Self-service business intelligence and analytics (SSBIA) empowers non-IT users to create reports and analyses independently. SSBIA methods and processes are discussed in the context of an increasing number of application scenarios. However, previous research on SSBIA has made distinctions among these scenarios only to a limited extent. These scenarios include a wide variety of activities ranging from simple data retrieval to the application of complex algorithms and methods of analysis. The question of which dimensions are suitable for differentiating SSBIA application scenarios remains unanswered. In this article, we develop a taxonomy to distinguish among SSBIA applications more effectively by analyzing the relevant scientific literature and current SSBIA tools as well as by conducting a case study in a company. Both researchers and practitioners can use this taxonomy to describe and analyze SSBIA scenarios in further detail. In this way, the opportunities and challenges associated with SSBIA application can be identified more clearly. In addition, we conduct a cluster analysis based on the SSBIA tools thus analyzed. We identify three archetypes that describe typical SSBIA tools. These archetypes identify the application scenarios that are addressed most frequently by SSBIA tool providers. We conclude by highlighting the limitations of this research and suggesting an agenda for future research.

自助式商业智能和分析 (SSBIA) 使非 IT 用户能够独立创建报告和分析。 SSBIA 方法和流程是在越来越多的应用场景的背景下讨论的。然而，先前对 SSBIA 的研究仅在有限程度上对这些情景进行了区分。这些场景包括各种各样的活动，从简单的数据检索到复杂算法和分析方法的应用。哪些维度适合区分SSBIA应用场景仍然没有答案。在本文中，我们通过分析相关科学文献和当前的 SSBIA 工具以及在一家公司进行案例研究，开发了一种分类法，以更有效地区分 SSBIA 应用程序。研究人员和从业者都可以使用这种分类法来更详细地描述和分析 SSBIA 场景。这样，可以更清楚地识别与SSBIA应用相关的机遇和挑战。此外，我们基于如此分析的SSBIA工具进行了聚类分析。我们确定了描述典型 SSBIA 工具的三种原型。这些原型确定了 SSBIA 工具提供商最常解决的应用场景。最后，我们强调了这项研究的局限性，并提出了未来研究的议程。