Business Intelligence Maturity Model Research Articles

A Systemic Mapping Study of Business Intelligence Maturity Models for Higher Education Institutions

Higher education institutions (HEIs) are investing in business intelligence (BI) to meet the increasing demand for information stemming from their operations. Information technology (IT) managers in higher education may turn to BI maturity models to evaluate the current state of HEIs’ BI operation capabilities and evaluate the readiness for future improvements. However, generic BI maturity models do not have domain-specific attributes that ensure a high degree of compatibility with HEIs. This study’s objective is to survey maturity models that could be used in HEIs and identify those used for BI to perform an analysis of their qualities and identify future avenues for research into HEI-specific BI maturity models. A systemic mapping was undertaken via both a keyword and snowball search of five indexing services, 6037 articles were processed using inclusion and exclusion criteria resulting in the identification of forty-one academic works regarding maturity model uses which were mapped to ten categories. The mapping reveals an increasing number of publications featuring maturity models for HEI, particularly since 2018, focused on e-learning and ICT. A single instance of a BI maturity model for HEI emerged in 2022 within the European HEI context. The HE-BIA MM has more dimensions than most other models identified, yet only a single co-occurrence of dimensions was identified in name only. We conclude that BI maturity models for HEI are emerging as a field of research with future directions for research including exploring co-occurrence of dimensions with existing maturity models, performing case studies, and validation of HE-BIA MM outside the European HEI context.

Business intelligence addressing service quality for big data analytics in public sector

<span>With the inauguration of Big Data Analytics initiative nationally, many nations have participated and paved way for BDA ecosystem. The initiative is a catalyst to further encourage economic growth in Public Sectors. Some of the common key deliverables identified are increasing productivity involving information communications technology, cost savings, shared benefits, and encourage innovation. The objectives can be further elaborated by driving big data analytics demands in various public sectors agency, adopting big data analytics framework supporting the building of big data industry. This has encouraged talents and startup companies inspiring their capabilities by developing various technology platform, collaborate and innovate amongst public and private sectors, and further strengthen data governance by creating policy and procedures. With the establishment of big data analytics framework, performance measurement can be enforced effortlessly using the principles of business intelligence maturity model and the technological stack comes with it. Various data sources can be used to benchmark service quality using advanced analytics and data science techniques.</span>

Business intelligence for patient-centeredness: A systematic review

This study utilized a systematic review to provide an overall understanding of how academic research can be incorporated into business intelligence (BI) to ensure patient-centeredness (PC). Using the BI maturity model, this study analyzed findings of previous studies from four time periods within the period 2000–2016 to determine how BI can facilitate PC through organization, human-orientation, and technology, as well as other PC-specific conditions. Our results indicate that the number of BI applications that include PC have continued to grow since 2010, and that they primarily focus on the dimensions of organization, humanism, and PC-specific conditions; additionally, we noted that a time-based correlation exists between the related results. This study then explored the extent to which BI supports the subdimensions of PC (e.g., principles, enablers, and activities). Finally, future research focuses and directions were proposed.

Big Data Analytics Initiatives Using Business Intelligence Maturity Model Approach in Public Sector

Big Data requirement in Public Sector is gaining focus in many countries. Described in the Eleventh Malaysia Plan 2016–2020, Big Data Analytics (BDA) has been identified as key focus for Information and Communications Technology (ICT). Especially with the recent participation of Trans-Pacific Partnership Agreement, Malaysia will be competing amongst other nations to make quick and accurate decision contributing to economic and GDP growth. With good understanding and guided implementation framework of Business Intelligence, right and agile decision can be made using the various available information. Malaysia Public Sector requires ICT involvement in Business Intelligence domain to perform Big Data Analytics. With evolving of data availability and needs for building correlation with different sets of data, Big-Data offers an emerging space to analyze wealth of information form structure, semi-structured and un-structured data set. Business Intelligence provides an organization the insight required to make the key and critical decision for leading competitive edge.

Assessing the Organizational Readiness for Implementing bi Systems

Implementation of business intelligence systems (BIS) is very complex and requires a lot of resources and time. Business intelligence (BI) is a difficult concept and has a multi-tier architecture. The metadata causes the complexity of the BI. That is why a BI readiness model is required. The frequency of BI maturity models, such as the data warehousing (TDW), has been provided, but there are few frameworks for measuring the readiness. Moreover, readiness frameworks often provide a general model for all organizations. Hence, the objective of this study was to examine whether the factors affecting the organizational readiness for BI implementation in all organizations are identical. For this purpose, based on a comprehensive literature review, four factors of culture, people, strategy, and management were extracted as the most important factors affecting the readiness and implementation of BI, and they were studied in three educational, commerce, and IT organizations. Based on the findings, different factors affect various organizations, and using a general model should not be advised.

