Business Intelligence and Analytics (BI&A) systems have demonstrated their potential to enhance decision making; however, the linkage between BI&A and decision support systems (DSS) has been contested by some, if not completely denied by others. In this research, we investigate the foundations of BI&A by using foundational literature on DSS to open the ‘black box’ of BI&A systems. We argue that BI&A is fundamentally a subfield of DSS that is seeking to convert more data into deeper insight, but it has lost its connection to DSS literature and, thereby, missed research opportunities. In this paper, we first define DSS and BI&A and then present a systematic review of foundational DSS literature to assess their leveraging in BI&A research. By classifying cited DSS articles and citing BI&A articles into four areas: conceptual framework, design & implementation, business value & organizational use, and cognition & decision making, potential research for BI&A is uncovered. We reconcile these two research streams by mapping BI&A frameworks to classical DSS components through interviews with practitioners. The result is formulated as a comparative, process-level architecture for converting data into insight. New research opportunities for BI&A are suggested motivated by foundational DSS literature.

商业智能和分析（BI＆A）系统已经证明了其增强决策能力的潜力。但是，BI＆A与决策支持系统（DSS）之间的联系受到了某些人的质疑，甚至没有被其他人完全否定。在这项研究中，我们通过使用有关DSS的基础文献来打开BI＆A系统的“黑匣子”，来研究BI＆A的基础。我们认为BI＆A从根本上说是DSS的一个子领域，该领域正在寻求将更多数据转换为更深刻的见解，但它已失去了与DSS文献的联系，因此错过了研究机会。在本文中，我们首先定义了DSS和BI＆A，然后提出了对基础DSS文献的系统评价，以评估它们在BI＆A研究中的作用。通过将引用的DSS文章分类并引用BI＆A文章分为四个领域：概念框架，设计与实现，业务价值与组织使用，认知与决策，BI＆A的潜在研究被发现。通过与从业人员的访谈，将BI＆A框架映射到经典的DSS组件，我们协调了这两个研究流。结果被表述为用于将数据转换为洞察力的可比较的过程级体系结构。DSS的基础文献提示了BI＆A的新研究机会。结果被表述为用于将数据转换为洞察力的可比较的过程级体系结构。DSS的基础文献提示了BI＆A的新研究机会。结果被表述为用于将数据转换为洞察力的可比较的过程级体系结构。DSS的基础文献提示了BI＆A的新研究机会。