Predictive and Prescriptive Analytics in Business Decision Making: Needs and Concerns
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Business users make data-informed decisions by understanding the relationships between their data and business outcomes, typically tracked by key performance indicators (KPIs). To this end, they evaluate different decision scenarios, often using what-if analyses, which can benefit from predictive and prescriptive analytics (PPA). However, incorporating PPA into data analysis systems in a way that is easy to use and understand by business users can be challenging. Numerous studies have examined how data analysts use simple predictive analytics. However, there is little systematic understanding and empirical data on business users' analysis practices for decision-making and the effectiveness of various PPA affordances in facilitating their analysis. To address this gap, we conduct a study with 22 business users to better understand their workflows and elicit feedback on the effectiveness of four example PPA functionalities in business data analysis. We find that business users can't depend on data analysts for their data analysis due to four reasons: the limited availability of data analysts in enterprises, the inefficiency of communicating with data analysts, the business pressure to make quick decisions, and the importance of incorporating domain knowledge, which data analysts often lack. We also find that business users rely on interactive manual analysis using spreadsheet-like applications, which makes coping with large, live, and complex datasets difficult. While business users don't use PPA directly, their data analysis needs suggest that they can benefit from it. We also find that PPA functionalities can increase business users' decision-making speed and confidence. Additionally, our results suggest that they need help in PPA-driven analysis for preparing datasets, understanding the risk factors of recommendations, and incorporating their domain knowledge.
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