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Stating Tool Business Intelligence

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Department of Information Sciences and Technology, CIES-ISCTE—Center for Research and Studies in Sociology, ISCTE—Instituto Universitário de Lisboa, 1649-026 Lisbon, Portugal

Received: 24 March 2022 / Revised: 25 April 2022 / Accepted: 2 May 2022 / Published: 4 May 2022

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Business Intelligence and Analytics (BIA) systems play an important 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 available and usable models to guide them through the incremental development of BIA solutions to realize the full potential value of BIA. The situation has become worse as HE now operates 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 consists of three case studies of universities from different countries and two workshops with practitioners from more than ten countries. HE institutions reported that the assessment of the HE-BIA model was (i) useful and adequate for their needs; (ii) and contribute to a better understanding of the current state of their BIA landscape, explaining that a BIA program is a technological effort as well as an organizational development.

Business Intelligence and Analytics (BIA) systems play an important role in organizations, providing actionable insights that enable business users to make more informed, data-driven decisions [ 1, 2]. Conceptually, business intelligence (BI) systems integrate architectures, databases (or data warehouses), analytical tools, and applications to provide management decision support [3, 4]. The goal of BI is to provide the right information, to the right business user, at the right time and in the right context.

Traditionally, the BI component is linked to the data exploration layer, also called the BI application layer [5]. Today, BI is seen as a comprehensive concept, i.e., the complete end-to-end solution, including methods and processes that enable data collection and transformation into actionable insights used for to make a decision. The range and sophistication of BI methods and techniques have evolved over the years. Due to the growing emphasis on analytics and big data, the term business intelligence and analytics (BIA) is often 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], which enable prescriptive analysis, in addition to the usual descriptive and predictive analysis.

As the term BI has evolved throughout the years, the development of BI within an organization has typically evolved as well. This development path can be challenging, and there are factors that must be considered to ensure the success of this type of system. The literature suggests many studies on the critical success factors and capabilities of BI that organizations should seek to achieve in order to utilize the true value and impact of BIA [7, 8, 9]. An alternative approach to reflecting on the critical success aspects of BIA projects is to consider a maturity model. By design, maturity models (MM) are iterative and show a progressive path [10], where an organization starts at a basic or initial stage of maturity and progresses towards a more adult status. Maturity models are defined using a set of dimensions and a sequence of levels (or stages) that map the developmental path. The choice of dimensions of an MM is the foundation of model design. Often, these models are used as a self-assessment tool to identify strengths and weaknesses in certain areas of an organization. In other words, a MM can make an assessment of the current level of maturity in each dimension, as well as a reflection of the desired level of maturity to be achieved in the future. These models are instrumental in determining the AS-IS and TO-BE views of an organization, using maturity dimensions as key assessment areas.

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MM can be generic or domain specific. A generic model can be used across industries, enabling benchmarking. However, this approach tends to be complex, with many assessment questions and a set of terminology that does not significantly overlap with the vocabulary and definitions of a specific domain. In this paper, we focus on Higher Education (HE). In this sector, maturity models are used to assess several dimensions of HE institutions (HEIs), such as information technology (IT), process management, online learning and learning analytics. [11, 12]. Many BI-related maturity models exist in the literature [10], including models originating from academia or practice. These maturity models can be generic, such as [13, 14, 15], or specific domain, such as [16] for higher education or [17] for health care.

A previous study reported that the use of a generic model of BI maturity resulted in difficulties in properly assessing the level of BI maturity in the initiatives of different HEIs in Europe [18]. The main reason is the lack of understanding of the key concepts of BI and obstacles in finding the right set of experts in each institution who can answer correctly and with information to many different questions in MM. This result led to the decision in 2019 to develop a new maturity model specific to the assessment of BIA systems in HE. The new MM is the result of a research project carried out by two BIA professors in collaboration with the BI Special Interest Group (BI SIG) of EUNIS–the European University Information Systems organization, a non-profit organization that aims to improve the IT landscape of HEIs. through collaboration and networking [19]. The final objective of this project is to conduct a survey at European level on the level of maturity of BIA systems in Higher Education. A previous study conducted by EUNIS BI SIG was inconclusive [18]. Therefore, this research project was launched with the aim of designing a new MM, driven by the current knowledge published in the literature, and reflecting the needs and vocabulary of HE practitioners represented by EUNIS BI-SIG. Specifically, five requirements make up the research design of this project, which led to the decision to design a new BI maturity model, as opposed to using an existing one.

This paper presents the HE-BIA maturity model version 2.0 that enables Higher Education Institutions to perform a lean self-assessment of their BIA solutions. The remainder of this paper is structured as follows. We begin by discussing related work in terms of existing BIA maturity models, followed by a description of the research design. The next section presents a step-by-step description of the design science research method. Then, we presented the research artifacts, the HE-BIA maturity model v2.0 and the assessment model. In the next section, we will discuss the evaluation phase and the feedback received. The final section presents conclusions and avenues for future work.

Maturity models are built to measure the strengths and weaknesses of BI initiatives. These MMs cover several archetypal levels of maturity in a domain and can be used for assessment and organizational development. In the case of BI, several MMs have been proposed from academia and industry (practice/consulting). In the literature, we found many BI maturity models that have been used for many years, some of them for almost two decades. Table 1 presents an overview of the current BI MM critically analyzed in this study, selected according to the following criteria: the credibility of the promoter and the availability of documentation. The listed models can be used in any industry, except for the last two models [16, 17] which were developed respectively for higher education and health care.

Gartner Magic Quadrant For Enterprise Conversational Ai Platforms 2023

A comparative analysis is made of selected maturity models, taken as reference to

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