Effects of Data Envelopment Analysis on Performance Assessment : A Cognitive Approach

Business Intelligence (BI) and Business Analytics (BA) are very fast moving fields which have evolved greatly from fields such as Data Mining and Knowledge Discovery in Databases (KDD). Today the focus of much research in BI centers on the notion of the BI software platform and its analytic capabilities. The implications of the term business intelligence are much broader, however, and indicate a process through which an organization builds intelligence and awareness of its operations and context which ultimately facilitates decision making that makes the organization more competitive (Jourdan, Rainer, & Marshall, 2008). With this definition in mind, therefore, it is clear that BI could not and should not be limited to only software analytics, but rather should be expanded to include complimentary strategies that achieve the same aims of building organizational ’intelligence’. This perspective is supported by the finding that, among critical success factors for BI implementations, organizational issues such as the presence of a business vision were rated as more important than data and infrastructure issues according to experts (Yeoh & Koronios, 2010). Through utilizing strategies from other areas along with Software Analytics (SA) we can engineer stronger BI processes. Parallels in complimentary areas become more apparent if we conceptualize BI as a process that rests on three elements, similar to the widely used three-phase analytics continuum (Davenport & Harris, 2007): description (understanding the past), prediction (anticipating the future) and prescription (decision making informed by the previous two that maximizes organizational competitiveness). One such complimentary area with similar goals is Strategic Foresight (SF), which focuses on the, “identification, assessment and usage of weak signals to recognize and give warning about threats and opportunities at an early stage” (Rohrbeck, Arnold, & Heuer, 2007). Strategic Foresight also, “defines the methods, the actors, the process and the system needed to enhance the competitive position of a company” (Rohrbeck, Arnold, & Heuer, 2007). Strategic Foresight thus presents many techniques that are complimentary to software analytics. In what follows, we propose a convergence framework for BI based on software analytics and strategic foresight which uses the well-established KDD process outline as a foundation. The motivation is to capitalize on the relative strengths of each area and thereby build a more robust BI process that ensures more critical success factors are met and that more impactful analytics takes place.