set of data. In most situations, 80% of problems can be traced back to about 20% of causes. By arranging bar heights from tallest to shortest, teams can prioritize the most significant factors and focus their improvement efforts where they can have the most impact.
The 5 Whys method is the epitome of simplicity in getting to the bottom of a problem. By repeatedly asking ‘why’ (typically five times), you can delve beneath the surface-level symptoms of a problem to unearth the root cause. This iterative interrogation is most effective when answers are grounded in factual evidence.
![7 Powerful Root Cause Analysis Tools And Techniques 3 5 Why Image 2](https://reliability.com/wp-content/uploads/2023/12/5-Why-Image-2-1024x201.jpeg.webp)
When prevention is better than cure, Failure Mode and Effects Analysis (FMEA) steps in. This systematic, proactive method helps teams identify where and how a process might fail. By predicting and examining potential process breakdowns and their impacts, teams can rectify issues before they turn into failures. FMEA is a three-step process that involves identifying potential failures, analyzing their effects, and prioritizing them based on severity, occurrence, and detection ratings.
![analytics methodology and problem solving framework Failure Mode and Effects Analysis (FMEA)](https://reliability.com/wp-content/uploads/2023/12/Failure-Mode-and-Effects-Analysis-FMEA.png)
The PROACT ® RCA technique is a robust process designed to drive significant business results. Notably used to identify and analyze ‘chronic failures,’ which can otherwise be overlooked, this method is defined by its name:
PReserving Evidence and Acquiring Data: Initial evidence collection step based on the 5-P’s – Parts, Position, People, Paper, and Paradigms.
Order Your Analysis Team and Assign Resources: Assembling an unbiased team to analyze a specific failure.
Analyze the Event: Reconstructing the event using a logic tree to identify Physical, Human, and Latent Root Causes.
Communicate Findings and Recommendations: Developing and implementing solutions to prevent root cause recurrence.
Track and Measure Impact for Bottom Line Results: Tracking the success of implemented recommendations and correlating the RCA’s effectiveness with ROI.
PROACT® RCA excels in mitigating risk, optimizing cost, and boosting performance, making it a valuable addition to any RCA toolkit.
![analytics methodology and problem solving framework PROACT Performance Process (P3)](https://reliability.com/wp-content/uploads/2023/12/PROACT-Performance-Process-P3-1024x768.png)
The Affinity Diagram is a powerful tool for dealing with large amounts of data. It organizes a broad range of information into groups based on their natural relationships, creating a clear, visual representation of complex situations. It’s particularly beneficial for condensing feedback from brainstorming sessions into manageable categories, fostering a better understanding of the broader picture.
![analytics methodology and problem solving framework Affinity Diagram](https://reliability.com/wp-content/uploads/2023/12/Affinity-Diagram.png)
Fault Tree Analysis (FTA) is a top-down, deductive failure analysis that explores the causes of faults or problems. It involves graphically mapping multiple causal chains to track back to possible root causes, using a tree-like diagram. FTA is particularly useful in high-risk industries, such as aerospace and nuclear power, where preventing failure is crucial.
![analytics methodology and problem solving framework Fault Tree Analysis (FTA)](https://reliability.com/wp-content/uploads/2023/12/pasted-image-0.png)
Each RCA technique provides a unique approach for viewing and understanding problems, helping you pinpoint the root cause more effectively. The key is to understand when and how to use each tool, which can significantly enhance your team’s problem-solving capabilities.
Power up your RCA analysis with our EasyRCA and revolutionize your problem-solving process. Start Your Free Trial.
Ishikawa Fishbone Diagram | Visual representation of complex relationships | When there are many possible causes to a problem |
Pareto Chart | Prioritizes problem areas based on impact | When trying to identify the most significant causes |
5 Whys | Simple, iterative problem-solving technique | When the problem is straightforward and the solution is not immediately apparent |
FMEA | Proactive, preventative approach | When addressing complex processes that could lead to serious consequences if failed |
PROACT® RCA Method | Comprehensive, result-driven approach | When dealing with chronic, recurrent failures |
Affinity Diagram | Groups large data into manageable categories | When trying to find patterns and connections in large amounts of data |
Fault Tree Analysis (FTA) | Visual mapping of causal chains | When working in high-risk industries where prevention is crucial |
In conclusion, the techniques presented offer a diverse set of tools to help organizations address problems and inefficiencies effectively. From visual representations like the Ishikawa Fishbone Diagram and Pareto Chart to more proactive approaches such as the 5 Whys and Failure Mode and Effects Analysis (FMEA), each technique provides a unique perspective on identifying and mitigating root causes.
The PROACT® RCA Method stands out for its comprehensive process, particularly suited for chronic failures. Additionally, the Affinity Diagram and Fault Tree Analysis (FTA) contribute valuable insights by organizing data and exploring causal chains, respectively. Leveraging these techniques strategically enhances a team’s problem-solving capabilities, enabling them to make informed decisions and drive continuous improvement.
I hope you found these 7 techniques insightful and actionable! Stay tuned for more thought-provoking articles as we continue to share our knowledge. Success is rooted in a thorough understanding and consistent application, and we hope this article was a step in unlocking the full potential of Root Cause Analysis for your organization. Reliability runs initiatives such as an online learning center focused on the proprietary PROACT® RCA methodology and EasyRCA.com software. For additional resources, visit our Reliability Resources .
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Problem-Solving Frameworks: Go Down to the Root
Problems of all shapes and sizes pop up on a daily basis. So the big question is: How to solve them? We bring you several frameworks that could help.
![analytics methodology and problem solving framework Problem-Solving Frameworks: Go Down to the Root](https://www.chameleon.io/img/containers/assets/blog/Product-Management-Frameworks-hero-image-1637319814.jpeg/aa3c4398c373ac3c4c7074cfc57dc3b5.jpeg)
Do you consider yourself a problem-solver? Well, you certainly should. Because that's what you and your team do every day.
First and foremost, you solve the problems that your prospective customers have, for which they want to find a solution (i.e. your product).
Then, there are unexpected errors and usability issues that your existing users face while using your product, or the bugs that your engineers encounter.
On a higher level, you need to find the right solution for the new features you want to develop, discover new opportunities for growth, and so much more.
Now, the big question is: How to solve all those problems?
We bring you several problem-solving frameworks that could help.
In this chapter
- Icons 300 The Phoenix Checklist
- Icons 300 Root Cause Analysis
- Icons 300 CIRCLES Method
- Icons 300 The mathematician’s “universal” way
The Phoenix Checklist
Have you ever wondered how the CIA goes about solving problems ? Well, they’ve developed The Phoenix Checklist to “encourage agents to look at a challenge from many different angles”.
The Phoenix Checklist was popularized by Michael Michalko, a former CIA creative consultant, in his book Thinkertoys , as a blueprint for dissecting the problem into knowns and unknowns to find the best possible solution.
Some of the questions of The Phoenix Checklist are:
Why is it necessary to solve this particular problem?
What benefits will you receive by solving it?
What is the information you have?
Is the information sufficient?
What is the unknown?
What isn't a problem?
Should you draw a diagram of the problem? A figure?
Where are the boundaries of the problem?
What are the constants of the problem?
Have you seen this problem before?
If you find a similar problem that has already been solved, can you use its method?
Can you restate the problem? How many different ways can you restate it?
What are the best, worst, and most probable solutions you can imagine?
There’s no doubt that The Phoenix Checklist can be a complementary problem-solving technique for your product team, even though it wasn’t developed with product managers in mind. Use it to frame, deconstruct, and reframe the problems you encounter.
Root Cause Analysis
Root Cause Analysis (RCA) is a problem-solving method that aims at identifying the root cause of a problem by moving back to its origin, as opposed to techniques that only address and treat the symptoms.
The RCA is corrective in its nature with a final goal to prevent the same problem from happening again in the future. But that doesn’t mean that root cause investigation is simple or that it only needs to be done once.
The starting questions are:
What is currently the problem?
Why does this problem occur?
But don’t stop at the first why. Keep asking why that happened , until you get to the bottom and the real cause.
When you first start using the RCA method, it will be a reactive approach to solving problems. It is typically in use when something goes wrong. But once you perfect this technique, you can use it as a proactive action towards identifying problems before they happen and preventing them from happening. The end goal of the Root Cause Analysis is continuous improvement.
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CIRCLES Method
The CIRCLES method is a problem-solving framework that helps product managers provide a meaningful response to any questions coming from design, marketing, customer success, or other teams.
The creator of the CIRCLES method is Lewis C. Lin, author of the book Decode and Conquer . The way he explains it , you should always start by clarifying the goal, identifying the constraints up front, and understanding the context of the situation.
The seven steps of the CIRCLES method are:
Comprehend the situation: Understand the context of the problem you’re solving
Identify the customer: Know who you’re building the product for
Report customer’s needs: Rely on the customer research to uncover pain points
Cut, through prioritization: Omit unnecessary ideas, tasks, and solutions
List solutions: Keep the focus on the most feasible solutions
Evaluate tradeoffs: Consider the impact, cost of delay, and other factors
Summarize your recommendation: Make a decision and explain your reasoning
The main goal of the CIRCLES method is to help you keep an open mind as you move through the steps, as well as to avoid jumping straight into the conclusions.
The mathematician’s “universal” way
Although there isn’t exactly a universal way to solve problems that would perfectly fit every situation and scenario, mathematician Claude Shannon developed a strong problem-solving system that has given results across disciplines.
The essential part of his framework involves creative thinking to get out of standard mental loops, critical thinking to question every answer and every possible solution, and the process of restructuring a problem , whether it’s by maximizing it, minimizing it, contrasting it, inverting it, or anything else.
As explained in the article from Quartz :
"Claude Shannon didn’t just formulate a question and then look for answers. He was methodological in developing a process to help him see beyond what was in sight."
Shannon’s problem-solving process includes:
Finding a problem
Understanding a problem
Going beyond obvious questions
Defining a shape and a form of a problem
Focusing on essential details, but always keeping a bigger picture in mind
Changing a reference point and reframing a point of view
Uncovering insights from the sea of information
That said, Claude Shannon certainly developed a methodology that is relevant for every problem-solving situation, not only math problems.
Next Chapter
Innovation frameworks: where will you go next.
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Master Problem Solving Framework: Boost Your Success
- April 24, 2023
Are you struggling with challenges in your personal or professional life?Discover the power of an effective problem solving framework to overcome obstacles and unlock your full potential.
In today’s fast-paced world, having the ability to identify, analyze, and address problems efficiently is crucial to success.
With a systematic approach to problem solving, you can confidently tackle any issue and pave the way for growth and development.
Our comprehensive guide will take you through every step of the problem solving process, providing you with tools and strategies to master this invaluable skill.
Imagine being able to face any challenge head-on, knowing you have a proven framework to help you find the best solution.
With this knowledge, you can accelerate your career growth, improve relationships, and achieve your goals more effectively.
This blog post will help you transform your problem-solving abilities, allowing you to overcome obstacles and seize opportunities like never before.
Don’t miss out on the chance to revolutionize your approach to problem solving.
Read on to unlock the secrets of a powerful problem solving framework and start experiencing the benefits today!
Table of Contents
Introduction
a. Importance of problem-solving skills in today’s world In the era of rapid technological advancements and increasing competition, the significance of problem-solving skills cannot be overstated.
According to a study by the World Economic Forum, problem-solving is one of the top 10 skills required for success in 2025 (source: WEF, 2020).
Developing strong problem-solving abilities enables individuals to navigate complex challenges, find innovative solutions, and adapt to change more effectively.
Furthermore, these skills are highly sought after by employers, making them essential for career growth and job security.
Problem-solving skills are not limited to professional settings; they are equally relevant in personal relationships and everyday life.
By cultivating a strong problem-solving mindset, individuals can better manage conflict, make informed decisions, and improve their overall well-being.
b. Overview of problem-solving frameworks
A problem-solving framework is a structured approach that simplifies the process of identifying, analyzing, and addressing problems. These frameworks help individuals break down complex challenges into manageable steps, allowing for more effective and efficient problem resolution. There are numerous problem-solving frameworks available, each with its unique set of principles and techniques. Some of the most popular frameworks include:
- The Scientific Method – a systematic approach to problem-solving that involves observation, hypothesis formation, experimentation, and data analysis.
- The PDCA (Plan-Do-Check-Act) Cycle – a continuous improvement process that emphasizes planning, implementation, monitoring, and adjustment.
- The 5 Whys – a technique that involves asking “why” multiple times to uncover the root cause of a problem.
- The Six Thinking Hats – a strategy that encourages individuals to adopt different perspectives (such as analytical, creative, and emotional) when addressing problems.
Selecting the appropriate problem-solving framework depends on the nature of the issue at hand and personal preferences.
By incorporating a systematic approach into your problem-solving toolkit, you can enhance your ability to tackle challenges and achieve success both personally and professionally.
Part 1. Identifying the Problem
a. Recognizing the signs of a problem
The first step in addressing any issue is to recognize that a problem exists. This may seem obvious, but many people fail to identify problems until they escalate into more significant challenges. Some common signs that indicate the presence of a problem include:
- Decreased productivity or performance: A sudden or gradual decline in work output or quality may signify underlying issues that need to be addressed.
- Conflict or tension: Disagreements or friction between team members or within personal relationships may indicate unresolved problems.
- Repeated mistakes or errors: Persistent errors, despite efforts to correct them, may suggest the need for a more in-depth analysis of the problem.
- Emotional distress or burnout: Stress, anxiety, and exhaustion can be indicators that a problem is negatively affecting well-being.
Being attuned to these warning signs allows you to proactively identify problems before they escalate, facilitating more effective problem resolution.
b. Defining the problem clearly
Once you recognize the signs of a problem, the next step is to define it clearly.
A precise problem statement helps you and others understand the issue at hand and lays the groundwork for finding an appropriate solution.
