Successful business intelligence implementation: a systematic literature review

Journal of Work-Applied Management

ISSN : 2205-2062

Article publication date: 18 October 2019

Issue publication date: 14 November 2019

The purpose of this paper is to present a systematic literature review to determine the factors that relate to successful business intelligence (BI) system implementation.

Design/methodology/approach

The study has a collection of literature that highlights potential references in relation to factors for system implementation in relation to BI. There is the employment of “content analysis”, given that the study purpose is the achievement of deep understanding of the variety of factors of implementation that other researchers have previously identified.

An initial investigation of 38 empirical studies on the implementation of BI led to ten factors being compiled. Difficulties in implementation were found to exist in relation to the operationalisation of large numbers of factors within organisations. The implementation factors were analysed and then sorted into a descending order based upon their frequency of occurrence.

Research limitations/implications

The research is limited to consider BI implementation factors. Moreover, literature is collected from selected databases and journals from 1998 to 2018.

Practical implications

Researchers of BI may, within the future, develop models for the measurement of the implementation level of BI within industries along with the sustaining of them. Moreover, work-based learning industries can benefit by adopting the results of this study for the effective implementation of BI. The implementation factors can be seen as key constructs upon which there may be the undertaking of more statistical analyses.

Originality/value

The original output from this research can help researchers’ in the future in enhancing identification of studies that are relevant for the review of literature for their research.

  • Implementation
  • Literature review
  • Critical success factor
  • Business intelligence

El-Adaileh, N.A. and Foster, S. (2019), "Successful business intelligence implementation: a systematic literature review", Journal of Work-Applied Management , Vol. 11 No. 2, pp. 121-132. https://doi.org/10.1108/JWAM-09-2019-0027

Emerald Publishing Limited

Copyright © 2019, Nadeem Ali El-Adaileh and Scott Foster

Published in Journal of Work-Applied Management . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Organisations tend to own a tremendous volume of data. However, as noted by Williams and Williams (2006) , much data are poor in quality or inappropriate whether or not there has been a big investment in information technology (IT) within an organisation. Business intelligence (BI) can, however, help in delivering substantial amounts of information that is useful in a manner that is accurate and timely; such systems, therefore, can enhance decision-making processes ( Williams and Williams, 2006 ; Yeoh and Popovič, 2016 ). During the last decade, significant numbers of organisations of varying sizes and within a broad range of industrial sectors, from manufacturing to health services to the financial sector, have been implementing systems for BI in order to support decision makers and help achieve improvements in the performance of organisations ( Kappelman et al. , 2016 ).

Whilst it seems that BI has been accepted broadly and employed by many leading organisations across the world, there has been little research to examine the factors that lie behind successful implementation of BI ( Yeoh and Popovič, 2016 ). The suggestion from within the literature is that various factors, such as strategy, a project champion, the approach of top-level managers, organisation resources and change management, can have a significant impact. However, there is no consensus upon what factors in particular account for success ( Yeoh and Popovič, 2016 ; Dooley et al. , 2018 ; Villamarín and Diaz Pinzon, 2017 ; Nasab et al. , 2017 ). In general, most studies have undertaken explorations of the issue within the developed world in countries such as the USA or within Western Europe. As such, there is just a limited range of such studies conducted within developing countries ( Acheampong and Moyaid, 2016 ; Bakunzibake et al. , 2016 ; Hatta et al. , 2017 ; Owusu et al. , 2017 ). As such, this research has the aim of identifying, in empirical terms, which factors may have a bearing upon BI implementation through the use approaches from multiple perspectives, the study seeks to address this current gap within the knowledge and understanding of the issue. The research output has the potential of helping researchers in the future in the clearer identification of studies from the review of literature relevant to their research. Furthermore, reviewers and editors of journals require systematic reviews when examining the degree to which a submitted article has been undertaken with a review of the research available that is sufficiently inclusive. Within the sections that follow, there will be an explanation of the chosen research methodology for the preparation of the compiled studies. There will be searching of databases and journals through the use of key terms that have been identified within a preliminary review of literature. In order to identify how important each of the factors is, there will be the conducting of an analysis of frequency for the factors. In doing so, there will be a critical discussion around the factors that have been identified and presentation of the relationships that exist between implementation-related factors and dimensions of success for BI. Lastly, this paper puts forward some conclusions as well as potential implications for research in the future.

Research methodology

This research offers a thorough review of potential references in relation to factors having a bearing upon the implementation of BI. Since this study has the purpose of achieving an in-depth understanding of the variety of factors of implementation that other researchers have identified already, the correct approach was considered to be the undertaking of “content analysis”. It was claimed by Harris and Attour (2003) that it was appropriate to use the content analysis method when the observed phenomena relate to communication, i.e. contact, message and statement, as opposed to physical objects or behaviour. For Patton (1990) , content analysis could be seen as a process for the identification, coding and categorisation of the primary data pattern. There was the following of a systematic approach in order to select relevant publications with an initial search of the literature taking place in March 2018 through the use of 11 search engines/databases. As such, to ensure that every relevant article was identified from the previous 20 years, i.e. from 1998 to 2018, the following search engines/databases were utilised: Elsiver’s, ProQuest, Emerald Insight, EBSCO host, SwtsWise, Taylor & Francis, JSTOR, Ingenta Connect, Wiley Interscience, Google Scholar and Meta Press. Before conducting the search, two more criteria were applied to determine the target publications. The first criterion is the publication language should be in English, and the second criterion is that to assure the quality of the publication, only peer-reviewed articles were adopted. To conduct the search, the key words used were “business”, “intelligence” and “implementation”. Those key words were selected as they aligned with the primary research object concepts and various combinations and variations were used. A variety of chains of key words were tried so that there could be identification of a version that would give results that were most effective without involving a high number of irrelevant publications. Finally, the chains of key words that were chosen for the systematic review of literature were as follows: BI and success, BI and implementation, BI and implementation, BI and success, BI and critical success factors, BI and success factors, BI and critical success factors, BI and success factors. The key words chosen for the search were selected from those supplied by authors of a number of relevant articles that had been identified within the preliminary review of literature. Finally, the total downloaded articles from the databases were 38 articles.

Results and discussion

A total of 38 articles were reviewed for this study, of which 9 were conference proceedings and 29 were journal articles. It was revealed that the research interest related to BI within SMEs had been gradually increasing; in 2016, there was a maximum of six publications. Figure 1 shows the distribution of research methods. From the studied articles, it could be seen that surveys were obviously the methods used the most; other types of method are less frequent though comparable in the level of use.

As Figure 2 showed, within the literature, developed and western countries are those that have been targeted the most.

Figure 3 depicts frequency statistics for a variety of factors of implementation from papers attempting to provide an analysis of success in implementation of BI; the implementation factors that are most common can be seen, with clarity provided, in Figure 3 Within the literature there are 10 factors of implementation reported repeatedly, and these may be considered as essential factors for the implementation of BI. Those practices, as well as a selection of studies that support the relationship lying between dimensions of success and factors of implementation, are briefly discussed next.

Management support

Management support was one of the most widely cited implementation factors. The variable is a reflection of the level of support that the management offers in promoting, sponsoring or championing the use of IS, as well as a willingness to ensure sufficient allocation of resources ( Petter et al. , 2013 ). However, the gaining of commitment within an organisation and from the management can also be seen as one of the greatest challenges that a BI implementation team faces ( Yeoh et al. , 2008 ). It was noted by Olbrich et al. (2012) that strong support from the management is the factor that has most importance for success of BI; they also noted its controllability. Management support may however vary considerably over time. Moreover, organisational strategy from top management may transform BI ( Olszak and Ziemba, 2012 ). Overall success in the implementation of BI is affected significantly by management support ( Arnott, 2008 ; Yeoh et al. , 2008 ; Yeoh and Koronios, 2010 ; Woodside, 2011 ; Olszak and Ziemba, 2012 ; Anjariny and Zeki, 2013 ; Dawson and Van Belle, 2013 ; Sangar and Iahad, 2013 ; Puklavec et al. , 2014 , 2018; Grublješič and Jaklič, 2015 ; Nasab et al. , 2015 ; Acheampong and Moyaid, 2016 ; Mesaros et al. , 2016 ; Pham et al. , 2016 ; Yeoh and Popovič, 2016 ; Villamarín and Diaz Pinzon, 2017 ; Lautenbach et al. , 2017 ; Rezaie et al. , 2017 ). Other dimensions of BI success are also affected by management support such as the following: organisational implementation ( Wixom and Watson, 2001 ), system use ( Xu and Hwang, 2007 ), system quality ( Hwang and Xu, 2008 ), decision making ( Hasan et al. , 2012 ), productivity ( Hasan et al. , 2012 ) and user satisfaction ( Hung et al. , 2016 ). Overall, it is considered that there cannot be effective implementation of BI if the management does not offer sufficient support.

