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The Power of Data: Business Problem Framing through Analytics

By Krishna Raha – Senior Manager Sales at PepsiCo

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.
  • Craft SMART statements for specificity, measurability, actionability, relevance, and time constraints.
In this article
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    The Significance of Problem Framing

    Problem framing, a design thinking methodology, is pivotal for understanding, defining, and prioritizing complex business problems.

    It becomes indispensable when traditional problem-solving approaches fall short, particularly in scenarios where the vision or objectives are unclear, and pertinent information is lacking.

    Notably, problem framing proves invaluable in navigating scenarios with multiple stakeholders harboring conflicting priorities.

    Key Applications of Problem Framing

    1. Ambiguity Resolution

    Problem framing helps in clarifying ambiguous scopes, preventing teams from getting lost in the complexity of the task at hand.

    It breaks down broad objectives into manageable components, ensuring clarity and focus in problem-solving endeavors.

    2. Stakeholder Alignment

    In situations with conflicting priorities among stakeholders (e.g., finance, marketing, sales), problem framing facilitates dialogue and decision-making by aligning diverse perspectives towards a common goal.

    3. Strategic Decision-making

    When faced with challenges lacking clear solutions or defined outcomes, problem framing offers a structured framework for exploring new avenues and defining steps as necessary.

    Business Problems: Complexity vs Discovery Approach

    When the discovery approach to solving a problem is familiar, and the complexity is known, the task becomes straightforward. This scenario is characterized by a clear understanding of the problem and established methods for resolution. Utilize existing best practices and domain expertise for effective solutions.

    Conversely, when either the complexity or the discovery approach is unknown, navigating the problem-solving process becomes challenging. In such cases, it’s imperative to sense and analyze the problem’s intricacies, collaborating with domain experts and subject matter experts (SMEs) to chart the way forward.

    Distinguishing between simple, complicated, and complex challenges is essential for devising effective strategies. Let’s explore each category in detail and understand how to approach them.

    1. Simple Problems

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


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


    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.


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


    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.


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


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

    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.

    Strategic Considerations in Solution Approval and Data Interpretation

    Understanding the dynamics of top-down approvals, biases in data interpretation, and the significance of metrics is paramount. Let’s delve into these critical aspects to enhance our problem-solving efficacy.

    1. Top-Down Approvals

      • Identifying the origin of a problem request, whether it stems from top management or other stakeholders, is crucial. Each level of stakeholders possesses distinct success metrics and expectations.
      • Gatekeepers, often pivotal in the approval process, warrant special attention. Understanding their priorities enables crafting persuasive solutions that garner swift approval.
      • Avoiding biases in data interpretation is essential. Objectively scrutinizing data prevents the influence of preconceived notions, ensuring accurate insights and informed decision-making.

    2. Mitigating Biases

      • Biases can distort data interpretation, leading to flawed conclusions. Recognizing and addressing biases, such as confirmation bias or availability bias, is vital for objective analysis.
      • Scrutinizing data through various lenses and challenging assumptions fosters a more nuanced understanding of underlying trends and patterns.
      • Cultivating a culture of data-driven decision-making helps mitigate biases and ensures that solutions are grounded in empirical evidence rather than subjective beliefs.

    3. Importance of Metrics

      • Defining clear success metrics from the outset expedites the approval process. Alignment on success criteria enables stakeholders to evaluate proposed solutions effectively.
      • Collaborating with stakeholders to identify and prioritize key metrics ensures that solutions address specific business needs comprehensively.
      • Evaluating data metrics rigorously is essential for informed decision-making. Ensuring data availability, reliability, and usability lays the foundation for effective analysis and solution development.

    Situation-Based Frameworks

    Different situations demand different approaches, necessitating the adaptation of frameworks to suit specific challenges. Let’s explore various frameworks tailored to address diverse problem-solving scenarios.

    1. Prioritization Frameworks

      • When confronted with multiple problems or solution approaches, prioritization becomes paramount. Techniques like the Eisenhower Matrix or Bullseye Diagramming aid in categorizing and prioritizing tasks based on urgency and importance.
      • Prioritization enables agile decision-making, allowing stakeholders to focus on high-impact solutions and iterate efficiently. By aligning efforts with strategic priorities, teams can deliver incremental value and maintain stakeholder engagement.