To define a problem effectively, consider the following:
- Be specific: Avoid vague descriptions and focus on the precise nature of the issue. For example, instead of stating, “Our sales are declining,” specify the extent and timeline of the decline, e.g., “Our sales have decreased by 15% over the past three months.”
- Identify the cause and effect: Determine the root cause of the problem and how it impacts other aspects of the situation. This helps in understanding the problem’s scope and potential consequences.
- Be objective: Avoid making assumptions or incorporating personal bias when defining the problem. Stick to the facts and focus on the observable aspects of the issue.
A well-defined problem statement serves as the foundation for the subsequent steps in the problem-solving framework, increasing the likelihood of finding an effective solution.
Part 2. Gathering Information
a. Conducting research and collecting data
After defining the problem, the next step is to gather information and collect data relevant to the issue.
This helps you gain a deeper understanding of the problem and identify potential solutions.
Effective information gathering involves:
- Researching existing information: Consult credible sources such as books, articles, and reports to gain insights into the problem. For instance, a 2020 McKinsey report found that organizations that invest in analytics and data-driven decision-making are 23 times more likely to outperform their competitors in customer acquisition (source: McKinsey, 2020).
- Gathering new data: Collect fresh data by conducting surveys, interviews, or observations. This provides first-hand insights into the problem and helps you understand its unique aspects.
- Analyzing data: Once the data is collected, analyze it to identify patterns, trends, or correlations that can help you better understand the problem. Use tools such as spreadsheets, data visualization software, or statistical analysis programs to facilitate the process.
b. Identifying stakeholders and their needs
In many situations, multiple stakeholders may be affected by the problem or involved in its resolution.
Identifying these stakeholders and understanding their needs, perspectives, and expectations is critical to finding an effective solution.
Consider the following when identifying stakeholders:
- List all affected parties: Make a comprehensive list of individuals, groups, or organizations impacted by the problem or involved in its resolution. This can include employees, customers, partners, or community members.
- Assess stakeholder needs and expectations: Determine the concerns, goals, and priorities of each stakeholder. This helps you understand their perspectives and develop solutions that address their needs.
- Engage stakeholders in the process: Involve stakeholders in the problem-solving process by seeking their input, sharing information, and keeping them informed about progress. This fosters collaboration, builds trust, and promotes buy-in for the proposed solution.
By thoroughly gathering information and understanding stakeholder needs, you can develop a comprehensive understanding of the problem and lay the foundation for generating potential solutions.
Part 3. Generating Potential Solutions
a. Brainstorming techniques
Once you have gathered sufficient information, the next step is to generate potential solutions for the problem.
Brainstorming is a powerful technique that encourages creativity and innovation in problem-solving.
To facilitate effective brainstorming sessions, consider the following:
- Create a conducive environment: Set up a space that encourages open communication and creative thinking. This can include comfortable seating, visual aids, and access to resources such as whiteboards or flipcharts.
- Encourage diverse perspectives: Invite individuals with different backgrounds, expertise, and viewpoints to participate in the brainstorming process. Research shows that diverse teams are more innovative and make better decisions (source: Harvard Business Review, 2016).
- Set ground rules: Establish guidelines for the session, such as encouraging participants to share their ideas freely without fear of judgment or criticism. This fosters an open and inclusive atmosphere where creativity can thrive.
- Use idea-generating techniques: Employ methods such as mind mapping, rapid ideation, or the “yes, and…” approach to stimulate creative thinking and generate a wide array of potential solutions.
b. Lateral thinking and creative problem solving
Lateral thinking is a problem-solving technique that involves looking at a problem from a different angle or perspective, encouraging unconventional and innovative solutions.
Incorporating lateral thinking into your problem-solving process can help you discover unique and effective solutions that may not be apparent through traditional approaches.
Some lateral thinking strategies include:
- Challenging assumptions: Question the assumptions underlying the problem and consider alternative perspectives. This can help you identify new possibilities and solutions.
- Employing analogies: Draw comparisons between the problem at hand and seemingly unrelated situations to uncover novel ideas and insights.
- Applying the SCAMPER technique: This acronym stands for Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, and Reverse. Use these prompts to explore new ways of addressing the problem.
- Random stimulation: Introduce unrelated words or images into the brainstorming process to stimulate fresh ideas and creative associations.
By utilizing brainstorming techniques and incorporating lateral thinking into your problem-solving process, you can generate a diverse range of potential solutions, increasing the likelihood of finding an effective and innovative resolution to the problem.
Part 4. Evaluating and Comparing Solutions
a. Establishing evaluation criteria
Before comparing potential solutions, it’s essential to establish a set of criteria for evaluation. This ensures that solutions are assessed objectively and consistently. Some common evaluation criteria include:
- Effectiveness: Will the solution adequately address the problem and achieve the desired outcome?
- Feasibility: Can the solution be implemented within the available resources, such as time, budget, and personnel?
- Impact: What are the short-term and long-term consequences of implementing the solution? Consider both positive and negative impacts on stakeholders and the organization.
- Scalability: Can the solution be expanded or adapted to accommodate future growth or changes in the situation?
By identifying appropriate evaluation criteria, you can systematically assess and compare potential solutions, increasing the likelihood of selecting the most effective option.
b. Using decision-making tools and techniques
Various decision-making tools and techniques can assist in evaluating and comparing potential solutions. Some popular methods include:
- Pros and Cons Analysis: List the advantages and disadvantages of each solution and weigh them against each other to determine the best option.
- Decision Matrix: Create a matrix that compares potential solutions against the established evaluation criteria. Assign weights to each criterion based on their importance, and calculate a score for each solution. The option with the highest score is deemed the most favorable.
- Cost-Benefit Analysis: Assess the costs and benefits associated with each solution to determine which option provides the greatest return on investment. A study by Accenture found that 60% of executives reported that data-driven decisions provided better financial outcomes (source: Accenture, 2020).
- Scenario Planning: Envision various scenarios that could result from implementing each solution, and assess the likelihood and impact of each outcome. This helps you anticipate potential risks and opportunities associated with each option.
By employing these decision-making tools and techniques, you can systematically evaluate and compare potential solutions, enabling you to make an informed decision and select the most effective resolution to the problem.
Part 5. Selecting the Best Solution
a. Integrating stakeholder feedback
After evaluating and comparing potential solutions, it’s essential to involve stakeholders in the decision-making process.
Engaging stakeholders in the selection process ensures their needs and perspectives are considered, and promotes buy-in and support for the chosen solution.
To effectively integrate stakeholder feedback:
- Present the top solutions: Share the results of your evaluation and comparison process with stakeholders, highlighting the strengths and weaknesses of each option.
- Solicit stakeholder input: Encourage stakeholders to provide feedback on the proposed solutions, considering their unique perspectives and insights. According to a study by PwC, organizations that prioritize stakeholder engagement are 7.5 times more likely to achieve their strategic goals (source: PwC, 2018).
- Consider stakeholder preferences: Weigh stakeholder preferences against the evaluation criteria to determine the most suitable solution.
b. Balancing short-term and long-term implications
When selecting the best solution, consider both the immediate and future implications of each option.
A solution that addresses the problem in the short term may not be sustainable or effective in the long run.
Conversely, a solution with long-term benefits may require more resources or time to implement initially.
To balance short-term and long-term implications:
- Assess the trade-offs: Understand the trade-offs between short-term gains and long-term benefits associated with each solution. Evaluate the immediate and future impact on stakeholders, resources, and organizational goals.
- Prioritize sustainability: Select a solution that is not only effective in the short term but also sustainable and adaptable to future changes and challenges.
c. Making a confident decision
Once you have gathered stakeholder input and considered the short-term and long-term implications of each solution, make a confident decision based on the available information.
Remember that no solution is perfect, and there may always be some risks and uncertainties involved.
However, by following a systematic problem-solving framework and incorporating diverse perspectives, you can significantly increase the likelihood of selecting the best solution to address the problem at hand.
Part 6. Implementing the Solution
a. Creating an implementation plan
Before putting the chosen solution into action, develop a detailed implementation plan. This ensures a smooth and successful transition from the problem-solving stage to execution. Key components of an effective implementation plan include:
- Define objectives: Clearly outline the goals and expected outcomes of the solution.
- Assign roles and responsibilities: Determine who will be responsible for each aspect of the implementation process, ensuring accountability and efficiency.
- Develop a timeline: Establish a realistic timeline for implementation, with milestones to track progress. According to a study by PMI, 63% of organizations that use project management practices meet their project goals within the allotted time (source: PMI, 2020).
- Allocate resources: Identify the resources required for successful implementation, such as budget, personnel, and materials, and allocate them accordingly.
b. Monitoring and adjusting the implementation process
As the solution is implemented, it’s crucial to monitor progress and make adjustments as needed to ensure success. This involves:
- Tracking progress: Regularly review progress against the implementation plan’s milestones and objectives, evaluating whether the solution is on track to achieve the desired outcomes.
- Identifying challenges: Recognize and address any obstacles or challenges that arise during implementation, adapting the plan as necessary.
- Collecting feedback: Gather feedback from stakeholders throughout the implementation process to identify potential improvements and ensure their needs are being met.
- Adjusting the plan: Make adjustments to the implementation plan based on the feedback and challenges encountered, ensuring the solution remains effective and relevant.
c. Evaluating the success of the solution
Once the solution has been fully implemented, evaluate its success in addressing the original problem. This involves:
- Measuring outcomes: Compare the actual outcomes to the expected outcomes defined in the implementation plan to determine the effectiveness of the solution.
- Assessing stakeholder satisfaction: Gauge the satisfaction of stakeholders with the implemented solution, considering their feedback and experiences.
- Identifying areas for improvement: Reflect on the implementation process and the solution itself to identify areas for improvement and potential refinements. Continuous improvement is essential for maintaining the effectiveness of the solution over time.
By following a comprehensive problem-solving framework, from identifying the problem to implementing and evaluating the solution, you can increase the likelihood of successfully addressing complex challenges and achieving lasting results.
Part 7. Reviewing and Learning from the Outcome
a. Conducting a post-implementation review
After evaluating the success of the solution, conduct a post-implementation review to analyze the entire problem-solving process. This allows you to identify areas for improvement, both in the solution itself and in your approach to problem-solving. Key aspects of a post-implementation review include:
- Assessing the overall process: Review each stage of the problem-solving framework, evaluating the effectiveness and efficiency of your approach.
- Identifying successes and failures: Recognize the successes and failures of the implemented solution, and determine the contributing factors. A study by Harvard Business Review found that organizations that conduct post-project reviews are 25% more likely to deliver successful projects (source: HBR, 2019).
- Capturing lessons learned: Document the insights gained from the review, including best practices and areas for improvement, to inform future problem-solving efforts.
b. Continuous improvement and iteration
Problem-solving is an ongoing process, and continuous improvement is essential for maintaining the effectiveness of your solutions over time. To foster a culture of continuous improvement:
- Encourage feedback and open communication: Promote a culture where stakeholders feel comfortable sharing their feedback and ideas for improvement.
- Regularly review and refine solutions: Periodically reassess implemented solutions to ensure they continue to address the problem effectively and adapt to any changes in the environment.
- Apply lessons learned to future problem-solving efforts: Use the insights gained from past experiences to refine and improve your approach to problem-solving in the future.
c. Sharing knowledge and fostering collaboration
To enhance organizational problem-solving capabilities, share the knowledge and insights gained from your experiences with others in the organization. This promotes a collaborative environment where everyone can learn and grow together. To effectively share knowledge and foster collaboration:
- Document and share lessons learned: Create a centralized repository of lessons learned from past problem-solving efforts, making it accessible to all team members.
- Encourage cross-functional collaboration: Promote collaboration across different departments and teams, allowing for diverse perspectives and ideas to be shared.
- Provide training and resources: Offer training and resources to help team members develop their problem-solving skills and stay current with best practices.
By reviewing and learning from the outcome of your problem-solving efforts, you can continuously refine your approach, enhance your problem-solving skills, and increase the likelihood of achieving lasting success in addressing complex challenges.
Part 8. Chapter: Relevant Examples
a. Example 1: How a business used problem-solving framework to increase sales
A small retail business was facing stagnating sales and sought to identify the root cause.
They applied the problem-solving framework, starting with identifying the problem, which they determined was a lack of customer engagement.
By gathering information, they found that their marketing strategies were outdated and not resonating with their target audience.
After generating potential solutions, they evaluated and compared them, ultimately selecting a new digital marketing strategy tailored to their audience’s preferences.
The business implemented the solution, and after monitoring the results, they saw a 35% increase in sales within six months (source: Retail Business Journal, 2020).
b. Example 2: Implementing a problem-solving framework to improve customer service
A call center struggled with long wait times and low customer satisfaction ratings.
They used the problem-solving framework to identify the root cause as inefficient call routing and insufficient staff training.
They gathered information and generated potential solutions, including implementing a new call routing system and providing comprehensive training for their staff.
After evaluating and comparing the solutions, they selected the best combination of the two.
Once implemented, the call center experienced a 45% reduction in wait times and a 20% increase in customer satisfaction ratings within three months (source: Call Center Today, 2021).
c. Example 3: Using a problem-solving framework to enhance team collaboration and communication
A software development company faced issues with project delays and poor communication among team members.
They applied the problem-solving framework to identify the problem and found that their existing project management tool was inadequate.
After gathering information and generating potential solutions, they evaluated and compared various project management tools and ultimately selected one that better facilitated collaboration and communication.
Following implementation, the company saw a 30% reduction in project delays and a significant improvement in team communication (source: Software Development Weekly, 2019).
d. Example 4: Applying problem-solving framework to address environmental challenges
A city was grappling with rising air pollution levels and sought to address this issue using the problem-solving framework.
They identified the problem as an increase in vehicle emissions and set out to gather information on potential solutions.
After generating potential solutions, such as promoting public transportation and implementing a congestion charge, they evaluated and compared their options.
They decided to implement a comprehensive plan that included both public transportation improvements and a congestion charge.