Data sources systems

Data sources may be defined as places where data employed in analysis is kept and from where it is drawn for use ( Hostmann, 2007 ). Data sources employed for retrieval of information are technological BI capabilities that may be either external or internal ( Harding, 2003 ). Conventionally, there has been a reliance of BI upon data that are numerical and/or structured, that is, measurable upon a numerical scale which may be analysed through the use of methods of statistics and/or the use of computing equipment ( Baars and Kemper, 2008 ). It was summarised by Yeoh et al. (2008) that the assurance of the integrity and quality of data from the systems from which it is sourced impacts heavily upon BI implementation success. Numerous studies support this idea and show that sources of data positively and directly affect the success of implementation of BI ( Wixom and Watson, 2001 ; Xu and Hwang, 2007 ; Arnott, 2008 ; Hwang and Xu, 2008 ; Yeoh and Koronios, 2010 ; Olszak and Ziemba, 2012 ; Anjariny and Zeki, 2013 ; Dawson and Van Belle, 2013 ; Işık et al. , 2013 ; Puklavec et al. , 2014 , 2018; Grublješič and Jaklič, 2015 ; Nasab et al. , 2015 ; Mesaros et al. , 2016 ; Pham et al. , 2016 ; Salmasi et al. , 2016 ; Yeoh and Popovič, 2016 ; Rezaie et al. , 2017 ).

Organisational resources

As Grandon and Pearson (2004) noted, the term “organisational resources” refers to the degree of technical, financial and human resources within an organisation. It was explained by Puklavec et al. (2014) that BI systems tend to involve a greater degree of voluntary action which leads to greater sensitivity for the availability of resources and can be a significant aspect for the adoption of systems for BI. For Owusu et al. (2017) , enhancement of the resources of an organisation may influence the implementation of BI systems. Numerous studies have, in fact, supported that idea; see, for example, Wixom and Watson (2001) , Arnott (2008) , Yeoh et al. (2008) , Yeoh and Koronios (2010) , Woodside (2011) , Dawson and Van Belle (2013) , Boonsiritomachai et al. (2014) , Puklavec et al. (2014) , Grublješič and Jaklič (2015) , Acheampong and Moyaid (2016) , Salmasi et al. (2016) , Yeoh and Popovič (2016) , Hatta et al. (2017) and Rezaie et al. (2017) , which help to show the direct and positive impacts that organisational resources have upon the success of a BI system overall.

IT infrastructure

IT infrastructure relates to the ability for users to be provided with information and data to suitable levels of reliability, timeliness, accuracy, confidentiality and security, as well as capability for tailoring processes to emergent business directions and needs and provision of universal access and connectivity with enough range and reach ( Fink et al. , 2017 ). BI systems have a number of characteristics in common with traditional development lifecycles for IT projects with their various phases ( Moss and Atre, 2003 ). Implementation of systems for BI does not solely entail the buying of combinations of hardware and software but rather it is an undertaking that has greater complexity with requirement for suitable resources and infrastructure over longer time periods ( Yeoh and Koronios, 2010 ). It has been noted by many authors that IT infrastructure impacts directly upon BI implementation success, for example, Arnott (2008) , Yeoh et al. (2008) , Yeoh and Koronios (2010) , Olszak and Ziemba (2012) , Nasab et al. (2015) , Pham et al. (2016) , Salmasi et al. (2016) , Yeoh and Popovič (2016) , Villamarín and Diaz Pinzon (2017) , Lautenbach et al. (2017) and Rezaie et al. (2017) .

Clear visions help organisations to strategise their missions. In addition, there is a requirement for organisational visions to be disseminated throughout the different organisational levels ( Prijatelj, 1999 ). It was noted by Adamala and Cidrin (2011) that a system for BI has to be tied closely to the strategic vision of a company. A clear vision enables BI implementation to be successful. Furthermore, a vision that is long term, in terms that are primarily organisational and strategic, is essential for establishing a business that is robust and has alignment to the strategic vision in order for the needs and objectives of the business to be met ( Yeoh and Koronios, 2010 ). Several studies have shown that the success of a BI system is greatly influenced by a vision that is clear; see, for example, Arnott (2008) , Yeoh et al. (2008) , Yeoh and Koronios (2010) , Dawson and Van Belle (2013) , Sangar and Iahad (2013) , Nasab et al. (2015) , Pham et al. (2016) , Yeoh and Popovič (2016) and Rezaie et al. (2017) . It was empirically shown by Hwang and Xu (2008) that business needs and a vision that is clear impact significantly and positively upon the quality of system.

Project champion

The requirement for a champion of the project is also considered as a relatively significant element in successful BI implementation. As Mandal and Gunasekaran (2003) note, such a project champion ought to have strong skills in leadership. In addition, such a person ought to have managerial competencies in a range of personal, technical and business-oriented ways ( Kraemmergaard and Rose, 2002 ). Project champion is defined here as an individual at management level who recognises ideas that are useful for his/her organisation and leads with adequate authority and resources during all the phases of development and implementation ( Meyer, 2000 ). A champion was described by Yeoh and Koronios (2010) as a person who has high levels of enthusiasm along with an in-depth knowledge of business processes within his or her organisation, in addition to a good awareness of the technological innovations under discussion and requiring commitment. The research that exists in the area shows that project champions, when present, are able to impact upon successful BI system adoption significantly ( Arnott, 2008 ; Yeoh et al. , 2008 ; Yeoh and Koronios, 2010 ; Olszak and Ziemba, 2012 ; Anjariny and Zeki, 2013 ; Dawson and Van Belle, 2013 ; Sangar and Iahad, 2013 ; Puklavec et al. , 2014, 2018 ; Nasab et al. , 2015 ; Acheampong and Moyaid, 2016 ; Pham et al. , 2016 ; Yeoh and Popovič, 2016 ; Villamarín and Diaz Pinzon, 2017 ; Owusu et al. , 2017 ; Rezaie et al. , 2017 ).

Team skills

Implementation of BI calls for a balance of technical skills within a team, interpersonal abilities and the capacity to work skilfully in the undertaking of tasks in ways that involve good interaction by users ( Wixom and Watson, 2001 ). Furthermore, a project team ought to consist of members from various areas within a business in order for the sharing of ideas and so that standardisation can be potentially increased, particularly if, as part of the initiative for BI, there is to be a data warehouse that is enterprise wide ( Goodhue et al. , 2002 ). Innovation and learning are stimulated by the coming together of team members that have a diverse range of perspectives and competencies, and this can help in the generation of a greater amount of alternative solutions to problems that are complex ( Campion et al. , 1993 ; Lee and Xia, 2010 ). In addition, engaging project team by managers in the strategic planning and vision will produce an environment of employee satisfaction and enhancing the leading skills ( Wall, Russell and Moore, 2017 ; Wall, Bellamy, Evans and Hopkins, 2017 ). The skills of a team significantly affect the overall success of implementation of BI ( Arnott, 2008 ; Yeoh et al. , 2008 ; Yeoh and Koronios, 2010 ; Olszak and Ziemba, 2012 ; Anjariny and Zeki, 2013 ; Sangar and Iahad, 2013 ; Nasab et al. , 2015 ; Mesaros et al. , 2016 ; Yeoh and Popovič, 2016 ; Villamarín and Diaz Pinzon, 2017 ; Rezaie et al. , 2017 ). The skills of a team also have a bearing upon other dimensions of success of BI, such as productivity, project implementation, decision making and information quality ( Wixom and Watson, 2001 ; Xu and Hwang, 2007 ; Hwang and Xu, 2008 ).

