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Technology advances in data availability and processing have made it increasingly possible to base decisions on quantitative evidence. With the ever-increasing focus on digitalization and data-driven or data-informed decision making, there is growing evidence of the importance of Business Intelligence and Analytics (BIA) for organizations. Business Intelligence (BI) has a rich history with origins from early decision support systems (DSS). Various definitions of BI have been proposed, each with emphasis on different capabilities of BI, such as executive information systems, expert systems, and online analytical processing. Wixom and Watson (2010, p14) describe BI as “a broad category of technologies, applications, and processes for gathering, storing, accessing, and analyzing data to help its users make better decisions.” With the arrival of Big Data, analytics has increasingly been used to describe the more predictive and diagnostic applications that provide decision support (Davenport & Harris, 2017; Hardoon & Shmueli, 2013; Beyer & Douglas, 2012). In this work, the authors adopt a broad, inclusive and inspirational definition and denote the term BIA to technologies, applications, and processes for gathering, storing, accessing, visualizing, and analyzing data that enable enhanced insight, better decision making, and process automation.
It is currently ever more relevant to periodically assess the progress of BIA initiatives in delivering the expected value to business users. Although Higher Education Institutions (HEI) in Europe are seldom directly business-driven, such assessment is equally relevant, partly due to the perceived need to increase public investment efficiency in Higher Education (HE) as an economic good (De Wit & Broucker, 2017). On top of the economic rationale, there is also an internal drive to compete for qualified students and the best faculty who excel in the teaching programs and research projects. HEI, like other organizations, are trying to become more data-driven and make data-driven informed decisions to improve their effectiveness (Drake & Walz, 2018). In this setting, the alignment between information systems and business needs is vital to ensure the delivery of HE's public value and stand out from competing HEI. Business intelligence and analytics have been instrumental for many years in delivering this alignment. For example, BI has been applied in HEI to ensure compliance (Niño et al., 2020) and understand student learning characteristics (Kabakchieva, 2015).
It is well acknowledged that organizations that successfully deploy BIA systems follow an iterative path, starting with a rudimentary usage of data and analytical tools, and progressing to a growing sophistication of their BIA applications until the BIA data-driven culture becomes embedded in the organization's activities and decision making. The design of maturity models tries to map this progressive path, in which an organization starts with a basic or initial stage of maturity and progresses towards a more mature state. Maturity is therefore related to this notion of evolution or progression. Maturity models (MM) play an important role by reducing the uncertainty of how BIA managers perceive the BIA systems' maturity in their organizations. Some existing MM enable the possibility to benchmark one's BIA systems' performance against the average performance of other organizations in the same industry. Furthermore, MM establishes an evolution path that, with a set of recommendations, helps organizations know what to do next to achieve a higher level of maturity.
This paper presents the BEVISST case study, a BIA project implemented at the Norwegian University of Science and Technology (NTNU), in Norway. A domain-specific maturity model is used as a lens to observe and reflect on the BIA project. The findings and discussions of this case can contribute to a wider understanding of the value and adoption dynamics of BIA in HE.
The rest of the paper is organized as follows. The knowledge gap leading up to the research question is first elaborated in Background. We then introduce the HE-BIA MM and describe the case settings. In the Result section, we present the result of the MM assessment and shed light on the usefulness of the MM. We proceed to relate the findings of this study back to the prior work of others in Discussion and conclude the paper after that in the Conclusion section.