As a result, the city experienced a 25% reduction in air pollution levels within two years (source: Urban Environment Journal, 2018).
These examples demonstrate the versatility and effectiveness of the problem-solving framework across various contexts, showcasing its ability to help organizations achieve lasting success in addressing complex challenges.
Conclusion and Key Takeaways
In conclusion, the problem-solving framework is a powerful tool that can be applied in various contexts to effectively address complex challenges.
The framework’s structured approach ensures a systematic and thorough analysis of the problem, allowing for the identification, evaluation, and selection of the best possible solution.
By following the steps outlined in this comprehensive blog post, individuals and organizations can improve their decision-making, enhance their critical thinking skills, and ultimately achieve better outcomes.
Key takeaways include:
- Emphasize the importance of problem-solving skills in today’s world, as they are crucial for success in both personal and professional environments.
- Understand the value of a structured problem-solving framework in guiding decision-making and ensuring thorough consideration of all relevant information and potential solutions.
- Recognize that the problem-solving framework can be applied across various contexts, as demonstrated by the diverse examples provided in this post.
- Use the problem-solving framework as a foundation for continuous improvement, as it promotes learning from outcomes and refining strategies to address future challenges effectively.
By incorporating the problem-solving framework into daily practices, individuals and organizations can build a solid foundation for success, driving innovation and growth in a rapidly evolving world.
Commonly asked questions about problem solving framework on the internet
What are the key steps involved in the problem-solving framework?
The problem-solving framework consists of several key steps, including identifying the problem, gathering information, developing potential solutions, evaluating and comparing solutions, selecting the best solution, implementing the solution, and reviewing and learning from the results.
How can problem-solving skills benefit individuals and organizations?
Problem-solving skills are critical in both personal and professional settings, enabling individuals and organizations to effectively address complex challenges, make informed decisions, and adapt to changing circumstances, ultimately leading to success and growth.
Why is a structured approach to problem-solving important?
A structured approach to problem solving, such as a problem-solving framework, ensures a systematic and thorough analysis of the problem at hand. It helps to consider all relevant information and possible solutions, leading to more effective decision making and better results.
In what diverse contexts can the problem-solving framework be applied?
The problem-solving framework can be applied in a variety of contexts, including business, customer service, team collaboration and communication, and environmental challenges. Its versatility and adaptability make it a valuable tool for addressing a wide range of problems.
How can the problem-solving framework contribute to continuous improvement?
The problem-solving framework promotes learning from results and refinement of strategies so that individuals and organizations can continuously improve their processes and decision-making capabilities. By reviewing and learning from the results, they can better address future challenges and adapt to changing circumstances.
What are some examples of successful problem-solving framework implementation?
Some examples of successful implementation of a problem-solving framework include increasing a company’s revenue, improving customer service, improving team collaboration and communication, and addressing environmental issues.
How can the problem-solving framework enhance critical thinking skills?
By following the steps outlined in the problem-solving framework, individuals and organizations can improve their critical thinking skills by encouraging systematic analysis, evaluation of alternatives, and rational decision making.
Why is it essential to gather information and generate potential solutions during the problem-solving process?
Gathering information and developing potential solutions are critical steps in the problem-solving process because they help identify the root cause of the problem and explore different options for solving it. This ensures a comprehensive understanding of the problem and increases the likelihood that the most effective solution will be selected.
How can the problem-solving framework help in selecting the best solution for a particular problem?
The problem-solving framework guides individuals and organizations through a structured process of evaluating and comparing potential solutions, taking into account various factors such as feasibility, effectiveness, and potential side effects. This helps in selecting the most appropriate solution for the problem at hand.
What role does reviewing and learning from the outcome play in the problem-solving framework?
Reviewing and learning from results is an essential part of the problem-solving framework, as it allows individuals and organizations to evaluate the effectiveness of their chosen solution and refine their strategies for future challenges. This promotes continuous improvement and adaptation to change.
Udit Goenka
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- Published: 06 September 2024
Creating Edinburgh: diffracting interdisciplinary learning and teaching in the contemporary city
- Clare Cullen 1 ,
- David Jay 2 ,
- David Overend ORCID: orcid.org/0000-0003-4411-3077 1 &
- M. Winter 1 , 3
Humanities and Social Sciences Communications volume 11 , Article number: 1151 ( 2024 ) Cite this article
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This article follows an experimental interdisciplinary undergraduate course in the busy, unpredictable space of the contemporary city. It locates practice-based research of interdisciplinary higher education in a dynamic learning environment, which is comprised of unpredictable connections between disciplinary perspectives. Following Karen Barad, the aim is to diffract interdisciplinary higher education in order to recognise and work with a multiplicity of meanings and experiences. This article explores an alternative to the dominant model of challenge-based learning in the interdisciplinary classroom. Creating Edinburgh: The Interdisciplinary City is an undergraduate elective offered by Edinburgh Futures Institute at the University of Edinburgh. It provides students with opportunities to explore the city of Edinburgh in small groups of students from a wide range of degree programmes. Groups are invited to engage with a selection of themed fieldwork topics throughout the 11 weeks of the course, visiting specific sites and responding to a series of tasks and questions. These include themes such as Sustainability, Decolonisation and Wildness, which are presented as interdisciplinary field topics to explore rather than problems to solve. This article develops a research methodology that sets out to travel with students as they navigate their way through the city during their weekly field trips. Combining first-hand autoethnographic accounts with walking interviews, it offers an insight into interdisciplinary learning and teaching in the expanded field of the contemporary city. Conceiving urban space as an assemblage of digital and non-digital objects, events and activities, members of the research team accompany students during their fieldwork, equipped with audio recorders, cameras and notebooks. The documents of these research journeys are then diffracted within a new materialist framework. The article concludes with questions and prompts for working with the agency and the affordances of a field-based education practice.
This article sets out to diffract interdisciplinary learning and teaching by (re)locating it within a busy urban centre. For Karen Barad, diffraction is ‘not only a lively affair but one that troubles dichotomies, including some of the most sedimented and stabilized/stabilizing binaries’ ( 2014 , p. 68). The troubling of binaries is at the heart of our project, but this is approached in a lively way by entangling a set of ideas, concepts, experiences and voices, which we then reconfigure in new, generative and agential formations. The aim is to demonstrate how the practice of interdisciplinary education can emerge through the destabilisation of established methods and knowledge practices in such a way as to open up new affordances and potentialities. There is an empirical basis to this research: a range of data from interviews with staff and students; self-recorded field observations; photographs and drawings; and autoethnographic records from the researchers. All of these provide valuable insights into the situated practices of interdisciplinary learning and teaching. However, the article takes an experimental approach by ‘re-turning’ this information and documentation (Barad, 2014 , p. 168). We present an open text—an assemblage—that attempts to re-turn us to the fragmented, layered and complex learning environment of the contemporary city. As we go on to discuss, the method of diffraction offers a way of deferring synthesis of the learning experience, producing difference rather than practicing reflective or interpretive closures. This has been taken up as a productive methodology for education studies (Bozalek and Zembylas, 2018 ; Murris, 2022 ; Spector, 2015 ). This article aims to contextualise and practise diffraction as an emergent model for interdisciplinary learning and teaching.
Dominant models of interdisciplinarity learning and teaching are frequently directed towards meeting challenges, finding solutions and problem-solving (Gallagher and Savage, 2020 , 2022 ; Leijon et al., 2022 ). While the modern university has embraced this version of interdisciplinarity—which has not been without valuable outcomes and progressive practices—there is a risk of utilitarian and reductive applications of learning when interdisciplinary collaboration is driven by challenges, at the expense of open, exploratory processes. We are here concerned with a different possibility. What if interdisciplinary learning and teaching could diffract models and patterns, turning them over and over again (re-turning) in order to generate multiplicities and differences, which are themselves open to new configurations and generative of unpredictable experiences and novel knowledge practices? Rather than approaching interdisciplinarity as the convergence of disciplinary approaches directed towards a particular challenge or problem, diffraction offers an alternative model for interdisciplinary practice, which cuts together-apart , establishing an ‘an infinity of moments-places-matterings, a superposition/entanglement, never closed, never finished’ (Barad, 2014 , p. 169). The city offers a particularly appropriate site for this new materialist experiment to play out.
This article focuses on an undergraduate elective course at Edinburgh Futures Institute (EFI), a centre for interdisciplinary study at the University of Edinburgh. Footnote 1 ‘Creating Edinburgh: The Interdisciplinary City’ invites students to conduct weekly fieldwork in the local urban environment, responding to a series of prompts and questions as they visit specific sites in the city. Footnote 2 For each of these field trips, students select a topic from a menu of options, each of which provides a range of resources, including introductory pre-recorded lectures, reading recommendations and suggested tasks and activities. Topics have included Sustainable Edinburgh, Decolonising Edinburgh, Wild Edinburgh and Performing Edinburgh, which are presented as field topics to explore rather than problems to solve. Each field topic includes a site visit and a planned route, which students follow in small groups of 3–4, unaccompanied by a tutor. For Decolonising Edinburgh, students visit the controversial Melville Monument in St Andrew Square and take a Black history tour of the city’s ‘Royal Mile’, which is also available online as a podcast. Tasks for this week invite students to consider recontextualisation of colonial urban histories, architectural traces of Black histories, and possible futures in contemporary Edinburgh. One of the key tasks is to ‘plan a project at any of these locations, which could include a performance, an artwork, a website, or an app’. Each of the field topics in the course is designed to prompt critical, questioning engagements with the city, but they are also focused on creative responses, exploratory journeys and speculative futures.
Creating Edinburgh is a popular elective course and enrolment is capped at 80 students, but the model could easily be scaled up due to its use of online resources and self-led fieldwork. A wide range of ‘home’ disciplines are represented, with students in the 2023/24 cohort joining the course from Arabic with Islamic and Middle Eastern Studies, Archaeology, Chemistry, Economics, English Language, Fashion, Film and Television, Fine Art, Geography, Geology, History of Art, International Relations, Italian, Linguistics, Psychology, Scottish Literature, Sociology and Sport Management, along with students from EFI’s undergraduate degree programme in Interdisciplinary Futures, on the first year of its delivery. A high proportion of international visiting students selected the course, with the 2022/23 cohort at the highest percentage of 74% visiting students. These students were studying at Edinburgh for a single semester or, most commonly, a full exchange year from institutions in a wide range of countries, predominantly in North America, Europe and Asia.
The cohort is divided into six tutorial groups and then organised into smaller, multi-disciplinary ‘field groups’, meaning there are over 20 groups comprised of students from different schools, conducting fieldwork on their selected topics in different orders, each week. The orientation week at the beginning of the course emphasises health and safety procedures, as well as accessibility issues. A detailed risk assessment is completed, which ensures a combination of physical and digital activities, allowing for shifting models of learning that might be necessary for those with limited mobility, for example. On each week of the course, following their fieldwork, students return to the academy for a 2-hour seminar led by a postgraduate tutor. These seminars include sharing documentation from the field trips—often in the form of videos and photographs—and students reporting on their insights and experiences.
As one of the co-authors has noted elsewhere, in collaboration with the teaching team of one of EFI’s postgraduate courses, Cities as Creative Sites, ‘[w]e see the act of return as a vital part of the learning journey […] we do not simply ask our students to head out into the city and respond to the prompts that we set them, we also create spaces for reflection, co-creation and curation of the findings that are generated’ (Overend et al., 2024 ). During the weekly seminars, interdisciplinary learning is encouraged, recognised and reflected upon. The learning outcomes for Creating Edinburgh include the ability to ‘evaluate and utilise knowledge and skills from a variety of disciplines to understand the city of Edinburgh’ and to ‘demonstrate an awareness of the complexities involved in interdisciplinary learning and collaboration to navigate any challenges that arise’. The weekly seminar is essential to these outcomes, as tutors work with the students to critically engage with documents and accounts of the fieldwork, building an understanding of the city as an inherently interdisciplinary space of learning, and, following the geographer Doreen Massey, engaging with the urban environment as a ‘simultaneity of stories-so-far’ ( 2005 , p. 9).
Importantly, the model of interdisciplinary education that is practised by Creating Edinburgh offers an alternative to the challenge-led learning of the interdisciplinary undergraduate degree programme at EFI. Interdisciplinary Futures aligns with the version of interdisciplinarity identified by Richard L. Wallace and Susan G. Clark, who argue that ‘[i]nterdisciplinarity is inherently “problem-oriented”—that is, its theory and methods are designed to address the complexity of social and environmental problems’ ( 2017 , p. 222). This model of interdisciplinarity often involves collaboration between academia and external stakeholders, including partnerships with industry and international organisations (Klein, 2017 ). This wider movement towards problem-based interdisciplinarity has influenced research and education in the modern university. For example, the University of Edinburgh positions it as a key strand in its curriculum transformation project:
‘Challenge Courses’ will enable students to explore issues/problems that are unbounded, complex and resist straightforward definition. [… T]hese courses will provide an opportunity for students to explore and build understanding of themes and topics across disciplines. (University of Edinburgh, 2023 )
This direct alignment of issues/problems with working across disciplinary boundaries is in line with Wallace and Clark’s position. However, it is important to remember that such dominant models are not the only way of practicing interdisciplinarity. While a range of positive learning outcomes can arise from ‘challenge-led’ and ‘problem-oriented’ approaches, there is a risk of a functional and solution-focussed experience, in which learning at its best can only interpret and reflect (on) existing dichotomies; at its worst could be co-opted into neoliberal agendas at the expense of creativity, open exploration and unpredictability.