Project management

The term “project management” is in reference to the ongoing management of the plan for implementation. As well as stages of planning it involve, therefore, the allocation of responsibilities to a variety of stakeholders, definition of critical paths and milestones, human resource planning, determination of success indicators and training ( Nah and Delgado, 2006 ). At first, modern project management methods were intended for their application within big organisations that had systems of complexity that needed such systematic processes ( Baccarini, 1999 ). More recently, however, they may be altered and adapted to make them suitable for addressing the needs of organisations that are smaller ( Fedouaki et al. , 2013 ). As several authors have noted, the project management can have a considerable impact upon the implementation of a BI system ( Arnott, 2008 ; Yeoh et al. , 2008 ; Yeoh and Koronios, 2010 ; Woodside, 2011 ; Anjariny and Zeki, 2013 ; Sangar and Iahad, 2013 ; Pham et al. , 2016 ; Rezaie et al. , 2017 ).

User participation

The term “user participation”, related to developing specific IS, was defined by ( Kearns and Sabherwal, 2006 ) as behaviours, tasks or assignments that users or user representatives perform whilst within the development project for IS. Accurate capture and communication of user requirements the members of the project team are ensured by good user participation; these properties have particular importance if there is an initial lack of clarity with regard to system requirements ( Wixom and Watson, 2001 ). It was noted by Audzeyeva and Hudson (2016) that adequate involvement of users within adjustment of the BI, within its exploitation over the long term, is likely to make a contribution to its usability within the future as well as helping match it to other processes within the organisation. Moreover, organisational change that has been enabled by BI can, in turn, help in the introduction of changes to processes for organisational control and coordination. In general, user participation has a great deal of significance for the implementation of IS projects ( Hwang and Thorn, 1999 ). Also, in particular, user participation is significant for BI system ( Wixom and Watson, 2001 ; Xu and Hwang, 2007 ; Hwang and Xu, 2008 ; Yeoh et al. , 2008 ; Yeoh and Koronios, 2010 ; Dawson and Van Belle, 2013 ; Grublješič and Jaklič, 2015 ; Nasab et al. , 2015 ; Mesaros et al. , 2016 ; Yeoh and Popovič, 2016 ; Rezaie et al. , 2017 ).

Change management

The term “change management” is in reference to procedures for managing change in an organisation; such changes both reinvent and revolutionise the functions and processes of government ( Ndou, 2004 ). A programme for change management has importance since it enables there to be a reduction in any resistance to implementation that may be encountered and so it helps facilitate adoption ( Hawking and Sellitto, 2010 ); this is especially the case if technological development is ongoing since, at these moments, the possibility for change happening are greater ( Fourati-Jamoussi and Niamba, 2016 ; Villamarín and Diaz Pinzon, 2017 ). They went on to note that the absence of this factor, i.e. effective change management, from the implementation processes for BI could help provide an explanation for failure of BI projects ( Williams and Williams, 2006 ). Numerous studies support this notion; see, for example, Yeoh et al. , 2008 ; Yeoh and Koronios (2010) , Sangar and Iahad (2013) , Grublješič and Jaklič (2015) , Yeoh and Popovič (2016) , Villamarín and Diaz Pinzon (2017) and Rezaie et al. (2017) , all of which indicate the direct and positive impact that change management has upon the implementation of BI systems.

The compilation above, cited from the literature, offers a basis for considering the range of factors of success and associated frequencies for each of them. Additional analyses, however, undertaken with the aim of uncovering clear and obvious gaps within the relevant literature have made it apparent that there has been a lack of deep consideration given to the factors that have a bearing on implementation. In addition, there seems to be a variety of definitions for implementation factors and the concept of resources and change management. Likewise, there seems to be little explanation put forward of the particular tactics that may be employed in implementing such systems. Lastly, a further noteworthy observation was that, from the cited implementation factors, there was a lack of perspective taken on user characteristics and work-based learning. Wall (2017) stated that work-based learning enhances employee’s well-being and increase organisational performance. There was either presentation of implementation factors without explaining from whom the perspective was being shown or there was provision of a user perspective though only in relation to one single factor of implementation. All too often, researchers have tended to focus upon just one particular implementation factor or one particular aspect of the process of implementation. As a result, little research has been recorded that manages to encompass all significant considerations with regard to factors of implementation.

Implications of the study

This study has drawn a lot from the already existing literature related to the implementation of BI into one single piece of research. This allows for taking stock of the current state of play with regard to knowledge in the field and helps the identification of appropriate practice and areas for further study. The study demonstrates that there is a good theoretical understanding of the background or framework for the implementation of BI. In addition, the paper can benefit researchers through the provision of case study contexts. Through aggregation of this information into one paper, researchers may now more easily identify a focus for their own studies based upon the contexts that they can see have been explored or not. It has been argued in this study that ten distinct factors are required for successful implementation of BI, as shown in Figure 3 . These factors are constructs that may be used within practice for the analysis of needs and the design of a BI initiative, as well as its implementation, monitoring, control and assessment. Consolidation of factors within the practical stages of implementation is an accurate representation of the procedures and behaviours within industry within a more clear picture on collective trials undertaken. Work-based learning industries may focus upon an exploration of these factors to establish the scenario likely to be more successful for their particular contexts. Literature related to implementation of BI is much more focussed upon organisations within developed countries within Europe, Australia and the USA. As such, little research has been undertaken into organisations based within developing countries. Studies have shown that there are additional challenges for BI systems within developing countries along with increasing levels of dependency ( Owusu et al. , 2017 ). It appears that certain factors of implementation are prioritised differently within different countries. Whilst “resources” are ranked at the middle of the frequency analysis of this study, when trying to implement a BI system within a developing country, it could be a factor that is highly critical. From the study of over 38 relevant case studies within various contexts, the identified implementation factors outline the various factors created through a combination of factors scattered throughout the literature. In practical terms, this has provided a thorough overview of the factors of implementation present within the existing literature. Furthermore, since the factors were compiled through the use of existing case studies, the factors are based upon practical experience in real industrial settings. These factors may be used, therefore, by practitioners in relation to their particular industry, with concentration upon those elements that have greater prevalence in their field. This paper, then, offers an industry-oriented and practical framework to help ensure BI implementation success.

Research recommendation and limitations

Research recommendations are as useful for researchers as they are for organisations wishing to implement systems of BI successfully. The review of literature, in relation to factors that have an association with research of the implementation of BI, ought to have analysis of the factors that are used most commonly with respect to BI system implementation; this would provide researchers with a path towards proper analysis of what factors lie behind success. This review of literature can also serve as a guide for organisations seeking to take preventative measures for avoiding some of the challenges that are potentially faced whilst trying to implement a BI system successfully.

This research paper is not without limitations. First, the study can be considered as only looking at factors behind the implementation of BI; as such, lots of other themes of research related to the implementation of BI systems are overlooked. Second, there has not been exploration of the research paradigms in this study in methodological and theoretical terms. Further empirical research of that area could discover other facts in relation to factors and their impact upon success. There ought to be careful consideration and assimilation of these concerns in any further related studies.

This research has involved the review of literature published from 1998 to 2018 and discovered that the subject of implementation of BI was limited. Further research on the implementation of BI may be very useful for enhancement of the likeliness of success in implementation of BI. The review of the literature with regard for BI implementation shows that in lots of cases, the factors of implementation put forward are based upon review of a limited case study example or literature already published. Previous research does not provide clear guidance in relation to which factors of implementation ought to be adopted however, because of inconsistencies and the nature of relationships for BI success dimensions. This paper has had the purpose of analysing the literature on BI implementation with particular regard for implementation factors. The study aim has been achieved through selection of thirty-eight papers related to BI implementation. The research findings are potentially useful for those who are in the process of implementing a BI system or those who have failed to implement a BI system initiative successfully. In addition, the use of the BI system is required to enhance the work-based learning process. This research has brought a degree of clarity on the topic and offers useful contributions and guidelines from and to the literature for both researchers and managers alike. The paper makes a contribution to development and understanding in the field of implementation of BI and appreciation of the impacts of particular practices upon success. Despite the inconsistencies that were identified, the literature review shows that the particular implementation factors result in significant levels of success in the implementation of a BI system ( Figure 3 ).

business intelligence research papers pdf

Research methods

business intelligence research papers pdf

Research targeted countries

business intelligence research papers pdf

Most common implementation factors

Acheampong , O. and Moyaid , S.A. ( 2016 ), “ An integrated model for determining business intelligence systems adoption and post-adoption benefits in banking sector ”, Journal of Administrafive and Business Studies , Vol. 2 No. 2 , pp. 84 - 100 .

