Business Intelligence Projects Research Articles

Systematic Literature and Expert Review of Agile Methodology Usage in Business Intelligence Projects

Background: Agile methodology is known for delivering effective projects with added value within a shorter timeframe, especially in Business Intelligence (BI) system which is a valuable tool for informed decision-making. However, identifying impactful elements for successful BI implementation is complex due to the wide range of Agile attributes. Objective: This research aims to systematically review and analyze the integration of BI within Agile methodology, providing valuable guidance for future projects implementation, enhancing the understanding of effective application, and identifying influential factors. Methods: Based on the Kitchenham method, 19 papers were analyzed from 288 papers, sourced from databases like Scopus, ACM, IEEE, and others published in 2016-2022. Meanwhile the extracted key factors impacting agile BI implementation were validated by qualified expert. Results: Agile was discovered to provide numerous benefits to BI projects by promoting flexibility, collaboration, and rapid iteration for enhanced adaptability, while effectively addressing challenges including those related to technology, management, and skills gaps. In addition, Agile methods, including tasks such as calculating cycle time, measuring defect backlogs, mapping code ownership, and engaging end users, offered practical solutions. The advantages included adaptability, success, value enhancement, cost reduction, shortened timelines, and improved precision. The research additionally considered other critical Agile elements such as BI tools, Agile Practices, Manifesto, and Methods, thereby enhancing insights for successful implementation. Conclusion: In conclusion, the research outlined Agile BI implementation into seven key factor groups, validated by qualified expert, providing guidance for BI integration and practices, and establishing a fundamental baseline for future applications. Keywords: Agile Methodology, Business Intelligence (BI), Expert Judgement, Kitchenham, Systematic Literature Review (SLR)

THE IMPACT OF BUSINESS INTELLIGENCE SYSTEMS ON PROJECT MANAGEMENT IN HIGHER EDUCATION: AN EMPIRICAL STUDY

The successful completion of projects depends on effective project management, particularly in the complex and dynamic environment of higher education institutions. Business intelligence (BI) tools can assist identify problems and hazards, facilitate decision-making, and offer insightful information on how projects are performing. The purpose of this study is to examine how BI systems are used for project management in higher education institutions and to suggest a successful project management strategy that incorporates BI systems. To determine the present state-of-the-art in project management and BI systems in higher education, the research technique includes a rigorous literature review of academic and industry sources. An analysis of a case study from a university that has used a BI system for project management is also included in the Study. The study's conclusions imply that BI systems can enhance project management at higher education institutions by supplying precise real-time data on project performance, risks, and problems. By facilitating better decision-making, early issue detection, and prompt problem resolution, the deployment of a BI system has improved project management, according to the case study analysis. The study suggests a successful project management strategy that incorporates BI systems in higher education institutions based on the findings. The following steps make up the suggested strategy: Project objectives and scope definition, project planning, BI system implementation, BI system performance monitoring, project issue and risk identification and mitigation, and continuous improvement are the steps in a project. The suggested framework can assist institutes of higher learning in managing projects successfully by making use of BI tools. The paper also outlines the critical success criteria for BI system deployment in project management in higher education institutions, such as top management support, transparent communication, user training, and efficient data governance.

Navigating the acceptance of implementing business intelligence in organizations: A system dynamics approach

The rise of information technology has transformed the business landscape, with organizations increasingly relying on information systems to collect and store vast amounts of data. To stay competitive, businesses must harness this data to make informed decisions that optimize their actions in response to the market. Business intelligence (BI) is an approach that enables organizations to leverage data-driven insights for better decision-making, but implementing BI comes with its own set of challenges. Accordingly, understanding the key factors that contribute to successful implementation is crucial.This study examines the factors affecting the implementation of BI projects by analyzing the interactions between these factors using system dynamics modeling. The research draws on interviews with five BI experts and a review of the background literature to identify effective implementation strategies. Specifically, the study compares traditional and self-service implementation approaches and simulates their respective impacts on organizational acceptance of BI. The results show that the two approaches were equally effective in generating organizational acceptance until the twenty-fifth month of implementation, after which the self-service strategy generated significantly higher levels of acceptance than the traditional strategy. In fact, after 60 months, the self-service approach was associated with a 30% increase in organizational acceptance over the traditional approach. The paper also provides recommendations for increasing the acceptance of BI in both implementation strategies. Overall, this study underscores the importance of identifying and addressing key factors that impact BI implementation success, offering practical guidance to organizations seeking to leverage the power of BI in today's competitive business environment.

