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Business decisions, once the realm of intuition and anecdotal experience, are being reinvented by modern organizations using data. Business Intelligence and Analytics (BI&A) encompasses the technologies, systems, practices, techniques, and methods that analyze data to help organizations understand themselves, their markets and their customers, in order to make business decisions (Chen, Chiang, and Storey, 2012). Mature BI&A systems can be the basis for making effective decisions, improving performance, and exploiting new opportunities (Olszak, 2016). Organizations attempt to improve and expand their BI&A capabilities to advance their competitive position. There has been much press coverage, with exemplars such as Google and Amazon, as well as significant investment across industries. However, the majority of organizations (over 87% in one study) are considered as having “low maturity” (Chien 2018), and failing to reach their strategic goals (Grover et al., 2018).
Progressing from stand-alone analytics projects to a BI&A capability that drives decisions across an organization, requires more than big databases and powerful processing capabilities. Competitive advantage requires maturity across many dimensions: collection and analysis of data (Delen and Demirkan, 2013); sophistication of data consumers (Kiron and Shockley, 2011); alignment with organization strategies (Hribar Rajterič, 2010); and cultural commitment of an organization (Davenport, Harris, De Long, and Jacobson, 2001). However, current maturity models focus on specific components (Lahrmann et al., 2011; Foshay et al., 2015; Chen and Nath, 2018). Although various BI&A maturity models have been proposed (Larhmann et al., 2010), they often lack theoretical foundations, empirical validation, and the ability to operationalize maturity measurements (Lasadro, Vatrapu, and Andersen, 2015). Instead, they typically focus on improving technological, business-technical alignment, or analytical capabilities, with little emphasis on the main value proposition of competitive advantage (Chen and Nath, 2018).
Organizations use BI&A system to sense and respond to market needs by focusing on automation and cost reduction, optimization, profitability and exploring newer avenues (LaValle et al., 2011). While the aim is to be competitive, the purchase or development of a BI&A tool alone is not sufficient (Siow, Tiropanis, and Hall, 2018). Many questions arise about the organizational resources and capabilities that influence successful deployment of analytical capabilities (Kohli and Tan, 2016; Abbasi, Sarker, and Chiang, 2016). These include the: role of executive leadership and information technology (IT) departments; data needs and how they can be made available to stakeholders; organizational transformation from an intuition-based decision-making culture to a data-driven one; potential instrumental and humanistic outcomes; and main factors influencing business value for real-time decision making using big data. Organizational resources and capabilities have an immense influence on the success of using a BI&A system to gain competitive advantage. Therefore, to derive value from a BI&A initiative, an organization needs to ensure that its initiative is aligned with its organizational context.