    2. Journey Mapping

      • Building a new product requires understanding the user journey intimately. Journey mapping techniques visualize the user’s experience, identifying pain points and opportunities for improvement.
      • By creating a “day in the life” narrative, product teams gain insights into user behaviors, preferences, and needs. This informs feature development and ensures that the product resonates with users, enhancing its market relevance and adoption.

    3. Problem Framing Techniques

      • Problem framing is essential for defining the scope and parameters of a problem. Techniques like the “Five Whys” or Problem Definition Worksheets facilitate a structured approach to problem analysis.
      • By dissecting complex problems into manageable components, teams can identify root causes and develop targeted solutions. Problem framing ensures clarity and alignment, laying the groundwork for effective problem-solving strategies.

    Crafting Smart Problem Statements: A Guide to Effective Problem Framing

    Clarity is key for problem-solving. 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. Let’s delve into how SMART problem statements can drive successful outcomes.

    1. Specificity

    A SMART problem statement articulates precisely what needs to be accomplished. It avoids vagueness and ambiguity, enabling stakeholders to grasp the essence of the problem. Specificity facilitates focused discussions and targeted interventions, setting the stage for impactful solutions.

    2. Measurability

    Goals must be quantifiable to gauge progress and success. SMART problem statements incorporate measurable criteria, such as key performance indicators (KPIs) or success metrics. Measurability provides a clear yardstick for assessing outcomes and making data-driven decisions.

    3. Action Orientation

    Effective problem-solving requires actionable insights and pragmatic solutions. SMART problem statements use action verbs to convey intended actions and deliverables. By delineating clear paths to execution, they promote accountability and drive tangible results.

    4. Relevance

    Solutions must align with organizational objectives and stakeholder needs. SMART problem statements ensure relevance by anchoring success metrics to broader strategic priorities. Relevance fosters buy-in from stakeholders and enhances the likelihood of solution acceptance and adoption.

    5. Time-Bound Goals

    Time constraints add urgency and focus to problem-solving efforts. SMART problem statements specify deadlines or timelines for achieving objectives. Time-bound goals create a sense of urgency, driving momentum and facilitating efficient resource allocation.

    5. Examples of SMART Problem Statements

    “Reduce business costs by 30% by October 1st through the implementation of auto-dimming lights in every room to turn off after five minutes of no movement.”

    “Increase customer satisfaction scores by 15% within six months by streamlining the complaint resolution process and reducing response times.”

    “Achieve a 20% increase in market share by the end of the fiscal year through targeted marketing campaigns and product enhancements.”


    Let us explore how to refine problem statements using the SMART framework and practical examples.

    1. Retail Example

    A retail store aims to increase sales and customer loyalty by improving product recommendations.

    Assessing SMART Criteria

      • Specific: Increase sales and customer loyalty.
      • Measurable: Lack of quantifiable metrics.
      • Action-Oriented: Solution focused on improving product recommendations.
      • Relevant: Relevance of product recommendations to sales and loyalty.
      • Time-Bound: Absence of a defined timeline.

    Reframing the Problem by Incorporating SMART Criteria

      • Specific: Increase sales by 5% and customer loyalty through understanding drivers of decline.
      • Measurable: Clearly defined targets for sales and loyalty improvement.
      • Action-Oriented: Solutions focused on analyzing decline drivers and implementing targeted strategies.
      • Relevant: Solutions tailored to address specific challenges affecting sales and loyalty.
      • Time-Bound: Setting deadlines for analysis and implementation, aligning with stakeholder timelines.

    2. Healthcare Example

    A hospital wants to reduce patient readmissions by identifying the key factors that contribute to readmissions and developing targeted interventions.

    Assessing SMART Criteria

      • Specific: Identify key factors contributing to readmissions and develop targeted interventions.
      • Measurable: Define the readmission rate and set reduction targets based on data analysis.
      • Actionable: Implement interventions to address identified factors and mitigate readmission risks.
      • Relevant: Align with the hospital’s overarching goal of enhancing patient care and outcomes.
      • Time-Bound: Establish a timeline for data analysis, intervention implementation, and outcome evaluation.

