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Framing a Business Problem as a Data Science Problem

  • 5 min read

By Krishna Raha – Senior Manager Sales at PepsiCo

In the process of product development, encountering challenges is inevitable. However, the approach to addressing these challenges can make all the difference. One critical aspect often overlooked is problem framing. In a recent study conducted by Harvard across top global companies, a concerning trend emerged: 85% of CXOs and senior executives confessed to their organizations’ struggles with problem diagnosis.

This revelation highlights a critical issue plaguing even the most esteemed organizations — the inability to accurately identify and tackle core challenges.

In this blog, we will learn about problem framing, its significance, and different problem framing frameworks.

Key Takeaways:

  • Problem framing, a design thinking methodology, is pivotal for understanding, defining, and prioritizing complex business problems.
  • Familiar problems with known complexities benefit from established practices and expertise. Unfamiliar challenges require collaboration with experts for effective navigation.
  • Simple, complicated, and complex problems demand different approaches. Solutions vary based on the clarity of complexities and discovery approaches.
  • Analyze business context, assess impact, and evaluate data for effective problem understanding. Define clear success metrics and engage stakeholders for solution development.
  • The Crisp-DM framework, standing for Cross-Industry Standard Process for Data Mining, offers a systematic cycle encompassing business understanding, data preparation, modeling, evaluation, and deployment.
In this article
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    An Example of Problem Reframing

    Picture this scenario: In an apartment complex, many residents approach the society president with a common complaint that the elevator is too slow. It seems like a very straightforward problem statement. However, the key lies in how we frame the problem and explore potential solutions to this problem.

    The immediate solution to this problem, the majority of us would think is typically to make it faster. Some of us might give a variety of options such as changing the motor, improving algorithms, or even replacing the lift altogether. While these solutions do address the problem, they do not consider the underlying factors such as budget constraints and the extent to which the association can financially help with solving the problem.

    Here’s where problem reframing comes into play. Instead of solely addressing the speed of the elevator, reframing the issue as the annoyance of waiting opens up new possibilities. To address this problem, you might come up with suggestions like adding mirrors or playing music in the elevator which aim to temporarily address this inconvenience, thus buying time to implement a more concrete solution.

    1. Simple Problems

    Simple problems are those with known complexities and clear discovery approaches. They often involve routine tasks or familiar scenarios.

    Characteristics

    • A clear understanding of the problem.
    • Established methods and best practices for resolution.
    • Familiarity with the required data and processes.

    Example 

    Building basic reports, and conducting market analysis based on past experiences.

    2. Complicated Problems

    Complicated problems involve known or unknown complexities but have clear discovery approaches. They require deeper analysis and expertise to navigate successfully.

    Characteristics

    • May involve various factors or parameters.
    • Requires expertise or domain knowledge.
    • Solutions may require iterative refinement.

    Example 

    Devising strategies to enhance revenue without clarity on underlying factors, requiring collaboration with experts for effective solutions.

    3. Complex Problems

    Complex problems pose challenges with both unknown complexities and discovery approaches. They are often dynamic and multifaceted, defying straightforward solutions.

    Characteristics

    • Uncertainty regarding the problem’s nature and resolution methods.
    • Involves multiple interconnected variables.
    • Solutions may emerge through iterative experimentation.

    Example 

    Addressing ambiguous challenges such as improving organizational culture or navigating market disruptions without clear precedents.

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    Problem Framing: Design Principles

    Let’s delve into the three key pillars of problem reframing: business context, business impact, and data and tools.

    1. Business Context Analysis

    • Understanding the context surrounding a problem is paramount. Categorizing the problem based on its nature, such as cost optimization or revenue improvement, lays the foundation for effective solutions.
    • Delving into the specifics of the business context, such as organizational constraints and industry dynamics, enables a deeper understanding of the problem’s intricacies.
    • Conducting thorough research and analysis before diving into solutions ensures alignment with organizational goals and stakeholders’ expectations.