Prioritizing Barriers and Strategies Mapping in Business Intelligence Projects Using Fuzzy AHP TOPSIS Framework in Developing Country

Business Intelligence (BI) is an essential technology in an increasingly competitive landscape since it helps make decisions more accurately. To achieve an effective BI implementation, the organization must formulate the right strategy to overcome its challenges. This research aimed to develop a framework to map barriers into strategies using qualitative and quantitative methods. The qualitative approach is driven by interviewing BI experts to validate the barriers and strategies previously obtained. Based on the interview, there are 19 barriers and 9 strategies that could be used. The quantitative approach compiles a priority list of the most significant barriers and the most effective strategies to overcome these barriers using fuzzy AHP TOPSIS, an MCDM method to eliminate inconsistencies during ranking. The results indicate that the lack of collaboration between the IT and BI departments, the BI implementation demands to be done quickly, and low data quality are the main barriers that hinder BI's success. This research also found that business people's involvement in a BI project is the best strategy to overcome the obstacles. The chances of a successful BI implementation will increase by having good cooperation between IT and business units within the company. Doi: 10.28991/ESJ-2022-06-02-010 Full Text: PDF

A Framework for Ranking Critical Success Factors of Business Intelligence Based on Enterprise Architecture and Maturity Model

Aim/Purpose: The aim of this study is to identify Critical Success Factors (CSF) of Business Intelligence (BI) and provide a framework to classify CSF into layers or perspectives using an enterprise architecture approach, then rank CSF within each perspective and evaluate the importance of each perspective at different BI maturity levels as well. Background: Although the implementation of the BI project has a significant impact on creating analytical and competitive capabilities, the lack of evaluation of CSF holistically is still a challenge. Moreover, the BI maturity level of the organization has not been considered in the BI implementation project. Identifying BI critical success factors and their importance can help the project team to move to a higher maturity level in the organization. Methodology: First, a list of distinct CSF is identified through a literature review. Second, a framework is provided for categorizing these CSF using enterprise architecture. Interviewing is the research method used to evaluate the importance of CSF and framework layers with two questionnaires among experts. The first questionnaire was done by Analytical Hierarchy Process (AHP), a quantitative method of decision-making to calculate the weight of the CSF according to the importance of CSF in each of the framework layers. The second one was conducted to evaluate framework layers at different BI maturity levels using a Likert scale. Contribution: This paper contributes to the implementation of BI projects by identifying a comprehensive list of CSF in the form of a holistic multi-layered framework and ranking the importance of CSF and layers at BI maturity levels. Findings: The most important CSF in BI implementation projects include senior management support, process identification, data quality, analytics quality, hardware quality, security standards, scope management, documentation, project team skills, and customer needs transformation, which received the highest scores in framework layers. In addition, it was observed that as the organization moves to higher levels of maturity, the average importance of strategic business and security perspectives or layers increases. But the average importance of data, applications, infrastructure, and network, the project management layers in the proposed framework is the same regardless of the level of business intelligence maturity. Recommendations for Practitioners: The results of this paper can be used by academicians and practitioners to improve BI project implementation through understanding a comprehensive list of CSF and their importance. This awareness causes us to focus on the most important CSF and have better planning to reach higher levels of maturity according to the maturity level of the organization. Future Research: For future research, the interaction of critical success factors of business intelligence and framework layers can be examined with different methods.

EDUCATIONAL DATA MINING AND DATA WAREHOUSE DESIGN USING BUSINESS INTELLIGENCE

Business intelligence (BI) is basically a collection of programs, utilities and apps that allows you to analyze large quantities of data (Big Data). Every year, educational institutions deal with enormous amounts of Big Data. The use of business intelligence (BI) in these organizations is critical for improving processes and supporting decision-making. A Datawarehouse is the foundation of every BI project (DW). The scheme deliberations for implementing DW in any of the institutional setting are described in this article. Using educational data mining (EDM) methods, the DW shall be utilized in an information unearthing procedure to manage the statistics and facts for the study of vital accomplishment metrics. A knowledge management framework's main technical assets are a data warehouse (DW) and an enterprise architecture (EA) repository (KMF). The agenda further was created to provide order to the processes of knowledge generation, capture, transfer, and digitization. This handbook and the framework are two of the results of a private university's research effort. In addition, an illustrative example demonstrates picking of the finest technique in higher education. The stages for DW design are given in the case study. This research may help academics and practitioners who want to create a data warehouse to analyze data utilizing EDM methods.