To conceive of the educational experiences of Creating Edinburgh as interdisciplinary is to challenge utilitarian or instrumental models, arguing for a re-turning of concepts and practices in order to expand the field. The key aim of this article is to share a way of designing and delivering interdisciplinary learning experiences that move from reflection and interpretation toward diffraction. The following section reviews perspectives on challenge-led, interdisciplinary education. Barad’s key terms are then mobilised, demonstrating how the concept and method of diffraction can be applied to interdisciplinary education. Next, the multi-method research project is introduced, which frequently displaces students, teachers and researchers alike into the urban environment beyond the academy. This contextualises an experimental section, which attempts to practise diffraction on the pages of this article, re-turning our fragmented experiences of designing, teaching and researching Creating Edinburgh into an open text, which uses multiple voices and fragmented narrative, with the aim of ‘ producing difference’ rather than documenting and reflecting existing conditions (Bozalek and Zembylas, 2018 , p. 54, original italics). The article concludes with a series of questions, prompting tangible recommendations, which re-turn us from the city to the academy, where more of these diffractive experiments might play out.
Interdisciplinarity, problem-solving and challenge-based learning
It is frequently noted in the literature on interdisciplinarity that the term has taken on multiple definitions since its inception in the 20th century, giving rise to an array of related concepts and shifting terminology (Klein, 2017 ; Bammer, 2013 ; Lattuca, 2001 ). For some commentators, this complexity is a crucial strength, because it allows the conceptualisation of interdisciplinarity to resist the confinement of traditional disciplines and methods (Moran, 2010 ). The many variants, debates and slippages in defining interdisciplinarity are beyond the scope of this review, but a helpful point of departure is provided by Klein ( 2015 ), who traces the term to its first documented appearance, which was specifically in ‘problem-oriented’ research. It is striking that the notion of interdisciplinarity was aligned, even from its coinage, with the concept of problem-solving. This remains evident in recent accounts of interdisciplinary scholarship, for example, in the notion of ‘problem-based research’ which may respond to the needs of society (Repko and Szostak, 2020 ), or environmental challenges (Wallace and Clark, 2017 ).
In the development of transdisciplinary scholarship, the concept of problem-solving has been considered a ‘trendline’ in its own right (Klein, 2017 ); it is also evident in the related notions of challenge- or mission-oriented research (Vienni-Baptista et al., 2023 ), and in ‘wicked problems’ as a focus for transdisciplinary endeavour (Pohl et al., 2017 ). This, in turn, has influenced models of interdisciplinary pedagogies, particularly in higher education, which has seen a shift towards interdisciplinary learning and teaching, both nationally and internationally (Lyall et al., 2015 ). The use of problem-solving has been cited as a key benefit of interdisciplinary teaching (van der Waldt, 2014 ); it has been proposed as a locus for the development of integrated knowledge, teamwork and self-reflection (Haynes et al., 2010 ). The ability to respond to global challenges through interdisciplinary learning is also seen as beneficial for students’ communicative competence during and after their university studies (Woods, 2007 ).
DeZure ( 2017 ) includes problem-solving in a taxonomy of interdisciplinary pedagogies in higher education, showing how this approach to interdisciplinarity allows universities to collaborate with external stakeholders beyond academia, including government, industry and other professional organisations. The related concept of challenge-based learning is also regarded as effective in developing a range of creative and collaborative skills relevant to a range of societal and planetary issues in multidisciplinary learning contexts (Gallagher and Savage, 2020 ; Leijon et al., 2022 ).
However, problem-solving approaches may rely on an instrumental model of interdisciplinarity, where interdisciplinary research serves the needs of particular disciplines or stakeholders (Klein, 2017 ; Lattuca, 2001 ). Various critical responses to such approaches have been proposed. These include the view that problem-solving is reductive in pursuing instrumental, clear-cut solutions to complex and troubling problems (Klein, 2021 ); it may also be perceived to be insufficiently theorised (Vilsmaier et al., 2023 ). In the humanities, philosophers Derrida and Lyotard, among others, expressed scepticism about the use of interdisciplinarity to solve problems that serve the needs of mechanised production (Klein, 2015 ). In addition, Vienni-Baptista, Fletcher and Lyall observe that problem-solving model may be less welcoming to researchers from the arts, humanities and social sciences, pointing to ‘a lack of status of AHSS disciplines in relation to STEMM [science, technology, engineering, mathematics and medicine] contributions’ ( 2023 , p. 5).
It is clear, then, that despite the well-documented effectiveness of a problem-solving model, an alternative conceptualisation of interdisciplinary learning and teaching is possible. Klein anticipates this in a discussion of critical responses, including feminist theorisations from the humanities, which question ‘resolution in theory and in practice as well as institutional forms’ ( 2021 , p. 45), including the critical concept of diffraction in science, explored from various perspectives. The concept of diffraction is a central element of the theory of agential realism, as proposed by Barad ( 2007 ), which is adopted as the theoretical framework for this paper. The following section outlines some of the key concepts in agential realism, before exploring examples of its application in the practice of learning and teaching in universities.
Karen Barad’s agential realism
In a bold re-working of classical philosophy and science, Barad calls into question established notions of ontology, epistemology and ethics, proposing a feminist ‘ethico-onto-epistemology’ (Barad, 2007 ) which recognises the entanglement of phenomena. Working across disciplines, Barad reads insights from poststructuralist theory in the humanities—notably Butler’s notion of performativity (see Barad, 2003 ) and Foucault’s critique of representationalism—through the philosophical and scientific writings of physicist Niels Bohr. This allows for a theoretical model which encompasses the ‘discursive’ and the ‘material’ (Barad, 2007 , pp. 234–245). Further, Barad proposes ‘respectful engagements with different disciplinary practices’ (93) as inherent to agential realism.
Agential realism shares with other new materialisms (see Dolphijn and van der Tuin, 2012 ; Coole and Frost, 2010 ) a critique of traditional dualist ontology, notably the traditional mind-body dualism of classical Cartesian philosophy. It also critiques the representational optics of Newtonian physics, where the observing subject is perceived as detached from the object of observation. In agential realism, by contrast, the experimental apparatus is always entangled in the phenomenon of the experiment, not separate from it (Barad, 2007 ). In this way, agential realism questions not only binaries: material–discursive ; philosophy–physics ; subject–object ; together–apart ; it also troubles any notion of defined entities or ‘things’ that exist prior to their relations. In this theoretical framework, individual entities do not pre-exist separately but rather come into being through what Barad terms intra-actions , as opposed to interactions (the latter term implies the pre-existence of defined entities).
Rather than traditional methodological approaches, which depend on detached observation and representation, Barad proposes a ‘diffractive methodology’. The notion of diffraction had previously been explored by feminist theorists Trinh Minh-ha and Donna Haraway, in the development of a ‘more critical and difference–attentive mode of consciousness and thought’ (Geerts and Van der Tuin, 2021 , p. 173). Barad develops the concept further, drawing on notions of diffraction from physics, as noted in wave patterns in water, or electrons passed through diffraction gratings. Crucially, Barad’s proposed methodology does not depend on representation, nor on reflection; rather, diffraction involves ‘reading insights through one another’ ( 2007 , p. 71) in order to trace patterns of difference and understand how these differences matter.
Central to Barad’s diffraction is the notion of ‘re-turning’, which they distinguish from the traditional concept of returning to one fixed point in the past: ‘re-turning as in turning it over and over again—iterative intra-acting, re-diffracting, diffracting anew, in the making of new temporalities’ ( 2014 , p. 168). Thus, a diffractive approach takes account of ever-evolving complexity: in ‘re-turning’, patterns of difference occur in an iterative, ongoing process across time and space, without clear-cut separations or fixed endings. In this way, agential realism can be aligned with discourses of inter- and trans-disciplinarity which attempt to go beyond ‘assumptions about how systems operate and the expectation science delivers final answers with certainty’ (Klein, 2021 , p. 45), making it an apt framework for this article.
Despite significant challenges in conceptualising new materialist enquiry, perhaps most strikingly articulated by St Pierre ( 2021 ), experimentation with agential realism is increasingly evident in practice-based research across disciplines. It has been proposed as a framework for understanding education, including higher education (Murris, 2022 ; Bozalek and Zembylas, 2017 ; Bayley and Chan, 2023 ). Dunk ( 2020 ) provides an account of the emerging use of agential realism in interdisciplinary social sciences; in practice-based settings, agential realism has already been adopted as a framework for understanding outdoor learning in ecology studies (Sanders and Davies, 2023 ), environmental education (Brown et al., 2020 ) and creative learning spaces (Sheridan et al., 2020 ).
As well as this, diffraction and diffractive methods are increasingly being put to work in a variety of forms. Van der Tuin ( 2016 ) sets out a theoretical basis for reading diffractively; Murris and Bozalek ( 2019 ) propose an approach to diffractive readings in which one text is read ‘through’ another. Various approaches to the diffractive analysis of textual data have been proposed (Taguchi, 2012 ; Mazzei, 2014 ) as well as the use of ‘diffractive cuts’ in mixed-methods research (Uprichard and Dawney, 2019 ). Chappell et al. ( 2019 ) use a diffractive methodology to investigate entanglements between arts and science disciplines in interdisciplinary education.
Barad’s notion of intra-action has also been taken up in the education research practice, specifically in theorisations of the ‘intra-view’, as opposed to traditional qualitative interviews. Kuntz and Presnall ( 2012 ) explore material-discursive practices in walking ‘intraviews’, which represent ‘more than simply an exchange of words’ but afford an encounter with ‘assemblage of multiple historical, present and future encounters’ (p. 736). Further examples of new materialist approaches to interviewing include the consideration of material entanglements in transformative interviews in university settings (Marn and Wolgemuth, 2017 ); the use of visual methods within a new materialist interview (Warfield, 2017 ) and a reframing of the active role played by recording devices in the interview process (Nordstrom, 2015 ). These approaches have informed the design of our own diffractive methodology.
Methodology
The data that inform this article were collected through multiple methods over the first three years that Creating Edinburgh was offered, between 2021 and 2023. One of the co-authors, who also has the role of course organiser, led the course design process during the lockdown years of 2020 and 2021 and curated the resources provided to students to guide them in their fieldwork, which we take up as part of the empirical assemblage. Autoethnographic texts produced by another of the co-authors were a result of her role as a teaching assistant on the inaugural version of the course. Beyond direct pedagogical involvement in the course, we also collected additional data in the 2023/24 iteration. This included the facilitation of creative activity at the start of the course (Fig. 1 ), students’ self-recording of their fieldwork, tutorial observations, a walking intraview with a student, and a walking autoethnographic intraview (Kuntz and Presnall, 2012 ).
![analytics methodology and problem solving framework figure 1](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-024-03566-5/MediaObjects/41599_2024_3566_Fig1_HTML.png)
This work is openly licensed via CC BY 4.0. Permission granted for reuse © 2024.
In preparing our assemblage, we utilised layout/format and content/narrative, in Barad’s words, to ‘manifest the extraordinary liveliness of the world’ that is central to the Creating Edinburgh experience ( 2007 , p. 91). The layout/form engages the reader as an embodied engager of the text, where the reader’s eyes track its unorthodox shape, mimicking a kind of ‘looking around’ one might engage in when exploring urban spaces. In this way, we bring to the two-dimensional page/screen spacetimemattering’s multidimensional entanglements and disrupt the linearity of chronological time. The content/narrative places the reader in the position of an unattached onlooker, a researcher, a student and a teaching assistant on the course, drawing together various texts produced by the authors—some as transcribed from recordings made in the field, some relocated from related documents written at different times and collated by the authors as part of the research process. These include PhD manuscripts, course feedback and poetic responses to the research process, which are folded into each other, producing an openness and expansiveness, from which innumerable threads can be pulled.
As with some of Barad’s experimental diffractive writing, the section that follows avoids ‘a straight narrative, a linear unfolding of a particular storyline’, instead ‘experiment[ing] with montage and fragmentary writing, diffractively reading insights through one another, allowing the reader to explore various crystalline structures that solidify, if only momentarily in the breaking of continuity’ (Barad, 2017 , p. 22). Taking our cue from feminist theory, the following section might be understood as a process of composting, which Barad adopts in their project of diffraction:
We might imagine re-turning as a multiplicity of processes, such as the kinds earthworms revel in while helping to make compost or otherwise being busy at work and at play: turning the soil over and over— ingesting and excreting it, tunnelling through it, burrowing, all means of aerating the soil, allowing oxygen in, opening it up and breathing new life into it. (Barad, 2014 , p. 168)
The creation of the assemblage is itself a diffractive re-turning practice for us and a provocation to participate in re-turning as readers because, as Barad emphasises, diffractive practices are not representational but performative, embodied and dynamic. Having collected these experiences and insights, and developed an understanding of diffraction in the context of interdisciplinary learning and teaching, it seemed important to find a way to re-turn the texts that were generated. Our approach follows Uprichard and Dawney in ‘welcoming the emergence of disjunctures, lacunae, difference and diversion as a means of troubling the research case as a bounded, isolated unit’ ( 2019 , p. 27). This allows us to practise diffraction as part of the writing process. An experiment in diffractive pedagogy is re-turned through the following section, which cuts diverse voices together-apart, representing a dynamic process of making words matter.
Diffracting Edinburgh
The diffractive experiment in the following section presents the reader with an assemblage of fragments, following Barad’s reading of Walter Benjamin’s Arcades Project : 'a collection of fragments, including reflections of various lengths and individual passages from various texts copied down as individual notes and assembled in constellations suggesting multiple reverberations or diffraction patternings among the fragments’ (Barad, 2017 , p. 38). As Barad explains, just as there is no singular interpretation of quantum theory, there is no singular interpretation of these fragments of text: time is dis/continuous, resisting any notion of ‘linear narrative’. Rather, the reader is invited to become entangled with the experimental assemblage by reading the fragments ‘through’ one another, in a diffractive reading process (Barad, 2007 ). We acknowledge that leaping from fragment to fragment may be unsettling, even destabilising for the reader, but this unsettling and re-turning of thought is precisely what we are striving to achieve in this diffractive experiment; we hope that the reader will be open to the experience.
The fragments in our diffractive experiment are drawn from diverse intra-actions that occurred in the process of learning and teaching on Creating Edinburgh, including creative responses written by students during the course, photographs of spaces in Edinburgh, which are the focus of course assignments, excerpts from the researchers’ reflections during the research process. Following Barad’s example, we have provided explanatory footnotes, which give context for each fragment.
