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Top1. Introduction
Several organizations have a vision to become data-driven (Davenport & Bean, 2018; Halper & Stodder, 2017; Watson, 2016), since those type of organizations are likely to capitalize on business insights more frequently than organizations that are not data-driven (LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011). Halper and Stodder (2017) classify an organization as data-driven “when it uses data and analysis to help drive action—even if that action is a deliberate inaction.” In theory, data-driven organizations can apply data-driven decisions for all types of analytics (descriptive, predictive, prescriptive), and all types of decisions (operational, tactical, strategical). In practice, we assume that most organizations aim for a subset of combinations of analytics and decisions.
Managers have taken several steps to initiate transformations to a data-driven organization, by introducing mantras such as - business insights are based on data and not opinions - into strategy documents, held large kick-off events, educated employees in Self-Service Business Intelligence (SSBI) tools, and hired data scientists and AI-programmers. Despite these good intentions, most of the organizations still struggle and few of them seem to reach their vision. In two recent surveys (Bean & Davenport, 2019; Halper & Stodder, 2017) roughly 30% of the organizations had made a successful shift to be data driven. The other organizations struggled with their barriers or had not started to move towards a data-driven culture. According to Halper and Stodder (2017), the biggest barrier to being data-driven was “lack of business executive support/corporate strategy” (42% of 264 respondents), and the most frequently mentioned step managers took to develop a data-driven culture was “make the case to corporate leadership to invest in BI and analytics” (57% of 230 respondents). In response to the low share of organizations that make a successful shift to become data-driven, Davenport and Bean (2018) suggested that organizations “… need more concerted programs to achieve data-related cultural change”.
Change management (Moran & Brightman, 2001; Todnem By, 2005) has previously been identified as a success factor for implementing business intelligence systems (Olszak & Ziemba, 2012; Pham, Mai, Misra, Crawford, & Soto, 2016; Yeoh & Koronios, 2010). As the area of business intelligence is closely related to data-driven organizations and analytics, change management has also been suggested in the literature (Berndtsson, Forsberg, Stein, & Svahn, 2018; Forbes-Insights & EY, 2015) as an enabler for establishing a data-driven organization. In a survey of 564 senior executives, conducted by Forbes Insight and EY, 59% of the respondents that considered themselves as top-performing, claimed that change management was “extremely important” to the organizations’ overall analytics initiative (Forbes-Insights & EY, 2015). Hence, change management has an important role to play when organizations intend to scale up their usage of analytics. However, none of the sources provide any details on how such a road map or program, inspired by change management may look like.
The objective of this paper is to investigate how 13 organizations started their journeys towards becoming data-driven, given previously reported barriers and potential usage of change management as an enabler. This paper is also a response to the recommendation by Arnott and Pervan (2014), to increase the usage of case studies within the field of decision support systems, as an approach to improve the relevance of conducted research.
In the remainder of this paper, we present a brief introduction to data-driven organizations and related barriers. Thereafter, we present our research approach. In the succeeding sections, we present our findings. Finally, related work and conclusions are presented.