Minimising Risk—The Application of Kotter’s Change Management Model on Customer Relationship Management Systems: A Case Study

Implementing a Customer Relationship Management (CRM) system requires significant consideration with respect to change management and the associated business risks. This paper describes how to best achieve the change goal and minimize these risks. The research question under investigation is: “How can Kotter’s change management model be used effectively to enhance the value and utilisation of a CRM system”. Kotter’s eight-stage change model is the adopted change model used by the organisation under study. As business intelligence (BI) is a growing field within industry and academia alike, limited substantive research has been done regarding how to manage the change process itself within a BI project. Often research either focuses on the technical development (e.g., agile methodology) or the change process from a holistic perspective. However, both are needed to effectively manage the risk of failure. The research design for this study was that of a single organisation case study. The research questions were addressed by using a deductive research style. To allow for multiple perspectives and triangulation of the data, a mixed-methods approach (Quant + QUAL) was used. Outcomes of the research showed that whilst there was some success in the implementation of Kotter’s change model, it could have been significantly improved if the competencies identified in this research were considered and incorporated prior and during the change journey. Building on Kotter’s classic work with change management, this research fills the gap by describing the pertinent competencies required in managing the change process, identifying common pitfalls and investigating the common threads between the ‘data to outcome’ process and the change management process to better mitigate the risk This paper adds value to current change literature/models by defining and describing the importance of these competencies when embarking on a change program related to BI tools and systems and how these competencies are incorporated into Kotter’s model.

Statistical Assessment of Business Intelligence System Adoption Model for Sustainable Textile and Apparel Industry

The textile and apparel industry is one of the biggest competitive industries in the world. Nowadays, industry 4.0 concepts put pressures on textile and apparel companies to integrate advanced technologies. Consequently, Business Intelligence (BI) systems are diffusing rapidly to process large data sets to harness the true value of smart technologies. Regardless of its potentials, most textile and apparel companies are lagging and hesitating to adopt this credible innovation in the presence of a high failure rate (70%-80%) especially in developing countries. To achieve the successful adoption of BI systems, statistical assessment is required to better understand this complex phenomenon. Therefore, a BI system model based on Technology-Organization-Environment (TOE) is developed to evaluate the role of potential determinants pertaining to the users, technology, organization, and environment. Data were collected using a survey with self-administered questionnaires from decision-makers with authoritative designations in the textile and apparel industry, academia, and software companies. Influential relationships among critical determinants were assessed and validated by using Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. The results of this study would contribute to the success of costly BI system projects and will motivate the industry experts to potentially assign investments for the BI projects in the developing countries to sustain in the competitive markets.

Crossing Human Factors Research and Business Intelligence

Starting from business intelligence (BI) reference models, this work proposes to extend the multi-dimensional data modelling approach to integrate human factors (HF)-related dimensions. The overall goal is to promote a fine grain understanding of the derived key performance indicators (KPIs) through an enhanced characterization of the operational level of work context. HF research has traditionally approached critical domains and complex socio-technical systems with a chief consideration of human situated action. Grounded on a review of the body of knowledge of the HF field, this work proposes the business intelligence for human factors (BI4HF) framework. It intends to provide guidance on pertinent data identification, collection methods, modelling, and integration within a BI project endeavour. BI4HF foundations are introduced, and a use case on a manufacturing industry organization is presented. The outcome of the enacted BI project referred in the use case allowed new analytical capabilities regarding newly derived and existing KPIs related to operational performance, providing insight into the value of the BI4HF framework.