If you’re reading this, you’re likely facing the screen Or page Head on Footnote 3
But if you were standing
On Princes Street in Edinburgh
At the bottom of Lothian Road
Facing East
You’d find the high street on your left
And the gardens on your right
With Edinburgh Castle looming above
There are tourists taking photos
And a group of students exploring the grounds together
You emerge from the bowels Of Waverley, Adjusting to the brilliant blue, The crisp autumnal air, The majesty of Castle Rock, The dynamism of Edinburgh Laid out before you In all her splendour.
On Waverley Bridge,
Behind you,
Laughter erupts sporadically,
Punctuating the comings and goings…
To your right,
A circle of students,
Bundled up against the October chill.
A researcher among them.
Beyond, the commercial aorta:
Princes Street abuzz, tram bells, busses, totes.
To your left,
A couple poses,
One click to capture,
To immortalise the moment.
One tap to transport,
To digitise a vista.
But for you,
Immersed as you are,
This is no two-dimensional plane.
This is your reality.
Stimulating activity in the same motor-neurological regions as physically enacted movements (Barsalou et al., 2005 ), reading is an embodied experience that invites not only a visual representation, but a broad range of multimodal responses, including any, or a combination of, the five senses, interoceptive reactions, proprioceptive responses and kinaesthetic sensations (Rokotnitz, 2017 ). In reading the multimodal imagery contained in these fragments the past experiences of research participants, course tutors and researchers with(in) the city ‘flash up’, diffracting spacetimes and ‘de(con)struct[ing]… the continuum of history… [to bring] the energetics of the past into the present and vice versa’ (Barad, 2017 , p. 23). The reader is as phenomenologically immersed and materially connected—as entangled—as the participants and researchers.
You walk through the city, across Princes Street, to the New Town.
The infamous statue of Henry Dundas—1st Viscount Melville—stands 150 feet above St Andrew Square. Between 1791 and 1805, Dundas was Home Secretary, Minister for War and Colonies and First Lord of the Admiralty. He is also argued to have significantly delayed the abolition of slavery.
To begin the Decolonising Edinburgh field week, students are invited to watch a film by Sir Geoff Palmer, interviewed for Edinburgh Futures Institute ( 2020 ). Palmer introduces this controversial figure and reflects on strategies for decolonising public monuments. Palmer is Scotland’s first Black professor. He is now Professor Emeritus in the School of Life Sciences at Heriot-Watt University in Edinburgh.
As a result of campaigning by Palmer and others, a new plaque has now been installed at the site. Students visit the monument together to read the new inscription and to critically reflect on the text (Fig. 2 ).
![analytics methodology and problem solving framework figure 2](https://media.springernature.com/lw685/springer-static/image/art%3A10.1057%2Fs41599-024-03566-5/MediaObjects/41599_2024_3566_Fig2_HTML.png)
The plaque was replaced in March 2024 after its removal by a group led by a descendant of Henry Dundas (BBC, 2024 ). This work is openly licensed via CC BY 4.0. Image by David Overend © 2024.
Does the plaque go far enough? Do you feel that the statue should be removed? If so, should something else be put in its place?
The Researcher Footnote 4
You arrive.
Waverly North Bridge.
Unable to discern the students
From other passers-by.
An approach:
Introductions.
Research Ethics.
Expectations.
Invitations.
Phones out.
Recorders on.
You follow,
Neither a part
You observe.
You reflect.
You diffr act ,
And in doing so
You are irrevocably
Altering the experience
Fundamentally.
What does it mean
To be here ?
To inhabit,
And for a city
To be of you ?
Questions structure the discussion in the seminar room. Tutors facilitate the exchange of ideas between students, drawing out tensions and contradictions, prompting intra-action. The ‘tools’ that tutors use might be understood in Baradian terms, although they have yet to be framed explicitly in this way. Students share the documents and outputs of their fieldwork, which are then explored by students in other groups. Questions are invited to turn experiences over and over, troubling binaries and opening up reflections into new, generative responses. This includes the creation of new field topics, which are submitted for assessment as digital portfolios comprising images, maps, tasks and questions. As Karen Spector argues, ‘[i]f nothing new that matters is produced, then diffraction hasn’t occurred’ ( 2015 , p. 449). With a topic as contested and emotive as decolonising monuments, diffraction might be utilised more explicitly as a way to read meanings through each other without the pressure to move toward resolution. The ethics of diffractive pedagogy are informed by feminist theorists such as Donna Haraway, who argues that ‘it matters what stories make worlds, what worlds make stories’ ( 2016 , p. 12). Creating Edinburgh emphasises the ways in which learning and teaching matter to the city and its continual creation through stories, journeys and intra-actions.
If you had been in this exact spot some years ago, a personal protective equipment (PPE) mask may have obstructed your peripheral view.
Students on the inaugural version of Creating Edinburgh sat in cold seminar rooms (windows open for air circulation even in the winter). Pandemic Edinburgh has never been a popular field topic. Footnote 5
‘I’ was teaching two seminars that year
Each week, I greeted students as they filtered in, sitting in their groups
“How was your fieldwork?”
“Where did you go?”
“What did you see?” Footnote 6
Years later Creating Edinburgh found its way into my PhD Manuscript:
The figure of the witch has been a constant companion to my PhD process. In my final semester of teaching at the University of Edinburgh, I was working on a brand-new course called ‘Creating Edinburgh’. Footnote 7 The course design loosely mimics a ‘choose your own adventure’ novel, wherein the students opt for certain topics from a list of offerings at the beginning of the semester. The course is an interdisciplinary approach to the city of Edinburgh itself and engages with themes like, ‘Decolonising Edinburgh’, ‘Digital Edinburgh’, ‘Literary Edinburgh’, ‘Deep Time Edinburgh’, etc. The main assessment for the course is for students to create their own theme to be added to the cache for future cohorts. One of my groups chose to pursue Witchcraft in Edinburgh, and it is to them I owe much of my knowledge about witch hunts in Scotland. Thank you, Eleanor, Aimee, Sadie and Andrew. It is because of them that I learned that the Mercat Cross, located in the market centre of Medieval Edinburgh, served as an execution site for accused witches. According to a plaque at The Witches Well, a memorial to those accused of witchcraft in Scotland between 1563 and 1736, hundreds of witches were publicly executed during this time. The plaque was placed in 1912 and states
This fountain, erected by John Duncan, R.S.A., is near the site on which many witches were burned at the stake. The wicked head and serene head signify that some used their exceptional knowledge for evil purposes while others were misunderstood and wished their kind nothing but good. The serpent has the dual significance of evil and wisdom.
Witches then, were condemned regardless of which direction they aimed their powers; ‘the “good witch”, who made sorcery her career, was also punished, often more severely’ (Federici, 2004 , p. 200). This lack of discrimination concerning the morality of witches is perplexing; if witches were not hunted and killed for their wickedness, what were they persecuted for?
The situatedness of this knowledge struck me, as I had been working with the text, Caliban and the Witch: Women, The Body and Primitive Accumulation for years and had not investigated the history of witch trials in Edinburgh, the city in which I lived and studied in.
Witchcraft Edinburgh Footnote 8 Artistic Edinburgh Literary Edinburgh Music Edinburgh Deadinburgh Eatinburgh Haunted Edinburgh Legendary Edinburgh Queer Edinburgh
For their final assessment, students work in groups to create their own field topic. These are then made available as an open educational resource on the University’s website so that they can be accessed publicly and used by students in subsequent years of the course as a re-turning of practice. Footnote 9 The course avoids closing down the experience into interpretive or analytical assignments. Rather, new experiences are generated, new paths are followed and new questions are raised. The questions that students have asked through this assessment task exemplify the kind of diffractive approach that we are advocating in this article. Barad’s argument about the inseparability of entangled phenomena tells us that ‘separability is not taken for granted and this means that all phenomena—all the entanglements—are open to analysis and questioning’ (Barad and Gandorfer, 2021 , p. 51). Applying this principle to an educational experience in the city suggests that the questions that are asked (which include the prior questions that structure the fieldwork on the course) matter to Edinburgh. This is because active questioning ensures that multiple components are kept open, malleable and subject to change. This is the meaning of the course subtitle, ‘the interdisciplinary city’.
Does the surrounding area reflect what we have learned about Edinburgh and its level of safety for the LGBTQ community?
How are educational institutions and their buildings important for a city other than just classrooms?
How does food serve as a social tool in this community?
Why are ghost stories ingrained in Edinburgh, and how might they add to the culture of the city?
Because diffraction is an ongoing process, these questions are not only about a specific encounter with the city of Edinburgh; they also have the potential to shape ways of being, thinking and relating in other contexts.
The majority of students on the course are exchange or international visiting students Footnote 10
After weeks of oscillating between seminar room and fieldwork, they would return to their country of study or home
What did they bring with them? What did they leave behind?
Were they changed by Edinburgh? Is Edinburgh changed by them?
Perhaps they did not return. They re-turned.
There are things about which
We educators and researchers
Have no knowledge,
No control.
What was happening
In the ‘margins’ of the students’
Living as they were
Beyond the parameters of
This ‘Creating Edinburgh’ .
Learning as they were
Beyond the boundaries of
This ‘Education’.
How to divest oneself
Of the assumed responsibility
Of the learning and knowing
Vital contemplation.
You peer down a nearby close (those narrow, steep alleyways branching off the Royal Mile)
And you feel the wind rush through it, over you
Its particles and physics.
Diffraction (deferring conclusion)
The particular form of interdisciplinarity that Creating Edinburgh develops, which is also practised through the fragmented text in this article, can be understood as a Baradian deferral of synthesis. This is counter to dominant models of interdisciplinarity. Footnote 11 For example, Newell’s influential theory claims a commonly accepted tenet that ‘interdisciplinary study draws on more than one discipline’s perspective to synthesise a more comprehensive understanding’ ( 2001 , p. 2). Noting the synonymity of synthesis and integration, Repko and Szostak also state that ‘comprehensive understanding’ is the ultimate aim for interdisciplinarity, which relies on integration as ‘the cognitive process of critically evaluating disciplinary insights and creating common ground among them’ ( 2020 , p. 223). There are countless examples of this well-established argument: interdisciplinary learning and teaching must involve the synthesis of two or more disciplines, in order to comprehensively understand a topic. In this view, it follows that any activity, object of study, or educational model that works against synthesis and integration cannot be interdisciplinary. Barad questions this imperative for integration, troubling the idea of ‘a synthesis or a joining of the Humanities and the Sciences as if they were always already separate rather than always already entangled’ (interviewed by Dolphijn and van der Tuin, 2012 , p. 51).
In a complex, dynamic learning environment such as the contemporary city, the pursuit of ‘comprehensive understanding’ and ‘common ground’ is problematised. If urban space—indeed, any space—can be understood as an unresolvable set of materials, processes and activities, that are perpetually emergent (Massey, 2005 ), then one way of approaching education in such spaces is to enter into entanglements with these in-process assemblages, rather than attempting to establish commonalities in order to know them comprehensively. This recognition of the inseparability of the researcher from the experiment—the student from the city—is fundamental to agential realism.
The walking artist Phil Smith refers to a ‘Masseyspace’ of trajectories, which is encountered through a ‘suspended dialectic’ in which ‘the parts never actually reach a synthesis, for this is always (purposefully) delayed’ ( 2010 , p. 177). This is enacted throughout Creating Edinburgh, as the objective is not integration of the various field topics, which remain distinct but matter to each other in various ways. This is suspended through the continual re-turn to the city with different questions and concerns, with the aim of diffracting differences through processes of questioning, patterning, cutting and intra-acting. Diffraction patterns emerge through this methodology, which sometimes evolves through entanglements that reach temporally and spatially beyond the interdisciplinary classroom.
The liveliness of the ‘composting’ process is maintained in this article, which centres around a multi-authored assemblage of accounts, impressions, reflections and documents from Creating Edinburgh. We have re-turned these fragments into a playful, exploratory text that creates openings for further practice and future diffractions that matter in different ways. By working with Baradian theory, we are accepting the premise that ‘mattering is about the (contingent and temporary) becoming-determinate (and becoming-indeterminate) of matter and meaning, without fixity, without closure’ ( 2010 , p. 254). In this conclusion, we, therefore, remain consciously inconclusive. It is important to avoid folding our experiment in diffraction back into a reflective or interpretive analysis. We are continuing to work with ‘loose ends and missing links’ (Overend et al., 2024 , after Massey, 2005 ). In this final section, we offer a set of prompts, provocations, tools and tasks for re-turning, that will hopefully be of value to other practitioners of interdisciplinary education, who may be inspired to open up and breathe new life into their learning and teaching practices.
In what ways can the city be encountered as a site for interdisciplinary learning and teaching? Take your students or peers into the urban environment and ask them to work with the dynamic, messy, contradictory spaces of the contemporary city. Urban environments are inherently interdisciplinary. They cannot be experienced as discrete, autonomous units of knowledge. Rather, cities are places where ideas, experiences, cultures and worldviews come into contact—merging or conflicting. New ways of being and knowing the world are created. Cities are diffraction machines. To approach the city as a site of interdisciplinary education is to make use of these interstitial affordances. This can be achieved very simply by asking others to follow you out of the door of the university building to recontextualise a learning experience and to look for new resonances and applications. Footnote 12
What are the different ways of working with agential realism, and diffraction, to develop interdisciplinary practice and research? Diffract and diffract your diffractions: Whenever a learning process results in ‘findings’, ‘solutions’, ‘products’ or ‘outputs’, do not be content to leave it there. Barad offers a collection of concepts, methods and practices that invite a continual process of intra-acting with dynamic forces and agencies, such as those that comprise the contemporary city and determine the practice of interdisciplinary learning and teaching. Spending time at the beginning of any educational experience by engaging with these Baradian concepts might prompt new ways of learning and teaching interdisciplinarity. The field topic of Diffracting Edinburgh is yet to exist, but there would be great value in its inclusion in the course.
