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.

翻译：商务用户尽管缺乏正式的技术培训，仍会通过数据分析来为改善业务流程和结果提供决策依据。此前的研究主要关注数据分析师和科学家的实践与挑战，但对商务用户的决策实践以及他们如何将数据与可视化分析整合到工作流程中知之甚少。为填补这一空白，我们首先对22名商务用户开展访谈研究，以了解其数据驱动决策过程中的一般实践与挑战。我们提出了一个描述商务用户数据驱动决策过程的端到端模型，详细阐述了各阶段的任务、工具和挑战。研究发现，由于各种实际限制和考量，商务用户在不依赖数据分析师的情况下自行分析数据。然而，他们现有工具存在不足，特别是在理解数据变量与业务目标之间的关系以及促进假设情景探索方面。这些发现表明，需要高级预测性与规范性分析（PPA）工具来支持假设分析。基于此需求，我们开展了后续的任务型研究，以理解PPA在商务用户决策过程中的作用与潜力。研究发现，PPA有助于提高决策效率与信心。但商务用户也认为，目前基于PPA的假设分析工具仍处于初级阶段，并报告了在完全融入决策流程前需要改进之处。基于这些发现，我们探讨了将PPA整合到数据驱动决策中的机遇与挑战，及其对未来数据与可视化分析系统的影响。