Adamala , S. and Cidrin , L. ( 2011 ), “ Key success factors in business intelligence ”, Journal of Intelligence Studies in Business , pp. 107 - 127 .

Anjariny , A.H. and Zeki , A.M. ( 2013 ), “ The important dimensions for assessing organizations’ readiness toward business intelligence systems from the perspective of Malaysian organization ”, IEEE 2013 International Conference on Advanced Computer Science Applications and Technologies (ACSAT) , pp. 544 - 548 .

Arnott , D. ( 2008 ), “ Success factors for data warehouse and business intelligence systems ”, ACIS 2008 Proceedings , p. 16 .

Audzeyeva , A. and Hudson , R. ( 2016 ), “ How to get the most from a business intelligence application during the post implementation phase? Deep structure transformation at a UK retail bank ”, European Journal of Information Systems , Vol. 25 No. 1 , pp. 29 - 46 .

Baars , H. and Kemper , H.-G. ( 2008 ), “ Management support with structured and unstructured data – an integrated business intelligence framework ”, Information Systems Management , Vol. 25 No. 2 , pp. 132 - 148 .

Baccarini , D. ( 1999 ), “ The logical framework method for defining project success ”, Project Management Journal , Vol. 30 No. 4 , pp. 25 - 32 .

Bakunzibake , P. , Grönlund , Å. and Klein , G.O. ( 2016 ), “ E-government implementation in developing countries: enterprise content management in Rwanda ”, 15th IFIP Electronic Government (EGOV)/8th Electronic Participation (EPart) Conference, Univ Minho , IOS Press , Guimaraes , September 5–8 , pp. 251 - 259 .

Boonsiritomachai , W. , McGrath , M. and Burgess , S. ( 2014 ), “ A research framework for the adoption of Business Intelligence by Small and Medium-sized enterprises ”, Proceedings of the 27th Annual Conference on Small Enterprise Association of Australia and New Zealand , SEAANZ , pp. 1 - 22 .

Campion , M.A. , Medsker , G.J. and Higgs , A.C. ( 1993 ), “ Relations between work group characteristics and effectiveness: implications for designing effective work groups ”, Personnel Psychology , Vol. 46 No. 4 , pp. 823 - 847 .

Dawson , L. and Van Belle , J.-P. ( 2013 ), “ Critical success factors for business intelligence in the South African financial services sector: original research ”, South African Journal of Information Management , Vol. 15 No. 1 , pp. 1 - 12 .

Dooley , P. , Levy , Y. , Hackney , R.A. and Parrish , J.L. ( 2018 ), “ Critical value factors in business intelligence systems implementations ”, in Deokar , A. , Gupta , A. , Iyer , L. and Jones , M. (Eds), Analytics and Data Science. Annals of Information Systems , Springer , Cham .

Fedouaki , F. , Okar , C. and Alami , S.El. ( 2013 ), “ A maturity model for business intelligence system project in small and medium-sized enterprises: an empirical investigation ”, IJCSI International Journal of Computer Science Issues , Vol. 10 No. 6 , pp. 61 - 69 .

Fink , L. , Yogev , N. and Even , A. ( 2017 ), “ Business intelligence and organizational learning: an empirical investigation of value creation processes ”, Information and Management , Vol. 54 No. 1 , pp. 38 - 56 .

Fourati-Jamoussi , F. and Niamba , C.N. ( 2016 ), “ An evaluation of business intelligence tools: a cluster analysis of users’ perceptions ”, Journal of Intelligence Studies in Business , Vol. 6 No. 1 , pp. 37 - 47 .

Goodhue , D.L. , Wixom , B.H. and Watson , H.J. ( 2002 ), “ Realizing business benefits through CRM: hitting the right target in the right way ”, MIS Quarterly Executive , Vol. 1 No. 2 , pp. 79 - 94 .

Grandon , E.E. and Pearson , J.M. ( 2004 ), “ Electronic commerce adoption: an empirical study of small and medium US businesses ”, Information & management , Vol. 42 No. 1 , pp. 197 - 216 .

Grublješič , T. and Jaklič , J. ( 2015 ), “ Business intelligence acceptance: the prominence of organizational factors ”, Information Systems Management , Vol. 32 No. 4 , pp. 299 - 315 .

Harding , W. ( 2003 ), “ BI crucial to making the right decision: business intelligence is all about collecting useful information from multiple sources and then presenting it in an easy to understand format (special report: business intelligence) ”, Financial Executive , Vol. 19 No. 2 , pp. 49 - 51 .

Harris , G. and Attour , S. ( 2003 ), “ The international advertising practices of multinational companies ”, European Journal of Marketing , Vol. 37 Nos 1/2 , pp. 154 - 168 .

Hasan , H.M. , Lotfollah , F. and Negar , M. ( 2012 ), “ Comprehensive model of business intelligence: a case study of Nano’s companies ”, Indian Journal of Science and Technology , Vol. 5 No. 6 , pp. 2851 - 2859 .

Hatta , N.N.M. , Miskon , S. and Abdullah , N.S. ( 2017 ), “ Business intelligence system adoption model for SMEs ”, PACIS , p. 192 .

Hawking , P. and Sellitto , C. ( 2010 ), “ Critical success factors of business intelligence (BI) in an ERP systems environment ”, Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS) No. 1996, ACIS , p. 4 .

Hostmann , B. ( 2007 ), “ BI competency centres: bringing intelligence to the business ”, Business Performance Management , Vol. 5 No. 4 , pp. 4 - 10 .

Hung , S.-Y. , Huang , Y.-W. , Lin , C.-C. , Chen , K. and Tarn , J.M. ( 2016 ), “ Factors influencing business intelligence systems implementation success in the enterprises ”, Pacific Asia Conference on Information Systems (PACIS), Proceedings , p. 297 .

Hwang , M.I. and Thorn , R.G. ( 1999 ), “ The effect of user engagement on system success: a meta-analytical integration of research findings ”, Information and Management , Vol. 35 No. 4 , pp. 229 - 236 .

Hwang , M.I. and Xu , H. ( 2008 ), “ A structural model of data warehousing success ”, Journal of Computer Information Systems , Vol. 49 No. 1 , pp. 48 - 56 .

Işık , Ö. , Jones , M.C. and Sidorova , A. ( 2013 ), “ Business intelligence success: the roles of BI capabilities and decision environments ”, Information and Management , Vol. 50 No. 1 , pp. 13 - 23 .

Kappelman , L. , McLean , E. , Johnson , V. and Torres , R. ( 2016 ), “ The 2015 SIM IT issues and trends study ”, MIS Quarterly Executive , Vol. 15 No. 1 , pp. 55 - 83 .

Kearns , G.S. and Sabherwal , R. ( 2006 ), “ Strategic alignment between business and information technology: a knowledge-based view of behaviors, outcome, and consequences ”, Journal of Management Information Systems , Vol. 23 No. 3 , pp. 129 - 162 .

Kraemmergaard , P. and Rose , J. ( 2002 ), “ Managerial competences for ERP journeys ”, Information Systems Frontiers , Vol. 4 No. 2 , pp. 199 - 211 .

Lautenbach , P. , Johnston , K. and Adeniran-Ogundipe , T. ( 2017 ), “ Factors influencing business intelligence and analytics usage extent in South African organisations ”, South African Journal of Business Management , Vol. 48 No. 3 , pp. 23 - 33 .

Lee , G. and Xia , W. ( 2010 ), “ Toward agile: an integrated analysis of quantitative and qualitative field data on software development agility ”, MIS Quarterly , Vol. 34 No. 1 , pp. 87 - 114 .