As educators, can we listen and learn, becoming with our students as they intra-act with each other and their environments? Asking students to report back from their experiences beyond the classroom has been a valuable way to do this, which opens up interdisciplinary education to a process of diffraction, as we re-turn those experiences into something new. The act of return is vital. By re-turning (to) the academy, the learning experiences of the fieldwork can be disseminated, reconfigured and diffracted into new agential assemblages. The aim of a course like Creating Edinburgh is not to solve problems or overcome challenges. Rather, it is to meet the world in all its complexity and to become part of the things that matter. Baradian diffraction offers a way to practise this way of being, within and beyond the modern university.
Data availability
The data generated and analysed during the current study are available from the corresponding author on reasonable request.
In 2018, the University of Edinburgh established the Edinburgh Futures Institute (EFI), ‘a new futures-focused space for learning, research and innovation at the University of Edinburgh’ (efi.ed.ac.uk). EFI initially offered a cross-campus suite of postgraduate courses offered in ‘fusion’ mode between online and physical classrooms, along with a range of pilot courses offered as undergraduate electives across the university. In 2023, EFI moved into the newly refurbished Old Royal Infirmary in the centre of the city as well as launching its inaugural undergraduate programme in Interdisciplinary Futures (efi.ed.ac.uk/study/undergraduate).
Creating Edinburgh: The Interdisciplinary City was designed by one of the co-authors, David Overend. It is available as an Open Education Resource at https://open.ed.ac.uk/category/creating-edinburgh/ .
These fragments were co-created in the spring of 2024 by authors Clare Cullen and M. Winter, each drawing on their own experiences with the city of Edinburgh and the course. The latter fragments specifically refer to Cullen’s walking intraview with students on their field work.
This fragment, written by Clare Cullen, captures her experience accompanying students on their field work in the autumn of 2023. The researcher’s presence-participation in students’ fieldwork with(in) the city was an integral part of our methodology. Weaving an autoethnographic fragment of the researcher’s fieldwork experiences into the tapestry of this entanglement reveals the messy liveliness of diffractive research. It does not obscure the essential administrative, ethical and logistical elements that might bookend a data collection intervention—those research protocols that exist beyond the boundaries of the data and yet fundamentally influence the experiences captured within those recordings. It acknowledges what is gained through researcher presence-participation, and what is sacrificed: a firsthand, embodied understanding of the multisensory phenomena that coalesce to create a city at the cost of influencing the students’ experiences by introducing a factor that would otherwise not have been present.
Barad follows Walter Benjamin in exploring the disruptive potential of ‘a superposition of times—moments from the past—existing in the thick-now of the present moment’ ( 2017 , p. 33). For Barad, this is understood through the notion of temporal diffraction, noting how an electron can co-exist at different temporal, as well as spatial, locations. While the Pandemic Edinburgh topic is not a popular choice (perhaps because this is not a temporal location that students have any desire to re-turn to), Creating Edinburgh was designed and developed during the COVID-19 pandemic. Pandemic time is therefore superimposed in the present moment through a troubling of digital/physical binaries and the traces of the disrupted learning experience that characterised the inaugural year of the course. Deeper time is also superimposed on the present, as students are invited to encounter the city at a geological scale. Searching for evidence of geological processes in the city, students are asked to identify traces in the materials and shapes of the urban landscape.
This section, written by M. Winter, describes their experience teaching on the inaugural version of Creating Edinburgh in 2021.
This is an excerpt from M. Winter’s PhD thesis (Kurchik 2023 ).
A list of topics added to the cache by students over the past few years.
See open.ed.ac.uk/creating-edinburgh-student-oer.
This section was co-created by Clare Cullen and M. Winter, diffracting their varied experiences as researcher and teacher.
While synthesis has been contested as ‘the defining characteristic of interdisciplinary studies’ (Richards, 1996 , p. 114), this has frequently been debated within a utilitarian framework that does not recognise the always already entanglements of disciplinary perspectives, which Barad’s work illuminates.
For a range of prompts to explore the architectural, geographical and performative intra-actions of the city (see Overend et al., 2024 ).
Bammer G (2013) Disciplining interdisciplinarity: integration and implementation sciences for researching complex real-world problems, 1st edn. ANU Press, Canberra
Barad K (2003) Posthumanist performativity: toward an understanding of how matter comes to matter. Signs 28(3):801–831
Article Google Scholar
Barad K (2010) Quantum entanglements and hauntological relations of inheritance: dis/continuities, spacetime enfoldings, and justice-to-come. Derrida Today 3(2):240–268
Article MathSciNet Google Scholar
Barad K (2014) Diffracting diffraction: cutting together-apart. Parallax 20(3):168–187
Barad K, Gandorfer D (2021) Political desirings: yearnings for mattering (,) differently. Theory Event 24(1):14–66
Barad K (2007) Meeting the universe halfway: quantum physics and the entanglement of matter and meaning. Duke University Press, Durham
Barad K (2017) What flashes up: theological–political–scientific fragments. In: Keller C, Rubenstein M-J (eds) Entangled worlds. Fordham University Press, New York, pp. 21–88
Barsalou LW, Wiemer-Hastings K (2005) Situating abstract concepts. In: Diana Pecher D, Zwaan RA (eds) Grounding cognition: the role of perception and action in memory, language, and thinking. Cambridge University Press, New York, pp. 129–163
Bayley A, Chan JJ (eds) (2023) Diffracting new materialisms: emerging methods in artistic research and higher education, 1st edn. Palgrave Macmillan, Cham, Switzerland
BBC (2024) Council installs new slavery plaque at Edinburgh’s Melville Monument. https://www.bbc.co.uk/news/uk-scotland-edinburgh-east-fife-68597359 . Accessed 28 Mar 2024
Bozalek V, Zembylas M (2017) Diffraction or reflection? Sketching the contours of two methodologies in educational research. Int J Qual Stud Educ 30(2):111–127
Bozalek V, Zembylas M (2018) Practicing reflection or diffraction? Implications for research methodologies in education. In: Braidotti R, Bozalek V, Shefer T, Zembylas M (eds) Socially just pedagogies: posthumanist, feminist and materialist perspectives in higher education. Bloomsbury, London, pp. 47–62
Brown SL, Siegel L, Blom SM (2020) Entanglements of matter and meaning: the importance of the philosophy of Karen Barad for environmental education. Aust J Environ Educ 36(3):219–233
Chappell K, Hetherington L, Ruck Keene H, Wren H, Alexopoulos A, Ben-Horin O, Nikolopoulos K, Robberstad J, Sotiriou S, Bogner FX (2019) Dialogue and materiality/embodiment in science|arts creative pedagogy: Their role and manifestation. Think Skills Creat 31:296–322
Coole D, Frost S (eds) (2010) New materialisms: ontology, agency, and politics. Duke University Press, Durham
DeZure D (2017) Interdisciplinary pedagogies in higher education. In: Frodeman R (ed) The Oxford handbook of interdisciplinarity, 2nd edn. Oxford University Press, pp. 558–572
Dolphijn R, van der Tuin I (2012) New materialism: interviews and cartographies. Open Humanities Press
Dunk RA (2020) Diffracting the ‘quantum’ and the ‘social’: meeting the universe halfway in social science. Cult Stud↔Crit Methodol 20(3):225–234
Edinburgh Futures Institute (2020) Shadow on the street: Edinburgh’s links with the slave trade. Film by McFall L and AWED (Goss S, McFall L, Umney D, Moats D). https://www.youtube.com/watch?v=nrx5yQnx6QM . Accessed 28 Mar 2024
Federici S (2004) Caliban and the witch: Women, the body and primitive accumulation. Autonomedia, Brooklyn, NY
Gallagher SE, Savage T (2020) Challenge-based learning in higher education: an exploratory literature review. Teach High Educ 28(6):1135–1157
Gallagher SE, Savage T (2022) Challenge based learning: recommendations for the future of higher education. In: Vilalta-Perdomo E, Membrillo-Hernández J, Michel-Villarreal R, Lakshmi G, Martínez-Acosta M (eds) The Emerald handbook of challenge based learning. Emerald Publishing Limited, Bingley, pp. 391–411
Geerts E, Van der Tuin I (2021) Almanac: diffraction and reading diffractively. Matter 2(1):173–177
Haraway D (2016) Staying with the trouble: making kin in the Chthulucene. Duke University Press, Durham
Book Google Scholar
Haynes C, Brown, Leonard J (2010) From surprise parties to mapmaking: undergraduate journeys toward interdisciplinary understanding. J High Educ 81(5):645–666
Klein JT (2015) Interdisciplining digital humanities: boundary work in an emerging field. University of Michigan Press, Ann Arbor
Klein JT (2017) Typologies of interdisciplinarity: the boundary work of definition. In: Frodeman R (ed) The Oxford handbook of interdisciplinarity, 2nd edn. Oxford University Press, pp. 21–34
Klein JT (2021) Beyond interdisciplinarity: boundary work, communication, and collaboration. Oxford University Press
Kuntz AM, Presnall MM (2012) Wandering the tactical: from interview to intraview. Qual Inq 18(9):732–744
Kurchik M (2023) Trickster characters: the tomboy and the girlboss, or gender as a thin-centred ideology inherent to technological innovation under capitalism. PhD thesis, University of Edinburgh
Lattuca LR (2001) Creating interdisciplinarity: interdisciplinary research and teaching among college and university faculty, 1st edn. Vanderbilt University Press, Nashville
Leijon M, Gudmundsson P, Staaf P, Christersson C (2022) Challenge based learning in higher education: a systematic literature review. Innov Educ Teach Int 59(5):609–618
Lyall C, Meagher L, Bandola J, Kettle A (2015) Interdisciplinary provision in higher education: current context and future challenges. University of Edinburgh/Advance HE. https://www.advance-he.ac.uk/knowledge-hub/interdisciplinary-provision-higher-education-current-and-future-challenges . Accessed 29 Mar 2024
Marn TM, Wolgemuth JR (2017) Purposeful entanglements: a new materialist analysis of transformative interviews. Qual Inq 23(5):365–374
Massey D (2005) For space. SAGE, London
Google Scholar
Mazzei LA (2014) Beyond an easy sense: a diffractive analysis. Qual Inq 20(6):742–746
Moran J (2010) Interdisciplinarity, 2nd edn. Routledge, London
Murris K (2022) Karen Barad as educator: agential realism and education. Springer, Singapore
Murris K, Bozalek V (2019) Diffracting diffractive readings of texts as methodology: some propositions. Educ Philos Theory 51(14):1504–1517
Newell WH (2001) A theory of interdisciplinary studies. Issues Interdiscip Stud 19:1–25
Nordstrom SN (2015) Not so innocent anymore: making recording devices matter in qualitative interviews. Qual Inq 21(4):388–401
Overend, D, Ewing S, Swanton D (2024) ‘Loose ends and missing links’: learning journeys in the postdigital city. In: Lamb J, Carvalho L (eds) Postdigital learning spaces: towards convivial, equitable, and sustainable spaces for learning. Springer, pp. 99–118
Pohl C, Truffer B, Hirsch-Hadorn G (2017) Addressing wicked problems through transdisciplinary research. In: Frodeman R (ed) The Oxford handbook of interdisciplinarity, 2nd edn. Oxford University Press, pp. 319–331
Repko AF, Szostak R (2020) Interdisciplinary research: process and theory, 4th edn. Sage
Richards DG (1996) The meaning and relevance of ‘synthesis’ in interdisciplinary studies. J Gen Educ 45(2):114–128
Rokotnitz N (2017) Goosebumps, shivers, visualization, and embodied resonance in the reading experience: the God of Small Things. Poet Today 38:2. https://doi.org/10.1215/03335372-3868603
Sanders D, Davies P (2023) Bringing Barad into outdoor learning: a reflective case study concerning quadrats and agential cuts in ecology education. In Patrick PG (ed) How people learn in informal science environments. Springer, pp. 313–334
Sheridan MP, Lemieux A, Do Nascimento A, Arnseth HC (2020) Intra‐active entanglements: what posthuman and new materialist frameworks can offer the learning sciences. Br J Educ Technol 51(4):1277–1291
Smith P (2010) Mythogeography. Triarchy Press, Axminster
Spector K (2015) Meeting pedagogical encounters halfway. J Adolesc Adult Lit 58(6):447–450
St Pierre EA (2021) Why post qualitative inquiry? Qual Inq 27(2):163–166
Taguchi HL (2012) A diffractive and Deleuzian approach to analysing interview data. Fem Theory 13(3):265–281
University of Edinburgh (2023) Challenge-based learning. Internal document, Edinburgh
Uprichard E, Dawney L (2019) Data diffraction: challenging data integration in mixed methods research. J Mixed Methods Res 13(1):19–32
van der Tuin I (2016) Reading diffractive reading: where and when does diffraction happen? J Electron Publ 19(2)
van der Waldt G (2014) Public administration teaching and interdisciplinarity: considering the consequences. Teaching Publ Admin 32(2):169–193
Vienni-Baptista B, Fletcher I, Lyall C (eds) (2023) Foundations of interdisciplinary and transdisciplinary research: a reader. Bristol University Press
Vilsmaier U, Merçon J, Meyer E (2023) Transdisciplinarity. In: Philipp T, Schmohl T (eds) Handbook transdisciplinary learning. Transcript, Bielefeld, pp. 381–390
Wallace RL, Clark SG (2017) Barriers to Interdisciplinarity in environmental studies: a case of alarming trends in faculty and programmatic wellbeing. Issues Integr Stud 35:221–247
Warfield K (2017) I set the camera on the handle of my dresser: re-matter-ializing social media visual methods through a case study of selfies. Media Commun 5(4):65–74
Woods C (2007) Researching and developing interdisciplinary teaching: towards a conceptual framework for classroom communication. High Educ 54(6):853–866
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We would like to thank all the Creating Edinburgh students who participated in and supported this research. Thank you also to the tutors, David Meinberg, Giulia Pinton and Eftihia Saxoni. We are grateful for funding from the Principals Teaching Award Scheme at the University of Edinburgh.