    2. Assessing Business Impact

    • Identifying the success criteria and defining key metrics for measuring the impact of proposed solutions is crucial. Success criteria provide clarity on the desired outcomes and enable stakeholders to align their expectations.
    • Engaging stakeholders and mapping out their roles in the solution implementation process fosters collaboration and ensures buy-in from key decision-makers.
    • Evaluating potential risks and constraints associated with the proposed solutions enables proactive risk management and mitigation strategies.

    3. Data and Tools Evaluation

    • Assessing the availability and quality of data required for analysis is essential. Identifying data sources, collection methodologies, and potential gaps ensures the reliability and accuracy of insights derived from the data.
    • Evaluating the suitability of tools and resources for the problem-solving process is critical. Selecting the right tools and leveraging expertise in data analysis and technology enables efficient and effective solution implementation.
    • Balancing trade-offs between cost, time, and solution accuracy is key. Reframing the problem and adjusting solution approaches based on available resources and constraints ensures pragmatic and actionable outcomes.

    How To Prioritize the Target Audience and Define the Problem Statement

    When tackling a complex problem or project, it’s crucial to prioritize your target audience and define the problem statement effectively. Here’s a focused approach using three key strategies: questioning the objective, asking what’s missing, and establishing legitimacy.

    1. Question the Objective

    Understanding the true objective behind a brief or task is essential. Often, the initial problem presented might not be the actual issue that needs solving. Here’s how you can approach this:

    • Probe Deeply: When a brief is given, ask why this objective is important. For example, if you are tasked with improving a supply chain process, inquire why this specific aspect of the supply chain needs improvement and what outcomes are expected.
    • Identify Underlying Needs: Consider the example of two people arguing over whether to keep a window open or closed. By asking why each person wants the window in a certain position, you discover that one wants fresh air and the other fears getting a cold. Understanding these needs allows you to find a solution that accommodates both parties.
    • Clarify Expectations: Engage in discussions to clarify what success looks like for the stakeholders. This ensures that everyone is aligned and that the objective you are working towards is the one that truly matters.

    2. Ask What’s Missing

    Often, problem statements come with implicit assumptions and overlooked details. To get a complete picture, it’s crucial to ask what’s missing from the initial description:

    • Look Beyond the Obvious: Instead of diving straight into the given details, consider what might not be stated. For example, a hospital wants to reduce discharge times, but the root issue might be a lack of beds or scheduling conflicts for treatments. Always ask, “What are we not seeing here?”
    • Explicit Questioning: Pose direct questions about potential gaps. If you are told to improve market perceptions, ask what internal processes or external perceptions might be contributing to the current situation.
    • Consider All Angles: Take a holistic view of the problem. In the example of the company’s market perception, the team initially focused on financial metrics. However, considering non-financial factors, such as how junior leaders communicate with analysts, uncovered a simple yet effective solution.

    3. Establish Legitimacy

    Introducing new methods or questioning established practices can be challenging, especially if you’re the only one advocating for change. Establishing the legitimacy of your approach is crucial:

    • Build Credibility: Share your research and the rationale behind your methods. For example, if you propose reframing the questions asked, explain the benefits and provide evidence of its effectiveness.
    • Engage Key Stakeholders: Before presenting your solution to a larger group, get buy-in from influential stakeholders. Discuss your findings with them individually to gain their support.
    • Create a Supportive Environment: Ensure that your suggestions are seen as collaborative efforts rather than confrontational challenges. Make stakeholders feel part of the process and show how their input has shaped your approach.

    Best Practices for Effective Reframing

    Reframing problems is an essential skill in problem-solving, allowing for innovative and effective solutions. Here are the best practices for effective reframing, focusing on research and information gathering, concise problem statements, framing questions, using positive language, rephrasing and focusing, challenging assumptions, changing perspectives, and becoming a problem maker.

    1. Research and Collect Information

    Effective problem-solving starts with thorough research. Without a deep understanding of the problem’s context, implications, and possible consequences, any attempt at reframing will be superficial.