Decision-making approach based on Pythagorean Dombi fuzzy soft graphs

A Pythagorean fuzzy set model is more useful than intuitionistic fuzzy set model to handle the imprecise information involving both membership and nonmembership degrees, and a soft set is an other parameterized point of view for handling the vagueness. A Pythagorean fuzzy soft graph is considered more capable than intuitionistic fuzzy soft graph for representing the parametric relationships between objects, and the Dombi operators with operational parameters have creditable extensibility. Based on these two notions, we propose the concept of Pythagorean Dombi fuzzy soft graph (PDFSG). We describe certain concepts of graph theory under Pythagorean Dombi fuzzy soft environment. Further, we define the degree sequence and degree set in PDFSG, and the concept of edge regular PDFSG with consequential properties. Moreover, we illustrate the examples in decision making including selection of suitable ETL software for a business intelligence project and evaluation of electronics companies. Finally, we present the comparison analysis of our proposed model to show the superiority than existing model.

Critical Success Factors for Implementing Business Intelligence Projects (A BI Implementation Methodology Perspective)

Aim/Purpose: The purpose of this paper is to identify Critical Success Factors (CSFs) for Business Intelligence (BI) implementation projects by studying the existing BI project implementation methodologies and to compare these methodologies based on the identified CSFs. Background: The implementation of BI project has become one of the most important technological and organizational innovations in modern organizations. The BI project implementation methodology provides a framework for demonstrating knowledge, ideas and structural techniques. It is defined as a set of instructions and rules for implementing BI projects. Identifying CSFs of BI implementation project can help the project team to concentrate on solving prior issues and needed resources. Methodology: Firstly, the literature review was conducted to find the existing BI project implementation methodologies. Secondly, the content of the 13 BI project implementation methodologies was analyzed by using thematic analysis method. Thirdly, for examining the validation of the 20 identified CSFs, two questionnaires were distributed among BI experts. The gathered data of the first questionnaire was analyzed by content validity ratio (CVR) and 11 of 20 CSFs were accepted as a result. The gathered data of the second questionnaire was analyzed by fuzzy Delphi method and the results were the same as CVR. Finally, 13 raised BI project implementation methodologies were compared based on the 11 validated CSFs. Contribution: This paper contributes to the current theory and practice by identifying a complete list of CSFs for BI projects implementation; comparison of existing BI project implementation methodologies; determining the completeness degree of existing BI project implementation methodologies and introducing more complete ones; and finding the new CSF “Expert assessment of business readiness for successful implementation of BI project” that was not expressed in previous studies. Findings: The CSFs that should be considered in a BI project implementation include: “Obvious BI strategy and vision”, “Business requirements definition”, “Business readiness assessment”, “BI performance assessment”, “Establishing BI alignment with business goals”, “Management support”, “IT support for BI”, “Creating data resources and source data quality”, “Installation and integration BI programs”, “BI system testing”, and “BI system support and maintenance”. Also, all the 13 BI project implementation methodologies can be divided into four groups based on their completeness degree. Recommendations for Practitioners: The results can be used to plan BI project implementation and help improve the way of BI project implementation in the organizations. It can be used to reduce the failure rate of BI implementation projects. Furthermore, the 11 identified CSFs can give a better understanding of the BI project implementation methodologies. Recommendation for Researchers: The results of this research helped researchers and practitioners in the field of business intelligence to better understand the methodology and approaches available for the implementation and deployment of BI systems and thus use them. Some methodologies are more complete than other studied methodologies. Therefore, organizations that intend to implement BI in their organization can select these methodologies according to their goals. Thus, Findings of the study can lead to reduce the failure rate of implementation projects. Future Research: Future researchers may add other BI project implementation methodologies and repeat this research. Also, they can divide CSFs into three categories including required before BI project implementation, required during BI project implementation and required after BI project implementation. Moreover, researchers can rank the BI project implementation CSFs. As well, Critical Failure Factors (CFFs) need to be explored by studying the failed implementations of BI projects. The identified CSFs probably affect each other. So, studying the relationship between them can be a topic for future research.

Pythagorean Dombi fuzzy graphs

Pythagorean fuzzy graph, a broadly used extension of fuzzy and intuitionistic fuzzy graph, is helpful in representing structural relationships between several objects where the relation between these objects is vague, while the Dombi operators with operational parameters have excellent flexibility. Utilizing these two concepts, this research paper proposes the novel concept of Pythagorean Dombi fuzzy graphs (PDFGs). Basically, graph terminology is employed for introducing Pythagorean fuzzy analogs of various fundamental graphical ideas using Dombi operator. Further, under Pythagorean Dombi fuzzy environment, regular, totally regular, strongly regular and biregular graphs are defined with appropriate illustration and some of their crucial properties are examined. Meanwhile, the notion of edge regularity of PDFG is also initiated with substantial characteristics. Finally, a numerical example related to evaluation of appropriate ETL software for a business intelligence project is presented to better understand PDFGs.