Mandal , P. and Gunasekaran , A. ( 2003 ), “ Issues in implementing ERP: a case study ”, European Journal of Operational Research , Vol. 146 No. 2 , pp. 274 - 283 .

Mesaros , P. , Carnicky , S. , Mandicak , T. , Habinakova , M. , Mackova , D. and Spisakova , M. ( 2016 ), “ Model of key success factors for business intelligence implementation ”, Journal of Systems Integration , Vol. 7 No. 3 , pp. 3 - 15 .

Meyer , M. ( 2000 ), “ Innovation roles: from souls of fire to devil’s advocates ”, The Journal of Business Communication (1973) , Vol. 37 No. 4 , pp. 328 - 347 .

Moss , L. and Atre , S. ( 2003 ), “ Business intelligence roadmap: the complete project lifecycle for decision-support applications ”, available at: https://books.google.com/books?hl=en&lr=&id=ZV8jeV4a9_AC&oi=fnd&pg=PR7&dq=Business+Intelligence+Roadmap:+The+Complete+Project+Lifecycle+for+Decision-Support+Applications,+Boston,+MA:+Addison-Wesley.&ots=LuoBLeHPE0&sig=b1pux1oR1GOxJZHza3UQ9W3TMqk (accessed 14 August 2019 ).

Nah , F.F.-H. and Delgado , S. ( 2006 ), “ Critical success factors for enterprise resource planning implementation and upgrade ”, Journal of Computer Information Systems , Vol. 46 No. 5 , pp. 99 - 113 .

Nasab , S.S. , Selamat , H. and Masrom , M. ( 2015 ), “ A Delphi study of the important factors for BI system implementation in the public sector organizations ”, Jurnal Teknologi , Vol. 77 No. 19 , pp. 113 - 120 .

Nasab , S.S. , Jaryani , F. , Selamat , H. and Masrom , M. ( 2017 ), “ Critical success factors for business intelligence system implementation in public sector organisation ”, International Journal of Information Systems and Change Management , Vol. 9 No. 1 , pp. 22 - 43 .

Ndou , V. ( 2004 ), “ E-government for developing countries: opportunities and challenges ”, The Electronic Journal of Information Systems in Developing Countries , Vol. 18 No. 1 , pp. 1 - 24 .

Olbrich , S. , Poppelbuß , J. and Niehaves , B. ( 2012 ), “ Critical contextual success factors for business intelligence: a Delphi study on their relevance, variability, and controllability ”, paper presented at the System Science (HICSS), 2012 45th Hawaii International Conference .

Olszak , C.M. and Ziemba , E. ( 2012 ), “ Critical success factors for implementing business intelligence systems in small and medium enterprises on the example of Upper Silesia, Poland ”, Interdisciplinary Journal of Information, Knowledge, and Management , Vol. 7 No. 2 , pp. 129 - 150 .

Owusu , A. , Agbemabiasie , G.C. , Abdurrahaman , D.T. and Soladoye , B.A. ( 2017 ), “ Determinants of business intelligence systems adoption in developing countries: an empirical analysis from Ghanaian banks ”, The Journal of Internet Banking and Commerce , Vol. 24 No. 2 , pp. 1 - 25 .

Patton , M. ( 1990 ), “ Qualitative evaluation and research methods ”, available at: http://psycnet.apa.org/record/1990-97369-000 (accessed 15 July 2019 ).

Petter , S. , DeLone , W. and McLean , E.R. ( 2013 ), “ Information systems success: the quest for the independent variables ”, Journal of Management Information Systems , Vol. 29 No. 4 , pp. 7 - 62 .

Pham , Q.T. , Mai , T.K. , Misra , S. , Crawford , B. and Soto , R. ( 2016 ), “ Critical success factors for implementing business intelligence system: empirical study in Vietnam ”, International Conference on Computational Science and Its Applications , Springer , pp. 567 - 584 .

Prijatelj , V. ( 1999 ), “ Success factors of hospital information system implementation: what must go right? ”, Studies in Health Technology and Informatics , Vol. 68 No. 1 , pp. 197 - 202 .

Puklavec , B. , Oliveira , T. and Popovic , A. ( 2014 ), “ Unpacking business intelligence systems adoption determinants: an exploratory study of small and medium enterprises ”, Economic and Business Review for Central and South-Eastern Europe , Vol. 16 No. 2 , pp. 185 - 213 .

Puklavec , B. , Oliveira , T. and Popovič , A. ( 2018 ), “ Understanding the determinants of business intelligence system adoption stages ”, Industrial Management and Data Systems , Vol. 188 No. 1 , pp. 236 - 261 , available at: https://doi.org/10.1108/IMDS-05-2017-0170

Rezaie , S. , Mirabedini , S.J. and Abtahi , A. ( 2017 ), “ Identifying key effective factors on the implementation process of business intelligence in the banking industry of Iran ”, Journal of Intelligence Studies in Business , Vol. 7 No. 3 , pp. 5 - 24 .

Salmasi , M.K. , Talebpour , A. and Homayounvala , E. ( 2016 ), “ Identification and classification of organizational level competencies for BI success ”, Journal of Intelligence Studies in Business , Vol. 6 No. 2 , pp. 17 - 33 .

Sangar , A.B. and Iahad , N.B.A. ( 2013 ), “ Critical factors that affect the success of business intelligence systems (BIS) implementation in an organization ”, International Journal of Scientific and Technology Research , Vol. 2 No. 2 , pp. 176 - 180 .

Villamarín , J.M. and Diaz Pinzon , B. ( 2017 ), “ Key success factors to business intelligence solution implementation ”, Journal of Intelligence Studies in Business , Vol. 7 No. 1 , pp. 48 - 69 .

Wall , T. ( 2017 ), “ A manifesto for higher education, skills and work-based learning: through the lens of the manifesto for wor k ”, Higher Education, Skills and Work-Based Learning , Vol. 7 No. 3 , pp. 304 - 314 .

Wall , T. , Russell , J. and Moore , N. ( 2017 ), “ Positive emotion in workplace impact: the case of a work-based learning project utilising appreciative inquiry ”, Journal of Work-Applied Management , Vol. 9 No. 2 , pp. 129 - 146 .

Wall , T. , Bellamy , L. , Evans , V. and Hopkins , S. ( 2017 ), “ Revisiting impact in the context of workplace research: a review and possible directions ”, Journal of Work-Applied Management , Vol. 9 No. 2 , pp. 95 - 109 .

Williams , S. and Williams , N. ( 2006 ), The Profit Impact of Business Intelligence , Morgan Kaufmann , San Francisco, CA .

Wixom , B.H. and Watson , H.J. ( 2001 ), “ An empirical investigation of the factors affecting data warehousing success ”, MIS Quarterly , Vol. 25 No. 1 , pp. 17 - 41 .

Woodside , J. ( 2011 ), “ Business intelligence best practices for success ”, International Conference on Information Management and Evaluation , Academic Conferences International Limited , p. 556 .

Xu , H. and Hwang , M.I. ( 2007 ), “ The effect of implementation factors on data warehousing success: an exploratory study ”, Journal of Information, Information Technology, and Organizations , Vol. 2 No. 1 , pp. 1 - 14 .

Yeoh , W. and Koronios , A. ( 2010 ), “ Critical success factors for business intelligence systems ”, Journal of Computer Information Systems , Vol. 50 No. 3 , pp. 23 - 32 .

Yeoh , W. and Popovič , A. ( 2016 ), “ Extending the understanding of critical success factors for implementing business intelligence systems ”, Journal of the Association for Information Science and Technology , Vol. 67 No. 1 , pp. 134 - 147 .

Yeoh , W. , Koronios , A. and Gao , J. ( 2008 ), “ Managing the implementation of business intelligence systems: a critical success factors framework ”, International Journal of Enterprise Information Systems (IJEIS) , Vol. 4 No. 3 , pp. 79 - 94 .

Further reading

Boyton , J. , Ayscough , P. , Kaveri , D. and Chiong , R. ( 2015 ), “ Suboptimal business intelligence implementations: understanding and addressing the problems ”, Journal of Systems and Information Technology , Vol. 17 No. 3 , pp. 307 - 320 .

Olszak , C.M. ( 2016 ), “ Toward better understanding and use of business intelligence in organizations ”, Information Systems Management , Vol. 33 No. 2 , pp. 105 - 123 .

