  Business users perform data analysis to inform decisions for improving
business processes and outcomes despite having limited formal technical
training. While earlier work has focused on data analysts' and data scientists'
practices and challenges, little is known about business users' decision-making
practices and how they incorporate data and visual analytics into their
workflows. To address this gap, we first conduct an interview study with 22
business users to understand the general practices and challenges in their
data-driven decision-making processes. We contribute an end-to-end model of
business users' data-driven decision-making processes elaborating the tasks,
tools, and challenges at each stage. We find that business users analyze data
without relying on data analysts due to various practical constraints and
considerations. However, their existing tools are inadequate, particularly in
helping understand the relationship between data variables and business goals
and facilitating the exploration of what-if scenarios. These findings suggest a
need for advanced predictive and prescriptive analytics (PPA) tools to support
what-if analysis. Motivated by this need, we perform a follow-up, task-based
study to understand PPA's role and potential in business users' decision-making
processes. We find that PPA helps improve efficiency and confidence in
decision-making. However, business users also believe that PPA-powered what-if
analysis tools are currently in their nascent stages and report improvements
before fully integrating them into their decision-making processes. Building
upon these findings, we discuss the opportunities and challenges in
incorporating PPA into data-driven decision-making and its implications for
future data and visual analytics systems.
