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.