    • Understand the Full Context: Dive deep into the problem, much like Indra Nooyi, former CEO of PepsiCo, who would meticulously study every aspect of an issue from the ground up. Ensure you grasp the entire context before attempting to solve it.
    • Comprehensive Stakeholder Analysis: Identify and understand the perspectives of all stakeholders affected by the problem. This includes recognizing the urgency, frequency, and specific impacts on each group involved.
    • Avoid Analysis Paralysis: While in-depth research is crucial, it’s important to avoid getting stuck in excessive analysis. Focus on gathering relevant information that directly impacts the problem at hand.

    2. Apply the 40-20-10-5 Rule

    Concision is crucial in framing problems effectively. The 40-20-10-5 rule helps distill a problem into its most essential elements:

    • State the Problem in 40 Words: Begin by articulating the problem in 40 words.
    • Cut it Down to 20 Words: Refine the statement, retaining only the most critical elements.
    • Reduce to 10 Words: Further condense the problem, focusing on the core issue.
    • End with 5 Words: Create a succinct, actionable problem statement in five words.

    This exercise ensures your problem statement is focused and clear, aiding in precise problem-solving.

    3. Frame Questions, Not Statements

    Questions open the framework to new streams of thought, whereas statements can limit perspectives.

    • Promote Exploration: For example, instead of stating, “Our cricket team is performing poorly,” ask, “How can we improve our cricket team’s performance?” This approach invites solutions and fosters creative thinking.
    • Encourage Engagement: Questions engage stakeholders in a dialogue, making them active participants in the problem-solving process.

    4. Always Use Positive Language

    The language you use can significantly impact how problems are perceived and tackled.

    • Engage Constructively: Positive language promotes active listening and constructive engagement. Instead of highlighting what’s wrong, focus on opportunities for improvement.
    • Reduce Defensive Reactions: Negative statements can activate fear and defensiveness, while positive framing can ease tensions and foster a collaborative atmosphere.

    5. Rephrase and Focus

    Take the time to rephrase the problem, ensuring clarity and precision.

    • Iterative Reframing: Rephrase the problem multiple times, focusing on different aspects until you achieve a clear, actionable statement.
    • Break Down Complex Problems: Simplify complex issues by breaking them into smaller, manageable parts, ensuring each part is addressed effectively.

    6. Challenge Assumptions

    Many problems are rooted in unchallenged assumptions. Identifying and questioning these can lead to breakthrough insights.

    • Critical Evaluation: Ask yourself, “What do we know to be true?” and “How much do we assume to be true?” This critical evaluation can uncover hidden aspects of the problem.
    • Avoid Bias: Challenge existing paradigms and be open to new perspectives that may contradict initial assumptions.

    7. Change the Perspective

    Viewing the problem from different angles can reveal new solutions.

    • External Viewpoint: Observe the problem as if you are an outsider. This detachment can provide a fresh perspective.
    • Stakeholder Perspectives: Analyze the problem from the viewpoints of different stakeholders to gain a comprehensive understanding.

    8. Become a Problem Maker

    Instead of just solving problems, create new ways to view and define problems.

    • Proactive Approach: Anticipate potential issues and frame them proactively. This forward-thinking approach can prevent problems from escalating.
    • Innovative Framing: Continuously look for innovative ways to frame problems that challenge the status quo and open up new possibilities.

    Unraveling Business Challenges: A Deep Dive into Predictive Modeling in Banking

    In the intricate world of banking, data reigns supreme, especially when it comes to anticipating loan defaults. Let’s unpack a real challenge faced by banks and explore how predictive modeling can offer solutions.

    • Scene Setting

    Picture a scenario: a bank manager enters a room bustling with data experts, armed with a pressing issue—loan defaults. The task at hand? Develop a program that predicts which loans are likely to default. The manager promises access to historical data, setting the stage for data-driven solutions.

    • Understanding the Challenge

    The first step? Breaking down the problem. The lead data scientist poses key questions: Do we need to predict each loan’s default likelihood, or should we focus on broader trends across the loan portfolio?

    • Classifying vs. Analyzing Trends

    It’s crucial to distinguish between two approaches: classifying individual loans as defaulting or not, and analyzing overall default trends. The former aids in pinpointing high-risk loans, while the latter provides insights into broader patterns over time.