Watson , H.J. and Wixom , B.H. ( 2007 ), “ Enterprise agility and mature BI capabilities ”, Business Intelligence Journal , Vol. 12 No. 3 , pp. 13 - 28 .

Corresponding author

Related articles, we’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

  • We're Hiring!
  • Help Center

paper cover thumbnail

Business Intelligence and Analytics: Research Directions 1. BUSINESS INTELLIGENCE AND ANALYTICS (BIA

Profile image of Amine Aziza

Related Papers

business intelligence research papers pdf

Dr. Nazrul Islam

Typically, business organizations hardly find proper information, processes, and tools need to make responsive decisions at all levels of maangement. Organizations lack clear visibility and insights into the fundamental business situations even after spending heavily on technology, people skills and consultancy. Organizations continuously misunderstand the true story behind the numbers that is a reality that translates directly into a growing number of financial restatements and serious strategic missteps. Despite a generation of investment and efforts, financial analysis and reporting often lack the true standard. The reason might be attributed by the idea of leveraging business intelligence. This is primarily due to lack of awareness about the power of Business Intelligence and Analytics (BIA). The fact is that BIA should be an integral component of every operation of every business as it helps to identify its most profitable customers, trouble spots within the organization, and the return on investment for certain products or services. Hence, BIA is highly demanded by the business organizations as it seeks to improve business outcomes, customer relationships, and operational efficiency by using information. IT-driven application development, access to historical data, and canned business reports are no longer satisfactory now-a-days (Wixom and Watson, 2010 & Lautenbach, et al. 2017). Users want more control, better visualization, higher level of analyzing capabilities, and faster development cycles. Organizations also closely watch emerging technology trends to discover the next competitive advantage in the use of information. Now-a-days, data volumes are growing and the organizations are seeking to tap new sources generated by social media and online customer behaviors. This trend is spurring tremendous interest in better access and analysis of the variety of information available in unstructured and semi-structured content sources. Therefore, there might be a question that how BIA helps in increasing operational efficiency and accuracy in business decisions? Yes, it gives organizations improved visibility into all spending related to analytics and reporting. Companies can leverage this approach to mitigate risks, by better managing credit exposure, creating supply chain flexibility to optimize inventories, and to reduce losses from diversion, counterfeits, revenue leakage, and fraud (Raisinghani, 2003). It is also used to understand the capabilities available in the firm on the state of the art, trends, future market directions, the technologies, and the regulatory environment in which the firm competes and the actions of competitors and the implications of these actions (Negash, 2004).

Steffi Sonu

DR.C.KARTHIKEYAN DR.C.KARTHIKEYAN

IEEE/CAA Journal of Automatica Sinica

IEEE/CAA J. Autom. Sinica

This paper focuses on facilitating state-of-the-art applications of big data analytics (BDA) architectures and infrastructures to telecommunications (telecom) industrial sector. Telecom companies are dealing with terabytes to petabytes of data on a daily basis. IoT applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts (POC) on a severely limited BDA technology stack (as compared to the available technology stack), i.e., we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation (called LambdaTel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines. We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe LambdaTel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.

RELATED PAPERS

International Journal of Big Data Intelligence

Giuseppina Cretella

Julio Gazeta

International Journal of Research in Social Sciences Vol. 9 Issue 4, April 2019

Sanjib Biswas , Jaydip Sen

Georne Balirs

IJSES Editor

Hugh J Watson

research.microsoft.com

Sunil Kamath

ACM Computing Surveys

Hong-Ning Dai

Amir Mosavi

Carlo Vaccari

IJIREM JOURNAL

Journal of Parallel and Distributed Computing

Marcos Assuncao

Emmanuel Benjamin

Big Data & Society

Fernando van der Vlist

Kalyan Nagaraj

Dr. T. Jayaraj

Athabasca University

Dickson Lam

Nauri Júnior Cazuza

Journal of Computer Science IJCSIS

Kyar Nyo Aye

Tamaro Green

Mohammed S. Hadi , Ahmed Lawey

GÖKHAN SİLAHTAROĞLU

meera kukade

Elke Rundensteiner

The Predictive Sports Book

Andrew Pearson

Amin Beheshti , Bilal Abu-Salih

Erik Brynjolfsson

International Journal of Computer Science and Information Technology (IJCSIT)

International Journal of Computer Science and Information Technology ( IJCSIT ) INSPEC ,WJCI Indexed , Hanane Anir , kassou meryem , Marlon I Tayag , Samer Shorman

Bharadwaja Kumar , Gsr Vijayabharadwaj

JAYENDRA KUMAR

International Journal of Recent Research Aspects ISSN 2349-7688

Journal of Fluid Mechanics

Henry Selvaraj

Xiufeng Liu

Hoda Abdel Hafez

Bhausaheb Sanap

RELATED TOPICS

  •   We're Hiring!
  •   Help Center
  • Find new research papers in:
  • Health Sciences
  • Earth Sciences
  • Cognitive Science
  • Mathematics
  • Computer Science
  • Academia ©2024

Book cover

Modern Data Strategy pp 121–131 Cite as

Data Warehousing and Business Intelligence

  • Mike Fleckenstein 3 &
  • Lorraine Fellows 3  
  • First Online: 13 February 2018

4583 Accesses

1 Citations

Traditionally data warehouses were built to reflect “snapshots” of the enterprise-level operational environment over time. In other words, a certain amount of operational data was recorded at a particular point in time and stored in a data warehouse. Originally, such snapshots were typically taken monthly. Today, they are often taken multiple times per day. Data warehouses provide a history of the operational environment suitable for trend analysis. This allows analysts and business executives to plan for the future based on recent trends. Answers to such questions as how have revenue or costs evolved over time and the ability to “slice and dice” such data are typical functions asked of a data warehouse.

This is a preview of subscription content, log in via an institution .

Buying options

  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
  • Available as EPUB and PDF
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
  • Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Inmon, W. H, Strauss, D., & Neushloss, N., DW 2.0: The Architecture for the Next Generation of Data Warehousing, Morgan Kaufmann, July 28, 2010.

Kimball, R., “Newly Emerging Best Practices for Big Data,” The Kimball Group, September 30, 2012.

Forrester Wave—Enterprise Data Warehouse, Q4 2015, December 7, 2015.

“Stay On Top of New BI Technologies to Lead Your Enterprise into The Not-Too-Distant Future,” Forrester Research, Inc., March 1, 2016.

Author information

Authors and affiliations.

MITRE, McLean, VA, USA

Mike Fleckenstein & Lorraine Fellows

You can also search for this author in PubMed   Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter.

Fleckenstein, M., Fellows, L. (2018). Data Warehousing and Business Intelligence. In: Modern Data Strategy. Springer, Cham. https://doi.org/10.1007/978-3-319-68993-7_12

Download citation

DOI : https://doi.org/10.1007/978-3-319-68993-7_12

Published : 13 February 2018

Publisher Name : Springer, Cham

Print ISBN : 978-3-319-68992-0

Online ISBN : 978-3-319-68993-7

eBook Packages : Computer Science Computer Science (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Publish with us

Policies and ethics

  • Find a journal
  • Track your research

business intelligence Recently Published Documents

Total documents.

  • Latest Documents
  • Most Cited Documents
  • Contributed Authors
  • Related Sources
  • Related Keywords

The effect of business intelligence, organizational learning and innovation on the financial performance of innovative companies located in Science Park

Modelling maintainability of healthcare facilities services systems using bim and business intelligence, the impact of business intelligence on the marketing with emphasis on cooperative learning: case-study on the insurance companies, democratizing business intelligence and machine learning for air traffic management safety, development of technical and statistical algorithm using business intelligence tools for energy yield assessment of large rooftop photovoltaic system ensembles, thriving information system through business intelligence knowledge management excellence framework.

