Article Preview
Top1. Introduction
Most organizations are operating in a business climate of intense competition, where management must pay close attention to the whereabouts of a myriad actors. Although this is a daunting task, there are no shortcuts to maintaining a competitive edge. Organizations that neglect to do this are at severe risk of being overwhelmed by competitive forces.
In order to maintain their edge, organizations need advanced systems that are able to present a holistic view of the organization and its business in a timely and accurate manner. Various (often overlapping) solutions have been proposed in the literature, involving concepts such as business intelligence, decision support systems (DSS), analytics, and big data. For this work, we have used the concept business intelligence (BI), since it has attracted enormous interest in recent decades and is still regarded as the gold standard by many chief information officers (Watson & Wixom, 2007, Kappelman et al., 2013).
However, even the concept of BI is defined in different ways. Given that this work has a very specific focus on the data aspect of BI solutions, we have adopted Wixom and Watson’s definition of a BI solution as “a broad category of technologies, applications, and processes for gathering, storing, accessing, and analyzing data to help its users make better decisions” (Wixom and Watson, 2010, p. 14). BI could also be described very simply as getting data in and getting data out. The part of the system architecture supporting getting data in is referred to as the back-end.
BI projects have traditionally focused on incorporating data from systems that are internal to the organization. The benefits of using data acquired from outside the organization (external data) have seldom been considered (Devlin, 1997). For clarity, external data in this case refers to data that is incorporated from systems/suppliers external to the organization. Ram et al. (2016, p.221) claim that “currently, BI-solutions mainly focus on structured and internal data of an enterprise. As a result, a lot of valuable information embedded in unstructured and external data remains hidden, which could potentially lead to an incomplete view of the reality and resultantly biased business decision-making.” Yet external data may contribute to other insights or show other facets of a customer, a product, or a competitor, and so its importance has long been recognized (see, for example, Kelly, 1996; Alavi & Haley, 1997; Devlin, 1997; Chen & Frolick, 2000; Huang et al., 2002; Inmon, et al., 1997; March & Hevner, 2007; Anderson-Lehman et al., 2008; Ponniah, 2010; Poleto et al., 2017; Jukic et al., 2017).
Although the literature strongly emphasizes the importance and benefits of external data in relation to BI (and to data warehousing, business analytics, and big data), much remains to be learned. The literature lacks detailed accounts of how external data have been incorporated in fully functional systems, aiding decision-makers in business contexts. In addition, the existing scientific literature is mostly very general or rather old (as seen above, some published more than two decades ago). Some of this literature was included in this work, partly to illustrate the need for more up-to-date knowledge but also because, despite our best efforts, we have not managed to find more recent literature. While white papers, practitioner journals and consultant resource web pages (e.g. Hendler, 2014, Page, 2015, Weil, 2015) may contribute with some details, they are often rather conceptual, software specific, and not externally validated.