    • Making Informed Decisions

    Understanding the problem’s nuances is vital for decision-making. Collaborative discussions with stakeholders help align technical solutions with strategic goals, balancing accuracy, sensitivity, and specificity.

    • Tailoring Solutions

    Adapting solutions to meet specific business needs requires a deep understanding of the domain. By optimizing accuracy and prioritizing risk mitigation, technical solutions can be tailored to drive strategic objectives.

    • Conclusion: Bridging Data and Decisions

    Success in predictive modeling lies in bridging data analytics with business insights. By fostering collaboration and leveraging predictive analytics, banks can navigate uncertainties, drive growth, and stay resilient.

    • Embracing a Data-Driven Future

    As banking evolves, embracing a data-driven approach is essential. By harnessing predictive modeling, banks can unlock new opportunities and navigate challenges with confidence.

    Crisp_DM Framework

    The Crisp-DM framework, standing for Cross-Industry Standard Process for Data Mining, offers a systematic cycle encompassing business understanding, data preparation, modeling, evaluation, and deployment. It’s not limited to analytical models but extends to process and digital transformation. This iterative process involves aligning with business goals, refining problem statements, and documenting progress for clarity and efficiency.

    The Cross Industry Standard Process for Data Mining (Crisp-DM) offers a systematic framework for navigating the complexities of data analysis and problem-solving. Let’s delve into its structured approach, step by step.

    1. Business Understanding

    • Determine business objectives and project plans in alignment with stakeholders.
    • Assess the situation, define goals, and establish a cohesive project strategy.

    2. Data Exploration and Preparation

    • Explore available data sources to understand their relevance and quality.
    • Initiate data preparation tasks, including cleaning, integration, and formatting.

    3. Modeling

    • Select appropriate model techniques, such as regression or machine learning.
    • Emphasize process integrity over model complexity, focusing on practical effectiveness.

    4. Evaluation

    • Assess model results and performance against predefined metrics.
    • Review the process to identify strengths, weaknesses, and areas for improvement.

    5. Deployment

    • Implement the developed solutions into operational systems or workflows.
    • Monitor the deployment phase closely to ensure smooth integration and functionality.

    6. Monitoring and Maintenance

    • Continuously monitor deployed solutions for performance and effectiveness.
    • Course-correct as necessary based on ongoing evaluation and feedback.

    Effective problem-solving demands a strategic blend of structured frameworks, precise problem-framing, and strategic considerations. By leveraging approaches like the Crisp-DM model, organizations can navigate complexities, develop actionable solutions, and drive meaningful outcomes. This systematic and collaborative approach empowers businesses to overcome challenges, optimize processes, and achieve strategic objectives.

    About the Author:

    Krishna Raha – Senior Manager Sales at PepsiCo

    Frequently Asked Questions

    Framing a business problem as an analytical problem means defining it clearly for systematic analysis and data-driven decision-making. It involves setting specific objectives, clarifying scope, and articulating measurable outcomes to apply analytical techniques effectively and develop solutions.

    To frame a business problem using the SMART approach:

    • Specific: Clearly define the problem, focusing on its key aspects and objectives.
    • Measurable: Establish quantifiable metrics to assess progress and success in addressing the problem.
    • Actionable: Determine actionable steps and strategies to solve the problem effectively.
    • Relevant: Ensure that the problem is aligned with organizational goals and priorities.
    • Time-bound: Set deadlines or timelines for addressing the problem to maintain focus and accountability.

    Framing the problem is important because it provides clarity, direction, and structure to problem-solving efforts. It helps stakeholders understand the problem’s significance, scope, and desired outcomes. Additionally, framing ensures alignment with organizational goals, facilitates effective communication, and guides decision-making throughout the problem-solving process.

    Defining a problem accurately lays the foundation for developing effective solutions. This process is encapsulated in the SMART format, which emphasizes specificity, measurability, action orientation, relevance, and time-bound goals.

    The Crisp-DM framework, standing for Cross-Industry Standard Process for Data Mining, offers a systematic cycle encompassing business understanding, data preparation, modeling, evaluation, and deployment.

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