    Example: Reframed Problem Statement for Hospital

    The hospital seeks to reduce patient readmissions by 15% within the next 12 months by identifying key factors contributing to readmissions and implementing targeted interventions to address them. Success will be measured through monthly readmission rate assessments and feedback from patient care teams.

    3. Banking Example

    A bank wants to reduce the number of fraudulent transactions by identifying patterns of fraudulent behaviour and developing algorithms to flag suspicious transactions.

    Assessing SMART Criteria

      • Specific: The bank aims to reduce the number of fraudulent transactions.
      • Measurable: Establish clear criteria for measuring fraudulent activity.
      • Actionable: Implement actionable strategies to detect and prevent fraudulent behavior.
      • Relevant: Ensure solutions are tailored to address the specific challenges of fraudulent activity.
      • Time-bound: Set timelines for the implementation of fraud detection algorithms and strategies.

    Reframing the Problem by Incorporating SMART Criteria

      • Specific: The bank seeks to diminish fraudulent transactions by identifying patterns of fraudulent behavior and developing algorithms to flag suspicious transactions.
      • Measurable: We will define measurable thresholds for identifying fraudulent transactions and evaluating the effectiveness of our algorithms.
      • Actionable: Our focus will be on deploying actionable solutions to identify and mitigate instances of fraudulent transactions.
      • Relevant: Solutions will be customized to address the unique patterns and methods of fraudulent behavior prevalent in the banking sector.
      • Time-bound: Deadlines will be established for the development and deployment of fraud detection algorithms, aligning with stakeholder expectations and industry standards.

    4. Marketing Example

    A marketing agency wants to improve the effectiveness of its online advertising campaigns by identifying the key factors that influence customer engagement and optimizing its campaigns accordingly.

    Assessing SMART Criteria

      • Specific: The bank aims to reduce the number of fraudulent transactions.
      • Measurable: Establish clear criteria for measuring fraudulent activity.
      • Actionable: Implement actionable strategies to detect and prevent fraudulent behavior.
      • Relevant: Ensure solutions are tailored to address the specific challenges of fraudulent activity.
      • Time-bound: Set timelines for the implementation of fraud detection algorithms and strategies.

    Reframing the Problem by Incorporating SMART Criteria

      • Specific: Clearly define the problem statement, specifying what needs to be achieved and why it is important.
      • Measurable: Establish measurable targets or outcomes to gauge progress and success.
      • Actionable: Develop actionable strategies or interventions to address the identified problem.
      • Relevant: Ensure that the proposed solutions are relevant to the organization’s goals, objectives, and stakeholders’ needs.
      • Time-Bound: Set specific deadlines or timelines for implementing the solutions and achieving the desired outcomes. Establish a clear timeframe for monitoring progress and making any necessary adjustments.

    5. Transportation Example

    A logistics company wants to optimize its delivery routes to reduce delivery time and costs by analyzing traffic patterns, weather data, and other relevant factors.

    Assessing SMART Criteria

      • Specific: What are the precise objectives of optimizing delivery routes? Are we focusing on reducing delivery time, cost, or both?
      • Measurable: How do we quantify the current delivery time and cost? What metrics will we use to measure the effectiveness of route optimization?
      • Actionable: What actionable steps can we take to analyze traffic patterns and other relevant factors? Are there specific strategies or interventions we can implement to optimize routes?
      • Relevant: How does optimizing delivery routes align with the company’s overall goals and objectives? Are there any external factors or stakeholder priorities that we need to consider?
      • Time-bound: What is the timeline for implementing route optimization strategies? Are there deadlines or milestones we need to meet?

    Reframing the Problem by Incorporating SMART Criteria

      • Specific: Specifically, the company seeks to achieve a 15% reduction in delivery time within the next 12 months, while also lowering transportation costs by 10%.
      • Measurable: To measure progress, key performance indicators such as average delivery time and transportation expenses will be tracked on a monthly basis.
      • Actionable: Actionable strategies will involve leveraging advanced analytics to identify optimal routes, considering factors such as traffic congestion, weather conditions, and driver behavior.
      • Relevant: The project’s objectives align with the company’s mission to enhance efficiency and customer satisfaction in its logistics operations.
      • Time-Bound: The project timeline includes a three-month data collection and analysis phase, followed by the implementation of route optimization measures over the subsequent nine months.

    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 and SMART problem statements, 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 RahaSenior 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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