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Regardless of size or business domain, companies rely on relevant information to monitor their business activities and to support decision making (Papachristdoulou, Koutsaki, & Kirkos, 2017). Business Intelligence (BI) is used as an umbrella term to cover various technological tools and organizational activities that help decision makers make data-driven decisions and turn business insight into actions (Kumar, Chauhan, & Sehgal, 2012; Lavalle, Hopkins, Lesser, Shockley, & Kruschwitz, 2010; Trieu, 2016). Wixom and Watson (2010) define BI as “a broad category of technologies, applications, and processes for gathering, storing, accessing, and analysing data to help its user make better decisions” (p. 4). BI became established in the 1990’s, and a more recent focus on its key analytical component has become known as Business Analytics (BA), which also encompasses big data and big data analytics. This may be understood as a subfield of BI (Davenport & Harris, 2007) or an advanced discipline in itself (Laursen & Thorlund, 2010). We use the term Business Intelligence and Analytics (BI&A) suggested by Chen, Chiang, & Storey (2012) to indicate our focus on technologies, applications, processes and analytics. Research has addressed different aspects of BI&A, including Cloud BI, mobile BI and various BI applications (Llave, 2017), and reported transformational success stories. However most of these successes involve large companies: Continental Airlines (Anderson-lehman, Watson, & Wixom, 2008), Netflix (Valacich & Schneider, 2010) or Target (Sharda, Delen, & Turban, 2014). When it comes to small- and medium sized enterprises (SME’s) the published work is limited, even though SME’s constitute the backbone of national economies (99% of all European companies are categorized as small or medium sized (Airaksinen, Luomaranta, Alajääskö, & Roodhuijzen, 2015). This research gap has been addressed in literature (Grabova, Darmont, Chauchat, & Zolotaryova, 2010; Llave, 2017; Scholz, Schieder, Kurze, Gluchowski, & Böhringer, 2010), but not substantially addressed even though it has been pointed out that both researchers and practitioners need better understanding on how organizations get value from BI&A (Trieu, 2016). In a comprehensive literature review of BI&A and analytics in SME’s from 2000 to 2016, Llave (2017) showed that popular topics included data warehousing, dashboards, data mining, cloud services and BI&A implementation. However, the relevant research was sparse: nine articles in 2000 focused on BI adoption and three on BI&A benefits for SME’s (Llave, 2017), only three from 2015 and seven from 2016 covered any BI&A topic. Recent interest in big data has refocused research attention on intelligence and analytics, but SME’s are still neglected.