Problem Scoping: Properly Defining the Questions to Be Addressed

The data analytics process begins with a crucial first step: problem scoping. This stage sets the foundation for the entire analytics journey. Before jumping into data collection, wrangling, and analysis, it’s essential to clearly define the problem at hand. A well-defined problem guides the analysis, ensures that resources are used efficiently, and helps generate actionable insights that align with business goals. In this article, we’ll explore the importance of problem scoping and how to properly define the questions that need to be addressed.

Understanding the Business Context

Problem scoping begins by understanding the business context. The first question to ask is: What business challenge are we trying to solve? Whether you are analyzing sales data, customer behavior, or operational performance, the problem should be rooted in the real-world business issues that need to be addressed. It's important to align the analytics work with organizational goals and objectives.

For example, a company may be experiencing high customer churn, or a healthcare provider may want to assess the effectiveness of a new treatment. Understanding the business problem will help frame the analysis in a way that addresses the most pressing needs.

Collaborating with Stakeholders

Once the business context is understood, the next step in problem scoping is collaborating with key stakeholders. These stakeholders could include executives, managers, department heads, and others who have a vested interest in the outcome of the analysis. Engaging with stakeholders helps clarify their expectations and objectives, ensuring that the right questions are asked.

By working closely with stakeholders, analysts can gather important information that shapes the scope of the analysis. For example, stakeholders may provide insights into potential causes of the problem, metrics that are important to track, or areas of the business that need immediate attention. This collaborative approach ensures that the analysis will be relevant and useful for decision-making.

Framing the Problem with Clear, Actionable Questions

After understanding the business context and collaborating with stakeholders, the next step is to clearly frame the problem in terms of specific, actionable questions. These questions should directly address the issues identified during the business context phase. Properly defining these questions ensures that the analysis stays focused and aligned with business goals.

A common mistake in problem scoping is being too vague about the problem or the questions to be answered. Questions like “Why are sales down?” or “What’s wrong with our marketing strategy?” are too general and unhelpful. Instead, the problem should be framed in a way that directs the analysis toward specific, measurable outcomes.

For example, if a company is concerned about declining sales, the question could be reframed as: “What factors are contributing to a 10% decrease in sales in the last quarter, and how can we address them?” This question is more focused and actionable, making it easier to identify key drivers and areas for improvement.

Setting Clear Objectives and Success Criteria

Alongside defining the questions, it's important to establish clear objectives and success criteria. These are the goals you aim to achieve through the analytics process. Success criteria might include quantifiable metrics such as sales growth, improved customer retention rates, or cost reductions. These objectives help determine the scope of the analysis and guide the data collection and analysis stages.

For example, if the business goal is to improve customer retention, the objective of the analysis might be to identify the main drivers of churn and recommend targeted interventions to reduce it. Setting clear success criteria allows the team to evaluate whether the analysis has met its goals and provides a benchmark for success.

Determining the Scope and Boundaries

One of the most critical aspects of problem scoping is setting boundaries for the analysis. It’s easy to become overwhelmed by the volume of data and potential questions, but not every aspect of the problem needs to be addressed immediately. The scope should be focused on the most critical issues that will drive the greatest impact.

This involves making decisions about which variables, data sources, or time periods are relevant to the analysis. It’s also important to identify what will not be addressed in the analysis. By clearly defining the scope, analysts can ensure that resources are used efficiently and that the analysis remains manageable.

For example, if a company is investigating customer churn, the analysis might focus on specific customer segments or time periods rather than trying to understand every possible factor that influences churn. Limiting the scope ensures that the analysis remains focused and actionable.

Breaking Down Complex Problems into Manageable Questions

Sometimes, the business problem may seem too complex or broad to tackle in one go. In these cases, it’s helpful to break down the problem into smaller, more manageable sub-questions. By addressing each smaller question separately, analysts can build a comprehensive understanding of the larger issue.

For example, if the problem is to increase sales, the question might be broken down into smaller questions like:

• What are the primary factors influencing customer purchasing decisions?

• How does pricing impact customer behavior?

• Are there external factors, such as market trends or competition, that need to be considered?

By breaking down complex problems into smaller questions, the analysis becomes more structured and easier to approach.

Iterating and Refining the Problem Scope

Problem scoping is not a one-time exercise. As the analysis progresses, new insights may emerge that prompt a reevaluation of the problem or the questions being asked. Analysts should be prepared to iterate on the problem scope and refine the questions as needed. This iterative process helps ensure that the analysis remains aligned with the business’s evolving needs and objectives.

For example, during the analysis, it may become clear that customer churn is not the only issue; instead, the company may also need to address customer dissatisfaction with product quality. Refining the problem scope ensures that the analysis is flexible and can adapt to new information.

Conclusion

Effective problem scoping is the key to a successful data analytics project. By clearly defining the business problem, collaborating with stakeholders, framing actionable questions, setting objectives, and defining the scope, analysts ensure that the analysis remains focused and relevant. Proper problem scoping also helps guide the next steps in the analytics process, including data wrangling, exploratory analysis, and ultimately, the generation of insights that drive business decisions. In the next article, we’ll dive into the Data Wrangling process, where we will explore how to clean and prepare data for analysis.