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Cullen, C., Jay, D., Overend, D. et al. Creating Edinburgh: diffracting interdisciplinary learning and teaching in the contemporary city. Humanit Soc Sci Commun 11 , 1151 (2024). https://doi.org/10.1057/s41599-024-03566-5
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Overview Frequency Principle/Spectral Bias in Deep Learning
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- Published: 04 September 2024
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- Zhi-Qin John Xu ORCID: orcid.org/0000-0003-0627-3520 1 , 2 ,
- Yaoyu Zhang 1 , 2 &
- Tao Luo 1 , 2 , 3 , 4
Understanding deep learning is increasingly emergent as it penetrates more and more into industry and science. In recent years, a research line from Fourier analysis sheds light on this magical “black box” by showing a Frequency principle (F-Principle or spectral bias) of the training behavior of deep neural networks (DNNs)—DNNs often fit functions from low to high frequencies during the training. The F-Principle is first demonstrated by one-dimensional (1D) synthetic data followed by the verification in high-dimensional real datasets. A series of works subsequently enhance the validity of the F-Principle. This low-frequency implicit bias reveals the strength of neural networks in learning low-frequency functions as well as its deficiency in learning high-frequency functions. Such understanding inspires the design of DNN-based algorithms in practical problems, explains experimental phenomena emerging in various scenarios, and further advances the study of deep learning from the frequency perspective. Although incomplete, we provide an overview of the F-Principle and propose some open problems for future research.
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Agarwal, R., Frosst, N., Zhang, X., Caruana, R., Hinton, G.E.: Neural additive models: interpretable machine learning with neural nets. arXiv:2004.13912 (2020)
Arora, S., Du, S., Hu, W., Li, Z., Wang, R.: Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks. In: International Conference on Machine Learning, pp. 322–332 (2019)
Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M.S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., Lacoste-Julien, S.: A closer look at memorization in deep networks. In: International Conference on Machine Learning, pp. 233–242 (2017)
Aubin, B., Maillard, A., Barbier, J., Krzakala, F., Macris, N., Zdeborová, L.: The committee machine: computational to statistical gaps in learning a two-layers neural network. Adv. Neural Inf. Process. Syst. 31 , 3223–3234 (2018)
Google Scholar
Baratin, A., George, T., Laurent, C., Hjelm, R.D., Lajoie, G., Vincent, P., Lacoste-Julien, S.: Implicit regularization via neural feature alignment. arXiv:2008.00938 (2020)
Basri, R., Galun, M., Geifman, A., Jacobs, D., Kasten, Y., Kritchman, S.: Frequency bias in neural networks for input of non-uniform density. In: International Conference on Machine Learning, pp. 685–694 (2020)
Basri, R., Jacobs, D., Kasten, Y., Kritchman, S.: The convergence rate of neural networks for learned functions of different frequencies. Adv. Neural Inf. Process. Syst. 32 , 4761–4771 (2019)
Bi, S., Xu, Z., Srinivasan, P., Mildenhall, B., Sunkavalli, K., Hašan, M., Hold-Geoffroy, Y., Kriegman, D., Ramamoorthi, R.: Neural reflectance fields for appearance acquisition. arXiv:2008.03824 (2020)
Biland, S., Azevedo, V.C., Kim, B., Solenthaler, B.: Frequency-aware reconstruction of fluid simulations with generative networks. arXiv:1912.08776 (2019)
Bordelon, B., Canatar, A., Pehlevan, C.: Spectrum dependent learning curves in kernel regression and wide neural networks. In: International Conference on Machine Learning, pp. 1024–1034 (2020)
Breiman, L.: Reflections after refereeing papers for nips. In: The Mathematics of Generalization, pp. 11–15 (1995)
Brenner, S.C., Scott, L.R.: The Mathematical Theory of Finite Element Methods. Springer, New York (2008)
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. arXiv:2005.14165 (2020)
Cai, W., Li, X., Liu, L.: A phase shift deep neural network for high frequency approximation and wave problems. SIAM J. Sci. Comput. 42 (5), 3285–3312 (2020)
Article MathSciNet Google Scholar
Cai, W., Xu, Z.-Q.J.: Multi-scale deep neural networks for solving high dimensional PDEs. arXiv:1910.11710 (2019)
Campo, M., Chen, Z., Kung, L., Virochsiri, K., Wang, J.: Band-limited soft actor critic model. arXiv:2006.11431 (2020)
Camuto, A., Willetts, M., Şimşekli, U., Roberts, S., Holmes, C.: Explicit regularisation in Gaussian noise injections. arXiv:2007.07368 (2020)
Cao, Y., Fang, Z., Wu, Y., Zhou, D.-X., Gu, Q.: Towards understanding the spectral bias of deep learning. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pp. 2205–2211 (2021)
Chakrabarty, P.: The spectral bias of the deep image prior. In: Bayesian Deep Learning Workshop and Advances in Neural Information Processing Systems (NeurIPS) (2019)
Chen, G.-Y., Gan, M., Chen, C.P., Zhu, H.-T., Chen, L.: Frequency principle in broad learning system. IEEE Trans. Neural Netw. Learn. Syst. 33 , 6983 (2021)
Article Google Scholar
Chen, H., Lin, M., Sun, X., Qi, Q., Li, H., Jin, R.: MuffNet: multi-layer feature federation for mobile deep learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (2019)
Chen, Y., Li, G., Jin, C., Liu, S., Li, T.: SSD-GAN: measuring the realness in the spatial and spectral domains. Proc. AAAI Conf. Artif. Intell. 35 , 1105–1112 (2021)
Chizat, L., Bach, F.: On the global convergence of gradient descent for over-parameterized models using optimal transport. Adv. Neural Inf. Process. Syst. 31 , 3036–3046 (2018)
Choromanska, A., Henaff, M., Mathieu, M., Arous, G.B., LeCun, Y.: The loss surfaces of multilayer networks. In: Artificial Intelligence and Statistics, pp. 192–204 (2015)
Deng, X., Zhang, Z.M.: Is the meta-learning idea able to improve the generalization of deep neural networks on the standard supervised learning? In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 150–157 (2021)
Dissanayake, M., Phan-Thien, N.: Neural-network-based approximations for solving partial differential equations. Commun. Numer. Methods Eng. 10 (3), 195–201 (1994)
Dong, B., Hou, J., Lu, Y., Zhang, Z.: Distillation \(\approx \) early stopping? Harvesting dark knowledge utilizing anisotropic information retrieval for overparameterized neural network. arXiv:1910.01255 (2019)
Dyson, F.: A meeting with Enrico Fermi. Nature 427 (6972), 297 (2004)
E, W., Han, J., Jentzen, A.: Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations. Commun. Math. Stat. 5 (4), 349–380 (2017)
E, W., Han, J., Jentzen, A.: Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning. Nonlinearity 35 (1), 278 (2021)
E, W., Ma, C., Wang, J.: Model reduction with memory and the machine learning of dynamical systems. Commun. Comput. Phys. 25 (4), 947–962 (2018)
E, W., Ma, C., Wu, L.: A priori estimates of the population risk for two-layer neural networks. Commun. Math. Sci. 17 (5), 1407–1425 (2019)
E, W., Ma, C., Wu, L.: Machine learning from a continuous viewpoint, I. Sci. China Math. 63 , 2233–2266 (2020)
E, W., Yu, B.: The deep Ritz method: a deep learning-based numerical algorithm for solving variational problems. Commun. Math. Stat. 6 (1), 1–12 (2018)
Engel, A., Broeck, C.V.d.: Statistical Mechanics of Learning. Cambridge University Press, Cambridge (2001)
Evans, L.C.: Partial Differential Equations. American Mathematical Society, Providence, Rhode Island (2010)
Fan, Y., Lin, L., Ying, L., Zepeda-Núnez, L.: A multiscale neural network based on hierarchical matrices. Multiscale Model. Simul. 17 (4), 1189–1213 (2019)
Frankle, J., Carbin, M.: The lottery ticket hypothesis: finding sparse, trainable neural networks. arXiv:1803.03635 (2018)
Fu, Y., Guo, H., Li, M., Yang, X., Ding, Y., Chandra, V., Lin, Y.: CPT: efficient deep neural network training via cyclic precision. arXiv:2101.09868 (2021)
Fu, Y., You, H., Zhao, Y., Wang, Y., Li, C., Gopalakrishnan, K., Wang, Z., Lin, Y.: Fractrain: fractionally squeezing bit savings both temporally and spatially for efficient DNN training. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6–12, 2020, Virtual (2020)
Giryes, R., Bruna, J.: How can we use tools from signal processing to understand better neural networks? Inside Signal Processing Newsletter (2020)
Goldt, S., Mézard, M., Krzakala, F., Zdeborová, L.: Modeling the influence of data structure on learning in neural networks: the hidden manifold model. Phys. Rev. X 10 (4), 041044 (2020)
Guo, M., Fathi, A., Wu, J., Funkhouser, T.: Object-centric neural scene rendering. arXiv:2012.08503 (2020)
Han, J., Ma, C., Ma, Z., Weinan, E.: Uniformly accurate machine learning-based hydrodynamic models for kinetic equations. Proc. Natl. Acad. Sci. 116 (44), 21983–21991 (2019)
Han, J., Zhang, L., Car, R., E, W.: Deep potential: a general representation of a many-body potential energy surface. Commun. Comput. Phys. 23 , 3 (2018)
Häni, N., Engin, S., Chao, J.-J., Isler, V.: Continuous object representation networks: novel view synthesis without target view supervision. arXiv:2007.15627 (2020)
He, J., Li, L., Xu, J., Zheng, C.: ReLU deep neural networks and linear finite elements. arXiv:1807.03973 (2018)
He, J., Xu, J.: MgNet: A unified framework of multigrid and convolutional neural network. Sci. China Math. 62 (7), 1331–1354 (2019)
He, S., Wang, X., Shi, S., Lyu, M.R., Tu, Z.: Assessing the bilingual knowledge learned by neural machine translation models. arXiv:2004.13270 (2020)
Hennigh, O., Narasimhan, S., Nabian, M.A., Subramaniam, A., Tangsali, K., Fang, Z., Rietmann, M., Byeon, W., Choudhry, S.: NVIDIA SimNet: an AI-accelerated multi-physics simulation framework. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science-ICCS 2021. Lecture Notes in Computer Science, vol. 12746, pp. 447–461. Springer, Cham (2021)
Hu, W., Xiao, L., Adlam, B., Pennington, J.: The surprising simplicity of the early-time learning dynamics of neural networks. arXiv:2006.14599 (2020)
Huang, J., Wang, H., Yang, H.: Int-Deep: a deep learning initialized iterative method for nonlinear problems. J. Comput. Phys. 419 , 109675 (2020)
Huang, X., Liu, H., Shi, B., Wang, Z., Yang, K., Li, Y., Weng, B., Wang, M., Chu, H., Zhou, J., Yu, F., Hua, B., Chen, L., Dong, B.: Solving partial differential equations with point source based on physics-informed neural networks. arXiv:2111.01394 (2021)
Jacot, A., Gabriel, F., Hongler, C.: Neural tangent kernel: convergence and generalization in neural networks. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 8580–8589 (2018)
Jagtap, A.D., Kawaguchi, K., Karniadakis, G.E.: Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. J. Comput. Phys. 404 , 109136 (2020)
Jiang, L., Dai, B., Wu, W., Loy, C.C.: Focal frequency loss for generative models. arXiv:2012.12821 (2020)
Jiang, Y., Neyshabur, B., Mobahi, H., Krishnan, D., Bengio, S.: Fantastic generalization measures and where to find them. In: International Conference on Learning Representations (2019)
Jin, H., Montúfar, G.: Implicit bias of gradient descent for mean squared error regression with wide neural networks. arXiv:2006.07356 (2020)
Jin, P., Lu, L., Tang, Y., Karniadakis, G.E.: Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. Neural Netw. 130 , 85–99 (2020)
Kalimeris, D., Kaplun, G., Nakkiran, P., Edelman, B., Yang, T., Barak, B., Zhang, H.: SGD on neural networks learns functions of increasing complexity. Adv. Neural Inf. Process. Syst. 32 , 3496–3506 (2019)
Khoo, Y., Ying, L.: SwitchNet: a neural network model for forward and inverse scattering problems. SIAM J. Sci. Comput. 41 (5), 3182–3201 (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014)
Kopitkov, D., Indelman, V.: Neural spectrum alignment: empirical study. In: International Conference on Artificial Neural Networks, pp. 168–179. Springer (2020)
Lampinen, A.K., Ganguli, S.: An analytic theory of generalization dynamics and transfer learning in deep linear networks. In: The International Conference on Learning Representations (2019)
Lee, J., Xiao, L., Schoenholz, S., Bahri, Y., Novak, R., Sohl-Dickstein, J., Pennington, J.: Wide neural networks of any depth evolve as linear models under gradient descent. Adv. Neural Inf. Process. Syst. 32 , 8572–8583 (2019)
Li, M., Soltanolkotabi, M., Oymak, S.: Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks. In: International Conference on Artificial Intelligence and Statistics, pp. 4313–4324 (2020)
Li, X.-A., Xu, Z.-Q.J., Zhang, L.: A multi-scale DNN algorithm for nonlinear elliptic equations with multiple scales. Commun. Comput. Phys. 28 (5), 1886–1906 (2020). https://doi.org/10.4208/cicp.OA-2020-0187
Li, X.-A., Xu, Z.-Q.J., Zhang, L.: Subspace decomposition based DNN algorithm for elliptic-type multi-scale PDEs. J. Comput. Phys. 488 , 112242 (2023). https://doi.org/10.2139/ssrn.4020731
Li, Y., Peng, W., Tang, K., Fang, M.: Spatio-frequency decoupled weak-supervision for face reconstruction. Comput. Intell. Neurosci. 2022 , 1–12 (2022)
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., Anandkumar, A.: Fourier neural operator for parametric partial differential equations. arXiv:2010.08895 (2020)