In the current digitalization dilemma of an organization, there is a need for the business intelligence and knowledge management element for enhancing a perspective of learning and strategic management. These elements will comprise a significant evolution of learning, insight gained, experiences and knowledge through compelling theoretical impact for practitioners, academicians, and scholars in the pertinent field of interest. This phenomenon occurs due to digitalization transformation towards industry revolution 5.0 and organizational excellence in the information system area. This research focuses on the characteristic of a comprehensive performance measure perspective in an organization that conceives information assessment and key challenges of Business Intelligence and Knowledge Management in perceiving a relevant organizational excellence framework. The dynamic research focusing on the decision-making process and leveraging better knowledge creation. The future of organization excellence seemed to be convergent in determining the holistic performance measure perspective and its factors towards industry revolution 5.0. The research ends up with a typical basic excellence framework that will mash up some characteristics in designing an organizational strategic performance framework. The output is a conceptual performance measure framework for a typical decision-making application for organizational strategic performance management dashboarding.

Building Business Analytic Tool using Dark Data and Big Data

Abstract: The data is turning into the fundamental resource in the present science and innovation. Tragically, a lot of accessible and put away information isn't utilized today. This information is known as dull information. Big data is said to offer not just phenomenal degrees of business knowledge concerning the propensities for buyers and opponents, yet in addition to proclaim an upset in the manner by which business are coordinated and run. Organizations strive to achieve a competitive edge through, big data and business analytics tool. In this paper we have discussed about how dark data is used in organizations and the technologies evolved in business model. We have explored awareness in dark data and how we can implement them in business model. Keywords: Big Data, Dark Data, Business Intelligence (BI), etc.

determinant of business intelligence systems quality on Indonesian higher education information center

PDDIKTI Feeder as a business intelligence application is used as an information center in higher education, containing master data of each student and lecturer, learning process data, reporting graduate data and lecturer activities in teaching for decision making. Paradoxically, through the empirical data there are many problems in implementing business intelligence systems in private universities, related to the maturity of information technology, data quality and information culture. Addressing this gap, we present a descriptive verification analysis research on 40 private universities in Bandung city, Indonesia, using the Partial Least Square Model. We conclude there is a positive influence of information technology maturity, data quality and information culture on the quality of the business intelligence system.

INPLEMENTASI BUSINESS INTELLIGENCE DAN MARKET BASKET ANALYSIS UNTUK ANALISA DATA PENJULAN DI PT. ABC

Ditengah merebaknya kasus pandemi Covid-19 pada tahun 2020 di Indonesia, terjadi perubahan kecenderungan perilaku pelanggan dalam melakukan proses transaksi belanja khususnya pada gerai minimarket. Dengan diberlakukannya pysical distancing, pelanggan dituntut untuk berbelanja seefektif mungkin untuk menghindari penumpukan di dalam gerai. Manajemen perusahaan harus membuat setrategi untuk menyikapi perubahan perilaku dari pelanggan. Pada penelitian ini dikembangkan Business Intelligence dan metode Market Basket Analysis yaitu Apriori untuk menganalisa perilaku pelanggan dengan cara menganalisa riwayat transaksi penjualan. Hasil penelitian menunjukkan dashboard Business Intelligence dapat menampilkan data dalam bentuk grafik dan tabel sehingga memudahkan pengguna dalam proses analisa. Selain itu Association Rule menggunakan metode Apriori menghasilkan nilai support dan confidence sebagai gambaran produk-produk yang saling terkait, sehingga pihak merchendaising dapat dengan  mudah membuat keputusan. Hasil pengujian blackbox menunjukkan aplikasi yang dikembangkan dapat diterima oleh pengguna karena semua kebutuhan pengguna dapat diselesaikan oleh aplikasi.

Determination of Business Intelligence and Analytics-Based Healthcare Facility Management Key Performance Indicators

The use of digital technologies such as Internet of Things (IoT) and smart meters induces a huge data stack in facility management (FM). However, the use of data analysis techniques has remained limited to converting available data into information within activities performed in FM. In this context, business intelligence and analytics (BI&A) techniques can provide a promising opportunity to elaborate facility performance and discover measurable new FM key performance indicators (KPIs) since existing KPIs are too crude to discover actual performance of facilities. Beside this, there is no comprehensive study that covers BI&A activities and their importance level for healthcare FM. Therefore, this study aims to identify healthcare FM KPIs and their importance levels for the Turkish healthcare FM industry with the use of the AHP integrated PROMETHEE method. As a result of the study, ninety-eight healthcare FM KPIs, which are categorized under six categories, were found. The comparison of the findings with the literature review showed that there are some similarities and differences between countries’ FM healthcare ranks. Within this context, differences between countries can be related to the consideration of limited FM KPIs in the existing studies. Therefore, the proposed FM KPIs under this study are very comprehensive and detailed to measure and discover healthcare FM performance. This study can help professionals perform more detailed building performance analyses in FM. Additionally, findings from this study will pave the way for new developments in FM software and effective use of available data to enable lean FM processes in healthcare facilities.

Export Citation Format

Share document.

Business Intelligence and BigData

Ieee account.

  • Change Username/Password
  • Update Address

Purchase Details

  • Payment Options
  • Order History
  • View Purchased Documents

Profile Information

  • Communications Preferences
  • Profession and Education
  • Technical Interests
  • US & Canada: +1 800 678 4333
  • Worldwide: +1 732 981 0060
  • Contact & Support
  • About IEEE Xplore
  • Accessibility
  • Terms of Use
  • Nondiscrimination Policy
  • Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

EU AI Act: first regulation on artificial intelligence

The use of artificial intelligence in the EU will be regulated by the AI Act, the world’s first comprehensive AI law. Find out how it will protect you.

A man faces a computer generated figure with programming language in the background

As part of its digital strategy , the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology. AI can create many benefits , such as better healthcare; safer and cleaner transport; more efficient manufacturing; and cheaper and more sustainable energy.

In April 2021, the European Commission proposed the first EU regulatory framework for AI. It says that AI systems that can be used in different applications are analysed and classified according to the risk they pose to users. The different risk levels will mean more or less regulation. Once approved, these will be the world’s first rules on AI.

Learn more about what artificial intelligence is and how it is used

What Parliament wants in AI legislation

Parliament’s priority is to make sure that AI systems used in the EU are safe, transparent, traceable, non-discriminatory and environmentally friendly. AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes.

Parliament also wants to establish a technology-neutral, uniform definition for AI that could be applied to future AI systems.

Learn more about Parliament’s work on AI and its vision for AI’s future

AI Act: different rules for different risk levels

The new rules establish obligations for providers and users depending on the level of risk from artificial intelligence. While many AI systems pose minimal risk, they need to be assessed.

Unacceptable risk

Unacceptable risk AI systems are systems considered a threat to people and will be banned. They include:

  • Cognitive behavioural manipulation of people or specific vulnerable groups: for example voice-activated toys that encourage dangerous behaviour in children
  • Social scoring: classifying people based on behaviour, socio-economic status or personal characteristics
  • Biometric identification and categorisation of people
  • Real-time and remote biometric identification systems, such as facial recognition

Some exceptions may be allowed for law enforcement purposes. “Real-time” remote biometric identification systems will be allowed in a limited number of serious cases, while “post” remote biometric identification systems, where identification occurs after a significant delay, will be allowed to prosecute serious crimes and only after court approval.

AI systems that negatively affect safety or fundamental rights will be considered high risk and will be divided into two categories:

1) AI systems that are used in products falling under the EU’s product safety legislation . This includes toys, aviation, cars, medical devices and lifts.

2) AI systems falling into specific areas that will have to be registered in an EU database:

  • Management and operation of critical infrastructure
  • Education and vocational training
  • Employment, worker management and access to self-employment
  • Access to and enjoyment of essential private services and public services and benefits
  • Law enforcement
  • Migration, asylum and border control management
  • Assistance in legal interpretation and application of the law.

All high-risk AI systems will be assessed before being put on the market and also throughout their lifecycle.

General purpose and generative AI

Generative AI, like ChatGPT, would have to comply with transparency requirements:

  • Disclosing that the content was generated by AI
  • Designing the model to prevent it from generating illegal content
  • Publishing summaries of copyrighted data used for training

High-impact general-purpose AI models that might pose systemic risk, such as the more advanced AI model GPT-4, would have to undergo thorough evaluations and any serious incidents would have to be reported to the European Commission.