A framework for developing a domain specific business intelligence maturity model: Application to healthcare

The implementation of electronic health records has resulted in a rapid accumulation of data. Healthcare organizations can use business intelligence (BI) technologies to leverage the data and improve operational and clinical efficiency. Approaches to understanding BI readiness are needed for organizations to develop an overall BI strategy. While there are a number of BI maturity models, they are often generic and do not meet the industry specific requirements. This research proposes a framework for developing a domain specific BI maturity model. The research further demonstrates the efficacy of the framework by applying it to the development of a BI maturity model in healthcare. The results indicate that the framework is able to address the needs of a domain specific BI maturity model, and guide the development of such model that proved acceptable to expert practitioners in the field.

AN EMPIRICAL ANALYSIS ON BUSINESS INTELLIGENCE MATURITY IN MALAYSIAN ORGANIZATIONS

One of the greatest challenges organizations face today is the lack of infrastructure management. With the increased complexity and information overload, it is difficult for organizations to make quick and informed decisions at the pace required by today’s rapidly changing market. Business intelligence (BI) represents a significant opportunity to improve decision making by providing right information in a timely manner at the right place for the right people. Doing this requires the right management of infrastructure (e.g., people, processes, functionalities) to ensure that the BI environment meets the business expectations and lift BI capabilities to higher level. This researchin-progress paper focuses on an empirical analysis of a proposed BI maturity model among Malaysian organizations. This maturity model comprises of four dimensions: organizational, process, technology, and outcome, spanning across five levels of maturity. An empirical study was undertaken with the development of a structured questionnaire to examine the efficiency of each dimension of BI maturity. Study results show that outcome and organizational dimensions have a higher impact on the BI maturity level of Malaysian organizations. Results also indicate that none of the organizations in Malaysia have achieved level 5 and most of them are still at low level of maturity across all the four dimensions that built into the BI maturity model. Through the understanding of dimensions that affect current level of BI maturity, effective BI roadmaps can be formulated to assist the organizations to make necessary improvement in BI capabilities and keep them productively engaged for a long-term in achieving sustained high performance.

A framework for accessing an enterprise business intelligence maturity model (EBI2M): Delphi study approach

The implementation of enterprise-scale business intelligence (EBI) system is a complicated process involving considerable resources and numerous stakeholders over a lengthy time. To date, there has been only limited formal research work that explores the various key dimensions affecting EBI implementation efforts. This shortcoming arises from the fact that the business intelligence (BI) market is a relatively new phenomenon, with most of the pioneering work being driven in an informal, ad-hoc basis by various vendors in the IT industry. Therefore, the goal of this research is to seek to bridge the gap that exists between academia and practitioners. In particular, the research work will draw upon the maturity constructs underlying the prominent capability maturity model integration (CMMI) developed at Carnegie Mellon University. The driving motivation is to develop an EBI maturity model (EBI2M) that can serve as a useful guideline for enterprises which are planning, or undertaking large-scale EBI initiatives. In the first stage, the author utilises a Delphi study to conduct three rounds of enquiries with a panel of BI experts, and then refines the research into a preliminary EBI2M model. However, reliance on the Delphi study alone is not sufficient for the collection of data needed to rigorously address the research objective. Therefore, based on the research constructs and findings of Delphi study, multiple case studies will be carried out in the future. Key words: Business intelligence, maturity model.

Business Intelligence Excellence: A Company’s Journey to Business Intelligence Maturity