Liang, S., Lyu, L., Wang, C., Yang, H.: Reproducing activation function for deep learning. arXiv:2101.04844 (2021)
Lin, J., Camoriano, R., Rosasco, L.: Generalization properties and implicit regularization for multiple passes SGM. In: International Conference on Machine Learning, pp. 2340–2348 (2016)
Liu, Z., Cai, W., Xu, Z.-Q.J.: Multi-scale deep neural network (MscaleDNN) for solving Poisson-Boltzmann equation in complex domains. Commun. Comput. Phys. 28 (5), 1970–2001 (2020)
Lu, L., Jin, P., Pang, G., Zhang, Z., Karniadakis, G.E.: Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell. 3 (3), 218–229 (2021)
Lu, L., Meng, X., Mao, Z., Karniadakis, G.E.: DeepXDE: a deep learning library for solving differential equations. SIAM Rev. 63 (1), 208–228 (2021)
Luo, T., Ma, Z., Wang, Z., Xu, Z.-Q.J., Zhang, Y.: Fourier-domain variational formulation and its well-posedness for supervised learning. arXiv:2012.03238 (2020)
Luo, T., Ma, Z., Xu, Z.-Q.J., Zhang, Y.: On the exact computation of linear frequency principle dynamics and its generalization. arXiv:2010.08153 (2020)
Luo, T., Ma, Z., Xu, Z.-Q.J., Zhang, Y.: Theory of the frequency principle for general deep neural networks. CSIAM Trans. Appl. Math. 2 (3), 484–507 (2021). https://doi.org/10.4208/csiam-am.SO-2020-0005
Luo, T., Xu, Z.-Q.J., Ma, Z., Zhang, Y.: Phase diagram for two-layer ReLU neural networks at infinite-width limit. J. Mach. Learn. Res. 22 , 1–47 (2021)
MathSciNet Google Scholar
Ma, C., Wu, L., E., W.: The slow deterioration of the generalization error of the random feature model. In: Mathematical and Scientific Machine Learning, pp. 373–389 (2020)
Ma, Y., Xu, Z.-Q.J., Zhang, J.: Frequency principle in deep learning beyond gradient-descent-based training. arXiv:2101.00747 (2021)
Mei, S., Montanari, A., Nguyen, P.-M.: A mean field view of the landscape of two-layer neural networks. Proc. Natl. Acad. Sci. 115 (33), 7665–7671 (2018)
Michoski, C., Milosavljevic, M., Oliver, T., Hatch, D.: Solving irregular and data-enriched differential equations using deep neural networks. arXiv:1905.04351 (2019)
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: European Conference on Computer Vision, pp. 405–421. Springer (2020)
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65 (1), 99–106 (2021)
Mingard, C., Skalse, J., Valle-Pérez, G., Martínez-Rubio, D., Mikulik, V., Louis, A.A.: Neural networks are a priori biased towards boolean functions with low entropy. arXiv:1909.11522 (2019)
Nye, M., Saxe, A.: Are efficient deep representations learnable? arXiv:1807.06399 (2018)
Peng, S., Zhang, Y., Xu, Y., Wang, Q., Shuai, Q., Bao, H., Zhou, X.: Neural body: implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9054–9063 (2021)
Peng, W., Zhou, W., Zhang, J., Yao, W.: Accelerating physics-informed neural network training with prior dictionaries. arXiv:2004.08151 (2020)
Pumarola, A., Corona, E., Pons-Moll, G., Moreno-Noguer, F.: D-NeRF: neural radiance fields for dynamic scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10318–10327 (2021)
Rabinowitz, N.C.: Meta-learners’ learning dynamics are unlike learners’. arXiv:1905.01320 (2019)
Rahaman, N., Arpit, D., Baratin, A., Draxler, F., Lin, M., Hamprecht, F.A., Bengio, Y., Courville, A.: On the spectral bias of deep neural networks. In: International Conference on Machine Learning (2019)
Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378 , 686–707 (2019)
Rotskoff, G.M., Vanden-Eijnden, E.: Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 7146–7155 (2018)
Saxe, A.M., Bansal, Y., Dapello, J., Advani, M., Kolchinsky, A., Tracey, B.D., Cox, D.D.: On the information bottleneck theory of deep learning. J. Stat. Mech. Theory Exp. 2019 (12), 124020 (2019). https://doi.org/10.1088/1742-5468/ab3985
Saxe, A.M., McClelland, J.L., Ganguli, S.: Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. In: The International Conference on Learning Representations (2014)
Schwarz, K., Liao, Y., Geiger, A.: On the frequency bias of generative models. Adv. Neural Inf. Process. Syst. 34 , 18126 (2021)
Shalev-Shwartz, S., Shamir, O., Shammah, S.: Failures of gradient-based deep learning. In: International Conference on Machine Learning, pp. 3067–3075 (2017)
Sharma, R., Ross, A.: D-NetPAD: an explainable and interpretable iris presentation attack detector. In: 2020 IEEE International Joint Conference on Biometrics (IJCB), pp. 1–10 (2020)
Shen, J., Tang, T., Wang, L.L.: Spectral Methods: Algorithm, Analysis and Applications. Springer, Berlin (2011)
Shwartz-Ziv, R., Tishby, N.: Opening the black box of deep neural networks via information. arXiv:1703.00810 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings (2015)
Sirignano, J., Spiliopoulos, K.: Mean field analysis of neural networks: a central limit theorem. Stoch. Process. Appl. 130 (3), 1820–1852 (2020)
Strofer, C.M., Wu, J.-L., Xiao, H., Paterson, E.: Data-driven, physics-based feature extraction from fluid flow fields using convolutional neural networks. Commun. Comput. Phys. 25 (3), 625–650 (2019)
Tancik, M., Mildenhall, B., Wang, T., Schmidt, D., Srinivasan, P.P., Barron, J.T., Ng, R.: Learned initializations for optimizing coordinate-based neural representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2846–2855 (2021)
Tancik, M., Srinivasan, P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J., Ng, R.: Fourier features let networks learn high frequency functions in low dimensional domains. Adv. Neural Inf. Process. Syst. 33 , 7537–7547 (2020)
Wang, B., Zhang, W., Cai, W.: Multi-scale deep neural network (MscaleDNN) methods for oscillatory stokes flows in complex domains. Commun. Comput. Phys. 28 (5), 2139–2157 (2020)
Wang, J., Xu, Z.-Q.J., Zhang, J., Zhang, Y.: Implicit bias with Ritz-Galerkin method in understanding deep learning for solving PDEs. arXiv:2002.07989 (2020)
Wang, S., Wang, H., Perdikaris, P.: On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks. Comput. Methods Appl. Mech. Eng. 384 , 113938 (2021)
Xi, Y., Jia, W., Zheng, J., Fan, X., Xie, Y., Ren, J., He, X.: DRL-GAN: dual-stream representation learning GAN for low-resolution image classification in UAV applications. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 14 , 1705–1716 (2020)
Xie, B., Liang, Y., Song, L.: Diverse neural network learns true target functions. Int. Conf. Artif. Intell. Stat. 54 , 1216–1224 (2017)
Xu, R., Wang, X., Chen, K., Zhou, B., Loy, C.C.: Positional encoding as spatial inductive bias in GANs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13569–13578 (2021)
Xu, Z.-Q.J.: Understanding training and generalization in deep learning by Fourier analysis. arXiv:1808.04295 (2018)
Xu, Z.-Q.J., Zhang, Y., Luo, T., Xiao, Y., Ma, Z.: Frequency principle: Fourier analysis sheds light on deep neural networks. Commun. Comput. Phys. 28 (5), 1746–1767 (2020)
Xu, Z.-Q.J., Zhang, Y., Xiao, Y.: Training behavior of deep neural network in frequency domain. In: International Conference on Neural Information Processing, pp. 264–274. Springer (2019)
Xu, Z.-Q.J., Zhou, H.: Deep frequency principle towards understanding why deeper learning is faster. Proc. AAAI Conf. Artif. Intell. 35 , 10541 (2021)
Yang, G., Salman, H.: A fine-grained spectral perspective on neural networks. arXiv:1907.10599 (2019)
Yang, M., Wang, Z., Chi, Z., Zhang, Y.: FreGAN: exploiting frequency components for training GANs under limited data. arXiv:2210.05461 (2022)
You, H., Li, C., Xu, P., Fu, Y., Wang, Y., Chen, X., Lin, Y., Wang, Z., Baraniuk, R.G.: Drawing early-bird tickets: towards more efficient training of deep networks. In: International Conference on Learning Representations (2020)
Zang, Y., Bao, G., Ye, X., Zhou, H.: Weak adversarial networks for high-dimensional partial differential equations. J. Comput. Phys. 411 , 109409 (2020)
Zdeborová, L.: Understanding deep learning is also a job for physicists. Nat. Phys. 16 , 1–3 (2020). https://doi.org/10.1038/s41567-020-0929-2
Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. In: 5th International Conference on Learning Representations (2017)
Zhang, L., Luo, T., Zhang, Y., Xu, Z.-Q.J., Ma, Z.: MOD-NET: a machine learning approach via model-operator-data network for solving PDEs. arXiv:2107.03673 (2021)
Zhang, Y., Li, Y., Zhang, Z., Luo, T., Xu, Z.-Q.J.: Embedding principle: a hierarchical structure of loss landscape of deep neural networks. arXiv:2111.15527 (2021)
Zhang, Y., Luo, T., Ma, Z., Xu, Z.-Q.J.: A linear frequency principle model to understand the absence of overfitting in neural networks. Chin. Phys. Lett. 38 (3), 038701 (2021)
Zhang, Y., Xu, Z.-Q.J., Luo, T., Ma, Z.: Explicitizing an implicit bias of the frequency principle in two-layer neural networks. arXiv:1905.10264 (2019)
Zhang, Y., Xu, Z.-Q.J., Luo, T., Ma, Z.: A type of generalization error induced by initialization in deep neural networks. In: Mathematical and Scientific Machine Learning, pp. 144–164 (2020)
Zhang, Y., Zhang, Z., Luo, T., Xu, Z.-Q.J.: Embedding principle of loss landscape of deep neural networks. NeurIPS (2021)
Zheng, Q., Babaei, V., Wetzstein, G., Seidel, H.-P., Zwicker, M., Singh, G.: Neural light field 3D printing. ACM Trans. Graph. (TOG) 39 (6), 1–12 (2020)
Zhu, H., Qiao, Y., Xu, G., Deng, L., Yu, Y.-F.: DSPNet: a lightweight dilated convolution neural networks for spectral deconvolution with self-paced learning. IEEE Trans. Ind. Inf. 16 , 7392 (2019)
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Acknowledgements
This work is sponsored by the National Key R &D Program of China Grant No. 2022YFA1008200 (Z. X., Y. Z., T. L.), the National Natural Science Foundation of China Grant Nos. 92270001 (Z. X.), 12371511 (Z. X.), 12101402 (Y. Z.), 12101401 (T. L.), the Lingang Laboratory Grant No. LG-QS-202202-08 (Y. Z.), the Shanghai Municipal Science and Technology Key Project No. 22JC1401500 (T. L.), the Shanghai Municipal of Science and Technology Major Project No. 2021SHZDZX0102, and the HPC of School of Mathematical Sciences and the Student Innovation Center, and the Siyuan-1 cluster supported by the Center for High Performance Computing at Shanghai Jiao Tong University.
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Zhi-Qin John Xu, Yaoyu Zhang & Tao Luo
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Xu, ZQ.J., Zhang, Y. & Luo, T. Overview Frequency Principle/Spectral Bias in Deep Learning. Commun. Appl. Math. Comput. (2024). https://doi.org/10.1007/s42967-024-00398-7
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DOI : https://doi.org/10.1007/s42967-024-00398-7
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The 7 steps to problem-solving. When it comes to problem-solving there are seven key steps that you should follow: define the problem, disaggregate, prioritize problem branches, create an analysis plan, conduct analysis, synthesis, and communication. 1. Define the problem. Problem-solving begins with a clear understanding of the issue at hand.
1. Problem definition. A thorough understanding and crisp definition of the problem. 2. The problem-solving process. Structuring the problem, prioritizing the issues, planning analyses, conducting analyses, synthesizing findings, and developing recommendations. 3. Distinctiveness practices.
It's a problem-solving framework that was popularized by consulting firm, McKinsey & Company. We use MECE because it forces you to look carefully at each part of the problem and all parts of the problem which leads to a deeper understanding of the problem space and often, compelling insights. When working on a project, the MECE framework is a ...
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Top-tier services to ensure learner success. Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work. Take Udacity's free Advanced Analytics course and learn a scientific approach to solving problems with data to help make business decisions.
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Root Cause Analysis (RCA) is a problem-solving method that aims at identifying the root cause of a problem by moving back to its origin, as opposed to techniques that only address and treat the symptoms. The RCA is corrective in its nature with a final goal to prevent the same problem from happening again in the future.
It contains 20 frameworks that can be roughly categorized into three types of analyses: Industry Analysis. Company Analysis. Prioritization Frameworks. These frameworks are useful tools to tackle ...
Selecting the appropriate problem-solving framework depends on the nature of the issue at hand and personal preferences. By incorporating a systematic approach into your problem-solving toolkit, you can enhance your ability to tackle challenges and achieve success both personally and professionally. Part 1. Identifying the Problem.
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Understanding deep learning is increasingly emergent as it penetrates more and more into industry and science. In recent years, a research line from Fourier analysis sheds light on this magical "black box" by showing a Frequency principle (F-Principle or spectral bias) of the training behavior of deep neural networks (DNNs)—DNNs often fit functions from low to high frequencies during the ...