###### Abstract  
Business Intelligence and Analytics (BIA) systems play an essential role in organizations, providing actionable insights that enable business users to make more informed, data-driven decisions. However, many Higher Education (HE) institutions do not have accessible and usable models to guide them through the incremental development of BIA solutions to realize the full potential value of BIA. The situation is becoming ever more acute as HE operates today in a complex and dynamic environment brought forward by globalization and the rapid development of information technologies. This paper proposes a domain-specific BIA maturity model (MM) for HE-the HE-BIA Maturity Model. Following a design science approach, this paper details the design, development, and evaluation of two artifacts: the MM and the maturity assessment method. The evaluation phase comprised three case studies with universities from different countries and two workshops with practitioners from more than ten countries. HE institutions reported that the assessment with the HE-BIA model was (i) useful and adequate for their needs; (ii) and contributed to a better understanding of the current status of their BIA landscape, making it explicit that a BIA program is a technology endeavor as well as an organizational development. m _Applied_  
Designing a Business Intelligence and Analytics (BIA) systems play an essential role in organizations, providing actionable insights that enable business users to make more informed, data-driven decisions [1, 2]. Conceptually, business intelligence (BI) systems combine architectures, databases (or data warehouses), analytical tools, and applications to provide managerial decision support [3, 4]. The goal of BI is to provide the right information, to the right business users, at the right time and with the right context. Traditionally, the BI component was linked to the data exploration layer, also called the BI applications layer [5]. Nowadays, BI is seen as a comprehensive concept, i.e., the complete end-to-end solution, including the methods and processes that enable data collection and transformation into actionable insights used for decision making. The range and sophistication of BI methods and techniques have evolved through the years. Due to the growing emphasis on analytics and big data, the term business intelligence and analytics (BIA) has consistently been used to comprehensively and more accurately describe contemporary data-driven decision support systems [2]. The use of artificial intelligence (AI) techniques has led to a new generation of AI-enabled BI tools [6], enabling prescriptive analysis, in addition to the usual descriptive and predictive analysis. As the BI term has evolved throughout the years, BI development inside an organization typically also progresses iteratively. This development path can be challenging, and there are factors that need to be considered to ensure the success of this type of system. The literature suggests several studies on critical success factors and BI capabilities that organizations should seek to achieve in order to leverage the true value and impact of BIA [7; 8; 9]. An alternative approach to reflect on the critical success aspects of BIA projects is to consider a maturity model. By design, maturity models (MM) are iterative and showcase a progressive path [10], in which an organization starts with a basic or initial stage of maturity and progresses towards a more mature state. Maturity models are defined using a set of dimensions and a sequence of levels (or stages) mapping the progression path. The selection of dimensions in a MM is the foundation of the model design. Typically, these models are used as a self-assessment tool to identify the strengths and weaknesses of certain areas in an organization. In other words, a MM enables an assessment of the current maturity level in each dimension, as well as a reflection on the desired maturity level to be achieved in the future.