This paper is a case study which describes a company’s Business Intelligence (BI) journey. Although Business Intelligence is seen as priority by many companies the level of benefits achieved varies significantly. Often this is dependent on the level of Business Intelligence maturity. This paper documents a company’s trials and tribulations with their BI implementations. The paper maps and discusses the company’s Business Intelligence best practices. KEYWORDS; Business Intelligence, Business Performance, Case Study INTRODUCTION Companies today have all come to realise the importance of providing accurate, relevant and timely information— information that allows their organisational personnel to engage in effective decision-making practices. Accenture interviewed 163 executives from large enterprises around the world to identify how companies were using Enterprise Resource Planning (ERP) systems to improve business performance and the specific practices that resulted in sustained value creation (Davenport et al, 2003). They found that the implementation of an enterprise-wide information system resulted in sustained value creation however; some companies realised far more comparable benefits than others. A more extensive follow up study in 2006 involving 450 executives from 370 companies identified the factors that drove value from their ERP system, as well how companies used these systems to enhance competitiveness and differentiation (Harris and Davenport, 2006). One of the key findings from this study was that improved decision making was the most desired benefit. Related to this finding was that the top performing companies aggressively used information and analytics to improve their decision making. These findings are supported by Gartner, a leading business analyst firm, who conducted a worldwide survey of 1,500 Chief Information Officers and identified Business Intelligence (BI) as the number one technology priority for companies, followed by ERP systems (Gartner, 2008). In a Cutter Consortium Report (2003), a survey of 142 companies it was found that 70% of the respondents had implemented data warehousing and Business Intelligence initiatives. The increased focus on BI is reflected in the forecasted BI vendor revenue expected of $7.7 billion by 2012 (Sommer, 2008). Although Business Intelligence is seen as a priority for many companies to survive in a competitive market there is uncertainty as to the path to follow. Researchers have identified that companies utilise Business Intelligence in different ways, with varying levels of success. A review of literature indicates that a number of companies often fail to realise expected benefits of Business Intelligence and sometimes consider the project to be a failure in itself (Chenoweth et. al., 2006; Hwang et al., 2004; Johnson, 2004; Arte 2003; Adelman and Moss 2002). Gartner predicted that more than half of the Global 2000 enterprises would fail to realise the capabilities of Business Intelligence and would lose market share to the companies that did (Dresner et al, 2002). A survey of 142 companies found that 41 percent of the respondents had experienced at least one Business Intelligence project failure and only 15 percent of respondents believed that their Business Intelligence initiative was a major success (Cutter Consortium Report, 2003). Moss and Atre (2003) indicated that 60% of Business Intelligence projects failed due to poor planning, poor project management, undelivered business requirements, or of those that were delivered, many were of poor quality. A number of authors believe that in many Business Intelligence projects the information that is generated is inaccurate or irrelevant to the user’s needs or indeed, delivered too late to be useful (Ballou and Tayi, 1999; Strong et al., 1997). Researchers have attempted to map Business Intelligence usage and best practices to provide a roadmap for companies to move forward in an attempt to maximise the benefits of their Business Intelligence initiatives. One approach for this roadmap has been the development of Business Intelligence Maturity Models (Watson et al, 2001; McDonald, 2004; Hamer, 2005; Eckerson, 2007, ASUG, 2007; Hewlett Packard, 2007). The purpose of these models is to provide companies with a roadmap to improve the management of their corporate data, as well as maximise the benefits obtained from Business Intelligence. Each year Gartner identifies companies for Business Intelligence Awards of Excellence. It would be reasonable to expect that a company which achieved such an award would be very mature as per the model. The remainder of the paper contains a case study documenting a company’s BI journey. This same company was a recent of the Gartner Business Intelligence Award of Excellence, (alias Acme Manufacturing).

A review of business intelligence and its maturity models

Summary of various maturity models. Maturity model Description TDWI’s maturity model (i) The maturity assessment tool is available in the web to evaluate BI’s maturity level as well as documentation. (ii) Concentrates on the technical viewpoints especially in data warehouse aspect. (iii) Can be improved on business viewpoint especially from the cultural and organizational view. Business intelligence maturity hierarchy (i) Applied the knowledge management field (ii) Author constructed maturity levels from a technical point of view but can considered as incomplete. (iii) The documentation of this model in the form of one paper and is not enough for maturity level assessment. Hewlett package business intelligence maturity model (i) Depicts the maturity levels from business technical aspect. (ii) This model is new and need to improve to add more technical aspects such as data-warehousing and analytical aspects. Gartner’s maturity model (i) Uses to evaluate the business maturity levels and maturity of individual departments. (ii) Provides more non technical view and concentrates on the business technical aspect. (iii) Well documented and can search easily on the Web. (iv)The assessment offers the series of questionnaire to form of spreadsheet. Business information maturity model (i) Well documented with the series of questionnaire to assist the users to perform self evaluation. (ii) However, criteria to evaluate the maturity level are not well defined. AMR research’s business intelligence/ performance management maturity model (i)Concentrates on the performance management and balanced scorecard rather than business intelligence. (ii)Not well documented and criteria to evaluate the maturity level are not well defined. (iii) No questionnaire to evaluate the maturity levels and is very hard to analysis the model (Rejteric, 2010). Infrastructure optimization maturity model (i) Focuses on the measurement of the efficiency of reporting, analysis and data-warehousing and is not complete in the business intelligence area (Rejteric, 2010). (ii) Discuss about the products and technologies rather than business point of view (Rejteric, 2010). (iii) Not well documented and criteria to evaluate the maturity level are not well defined. Ladder of business intelligence (LOBI) (i) Apply the knowledge management field (ii) Author constructed maturity levels from a technical point of view but can considered as incomplete. (iii) Not well documented and criteria to evaluate the maturity level are not well defined. Busin ess intelligence deve lopment model (BIDM) ( i) N ot w l dc um narvh y f .( i) C onc etr a sh pbu f v w