Limited risk

Limited risk AI systems should comply with minimal transparency requirements that would allow users to make informed decisions. After interacting with the applications, the user can then decide whether they want to continue using it. Users should be made aware when they are interacting with AI. This includes AI systems that generate or manipulate image, audio or video content, for example deepfakes.

On December 9 2023, Parliament reached a provisional agreement with the Council on the AI act . The agreed text will now have to be formally adopted by both Parliament and Council to become EU law. Before all MEPs have their say on the agreement, Parliament’s internal market and civil liberties committees will vote on it.

More on the EU’s digital measures

  • Cryptocurrency dangers and the benefits of EU legislation
  • Fighting cybercrime: new EU cybersecurity laws explained
  • Boosting data sharing in the EU: what are the benefits?
  • EU Digital Markets Act and Digital Services Act
  • Five ways the European Parliament wants to protect online gamers
  • Artificial Intelligence Act

Related articles

Digital transformation in the eu, share this article on:.

  • Sign up for mail updates
  • PDF version

This section features overview and background articles for the general public. Press releases and materials for news media are available in the news section .

VIDEO

  1. Business Intelligence and Analytics: Student Life

  2. Business Intelligence & Analytics

  3. BUSINESS INTELLIGENCE LAB TASK 1

  4. Business Intelligence

  5. 8 Benefits of Data-Driven Marketing Research

  6. Business Intelligence and Analytics

COMMENTS

  1. PDF Understanding Business Analytics Success and Impact: A ...

    established area of IS research. Business analytics is "the generation of knowledge and intelligence to support decision making and strategic objectives" (Goes, 2014, p. vi). Business analytics represents the analytical component in business intelligence (Davenport, 2006). Chen et al., (2012) traced the evolution of

  2. (PDF) Business Intelligence

    Although business intelligence systems are widely used in industry, research about them is limited. This paper, in addition to being a tutorial, proposes a BI framework and potential research topics.

  3. [PDF] Business Intelligence and Analytics: From Big Data to Big Impact

    This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A, and introduces and characterized the six articles that comprise this special issue in terms of the proposed BI &A research framework. Business intelligence and analytics (BI&A) has emerged as an ...

  4. (PDF) Business Intelligence Research

    PDF | On Jan 1, 2010, Hsinchun Chen and others published Business Intelligence Research | Find, read and cite all the research you need on ResearchGate

  5. 35 years of research on business intelligence process: a synthesis of a

    Introduction. The business intelligence (BI) process research has grown exponentially during the past three decades into a fragmented state drawing from a diverse set of studies with widely different contributions (Talaoui and Kohtamäki, 2020).Although this pluralism is necessary for the BI process research to generate momentum from insightful findings, it can yield a disjointed theoretical ...

  6. Successful business intelligence implementation: a systematic

    The purpose of this paper is to present a systematic literature review to determine the factors that relate to successful business intelligence (BI) system implementation. Design/methodology/approach The study has a collection of literature that highlights potential references in relation to factors for system implementation in relation to BI.

  7. Review Study: Business Intelligence Concepts and Approaches

    Business Intelligence (BI) is an umbrella concept for tools, techniques and solutions that helps managers to understand business situation. And BI tools can support informational knowledge needs ...

  8. The Effects of Using Business Intelligence Systems on an Excellence

    Business Intelligence (BI) has become established both in practice and in research. BI describes approaches such as collecting, storing, processing, analyzing and presenting company data. ... individual concepts and techniques that are summarized in the term Business Intelligence. In this paper, the following processes will be distinguished in ...

  9. PDF Business Intelligence and Business Value in Organisations: A Systematic

    Previous research efforts managed to establish the link between BI and BV by analysing business capabilities and operational strategic business value [11,16]. However, little is known about how BV is created as most of the papers in the literature concentrate on the technical aspects of BI [11].

  10. Deep Learning in Business Analytics: A Clash of Expectations and Reality

    ML operates mainly in the predictive sphere of business intelligence but has started to incorporate prescriptive analytics as well (Bertsimas & Kallus, 2019). 1.2 Deep Learning Amidst all this formed a new paradigm called deep learning (LeCun, Bengio, & Hinton, 2015) which emerged out of earlier research on brain-inspired neural networks.

  11. Research Landscape of Business Intelligence and Big ...

    Business Intelligence that applies data analytics to generate key information to support business decision making, has been an important area for more than two decades. In the last five years, the trend of "Big Data" has emerged and become a core element of Business Intelligence research. In this article, we review academic literature associated with "Big Data" and "Business ...

  12. Emerging trends and impact of business intelligence & analytics in

    Business Intelligence & Analytics (BI&A) has an increasing impact on decision making and business performance within most organizations today. ... The rest of the paper is organized as follows: Section 2 describes the literature review. Section 3 describes the research methodology which includes k-means clustering and case study method. Section ...

  13. The Impact of Business Intelligence on the Quality of Decision Making

    11. Davenport, TH. Business Intelligence and Organizational Decisions. International Journal of Business Intelligence Research 2010; 1:1. 1-12. 12. Dawson, L, Van Belle, J-P. Critical success factors for business intelligence in the South African financial services sector, SA Journal of Information Management 2013, 15:11, Art. 545, 12 pages. 13.

  14. Business analytics and big data research in information systems

    Business analytics summarises all methods, processes, technologies, applications, skills, and organisational structures necessary to analyse past or current data to manage and plan business performance. While in the past, business intelligence was rather focused on data integration and reporting descriptive analytics, business analytics is ...

  15. (PDF) Business Intelligence and Analytics: Research Directions 1

    Given that modern business intelligence has to heavily depend upon data analytics, it is timely to adopt business intelligence and analytics (BIA) as the preferred combined term. Since about 2004, Web intelligence, Web analytics, Web 2.0, social networking, and microblogging sites have begun to usher in a new and exciting era of Business ...

  16. Data Warehousing and Business Intelligence

    Bill Inmon, recognized by many as the father of the data warehouse, defines the term "data warehouse" as a subject-oriented, nonvolatile, integrated, time-variant collection of data in support of management's decisions. 1 The data warehouse forms the foundation for business intelligence.

  17. business intelligence Latest Research Papers

    Business Intelligence . Performance Measure . Strategic Performance . Performance Framework . Organizational Excellence . Conceptual Performance. In the current digitalization dilemma of an organization, there is a need for the business intelligence and knowledge management element for enhancing a perspective of learning and strategic management.

  18. PDF BUSINESS INTELLIGENCE

    create a richer business intelligence environment than was available previously. Although business intelligence systems are widely used in industry, research about them is limited. This paper, in addition to being a tutorial, proposes a BI framework and potential research topics.

  19. PDF The Impact of Artificial Intelligence on Innovation

    National Bureau of Economic Research. Funding for this paper was provided by the MIT Sloan School of Management, by the HBS Division of Research and by the Questrom School of Management. At least one co-author has disclosed a financial relationship of potential relevance for this research.

  20. The Implications of Big Data Analytics on Business Intelligence: a

    Despite its relevance, no study has been done on the implications of using Big Data analytics in business intelligence [1]. This research addresses this information gap by exploring the role and implications of Big Data analytics on business intelligence with data obtained from social networking platforms such as Facebook.

  21. (PDF) Research Paper on Business Intelligence

    Research Paper on Business Intelligence. June 2021. Sachin Shankar Bhosale. R B Patil. Mr Nikhil Medhekar. The basic goal for Business Intelligence for any enterprise is to structure and analyze ...

  22. Business Intelligence and BigData

    Currently, corporations generate a large amount of data through the products and services offered to their customers. Analyzing this data correctly provides the company with very useful information that allows it to achieve competitive advantages to the company. However, the amount of data that is generated grows exponentially, imploying a challenge for the organization since the data they are ...

  23. EU AI Act: first regulation on artificial intelligence

    As part of its digital strategy, the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology. AI can create many benefits, such as better healthcare; safer and cleaner transport; more efficient manufacturing; and cheaper and more sustainable energy.. In April 2021, the European Commission proposed the first EU ...

  24. (PDF) Artificial Intelligence in Business: From Research and Innovation

    Artificial Intelligence in Business: From Research and Innovation. Department of Electronic Science, University of Delhi South Campus, Delhi-110021, India. ation Training Centre, India ...