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Problem Definition

In: Predictive Analytics with KNIME

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  • Frank Acito

    (Indiana University)

Abstract

This chapter emphasizes the critical role of problem definition in the analytics process. Before collecting data or selecting analytical techniques, it is crucial to understand the business problem at hand thoroughly. Failure to do so often leads to the downfall of analytics projects. This chapter highlights the benefits of meticulously defining the problem, including improved efficiency, focused data collection, and the generation of valuable insights. Moreover, a well-defined problem sets the foundation for adding real value to an organization through analytics. Expert perspectives on problem definition are presented, underscoring the importance of asking the right questions and framing the problem appropriately. Various quotes from industry professionals emphasize the significance of problem definition in differentiating between successful and mediocre data science endeavors. The chapter provides a telecom-related case study on predicting customer churn to illustrate the benefits of careful problem definition. Through detailed questioning and understanding of customer behavior, the root cause of churn is identified, leading to a more effective solution that addresses the underlying issue. Defining the analytics problem is systematically broken down into tasks, such as determining business objectives, translating them into measurable metrics, identifying stakeholders, developing a comprehensive project plan, and carefully framing the problem. The chapter advocates for structured problem definition and introduces techniques like right-to-left thinking, reversing the problem, asking “whys,” and challenging assumptions to arrive at a clear, well-rounded problem statement. The chapter concludes by emphasizing the significance of investing time and effort in problem definition to increase the likelihood of successful analytics projects with meaningful results. It highlights the importance of various tools and strategies that aid in problem framing and exploration, ensuring that analytics efforts align with the organization’s goals and drive impactful outcomes.

Suggested Citation

  • Frank Acito, 2023. "Problem Definition," Springer Books, in: Predictive Analytics with KNIME, chapter 0, pages 11-19, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-45630-5_2
    DOI: 10.1007/978-3-031-45630-5_2
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