Evaluation of Business Intelligence Projects Success – a Case Study

Abstract Background: Many studies found in literature only focus on specific aspects of the evaluation of the success of projects, such as the criteria for evaluation; and just a few are focused on the activities for evaluating success. Objectives: The goal of the paper is to present the process for evaluating the success of Business Intelligence (BI) projects in a large company. Methods/Approach: An exploratory case study was carried out at Tintas Robbialac, SA, a Portuguese company of the paint industry. Results: The specific company approach for evaluating the success of BI projects is presented and discussed. Conclusions: The process for evaluating the success of BI, as well as the evaluation criteria, should be formally defined; and the success should be evaluated and monitored along all the project lifecycle.

Failure Factors in the Control of Large-Scale Business Intelligence Systems Development Projects: Case Study of an Advanced Engineering Firm in Mexico

Failed automation systems engineering projects are difficult to study and there is a lack of case studies regarding these projects. The literature over-represents ERP projects in western countries and theories of antecedents of failure are derived from data collected on ERP projects in the West. Studies from Mexico are almost absent. Also, intelligent applications for businesses are now very topical but few cases focussing on failure factors in business intelligence (BI) projects have appeared. This is in spite of the importance of business intelligence applications for firms in less developed economies in an increasingly globalised economy. This paper presents a challenged project for a complex business intelligence solution in Mexico. It compares ranked failure factors based on academic literature to the Mexican case. Whilst the comparison reveals that similar factors are failure antecedents, their relative importance is different, opening possibilities for further investigations.

Business intelligence governance framework in a university: Universidad de la costa case study

Universities and companies have decision-making processes that allow to achieve institutional objectives. Currently, data analysis has an important role in generating knowledge, obtaining important patterns and predictions for formulating strategies. This article presents the design of a business intelligence governance framework for the Universidad de la Costa, easily replicable in other institutions. For this purpose, a diagnosis was made to identify the level of maturity in analytics. From this baseline, a model was designed to strengthen organizational culture, infrastructure, data management, data analysis and governance. The proposal contemplates the definition of a governance framework, guiding principles, strategies, policies, processes, decision-making body and roles. Therefore, the framework is designed to implement effective controls that ensure the success of business intelligence projects, achieving an alignment of the objectives of the development plan with the analytical vision of the institution.

Data quality and governance in a UK social housing initiative: Implications for smart sustainable cities

Smart Sustainable Cities (SSC) consist of multiple stakeholders, who must cooperate in order for SSCs to be successful. Housing is an important challenge and in many cities, therefore, a key stakeholder are social housing organisations. This paper introduces a qualitative case study of a social housing provider in the UK who implemented a business intelligence project (a method to assess data networks within an organisation) to increase data quality and data interoperability. Our analysis suggests that creating pathways for different information systems within an organisation to ‘talk to’ each other is the first step. Some of the issues during the project implementation include the lack of training and development, organisational reluctance to change, and the lack of a project plan. The challenges faced by the organisation during this project can be helpful for those implementing SSCs. Currently, many SSC frameworks and models exist, yet most seem to neglect localised challenges faced by the different stakeholders. This paper hopes to help bridge this gap in the SSC research agenda.

Business Intelligence Issues for Sustainability Projects

Business intelligence (BI) is an umbrella term for strategies, technologies, and information systems used by the companies to extract from large and various data, according to the value chain, relevant knowledge to support a wide range of operational, tactical, and strategic business decisions. Sustainability, as an integrated part of the corporate business, implies the integration of the new approach at all levels: business model, performance management system, business intelligence project, and data model. Both business intelligence issues presented in this paper represent the contribution of the author in modeling data for supporting further BI approaches in corporate sustainability initiatives. Multi-dimensional modeling has been used to ground the proposals and to introduce the key performance indicators. The démarche is strengthened with implementation aspects and reporting examples. More than ever, in the Big Data era, bringing together business intelligence methods and tools with corporate sustainability is recommended.

Applying structural equation model to study the critical risks in business intelligence and analytical system implementation in Indian retail

In order to reduce the failure rate of business intelligence and analytical system (BIAS) projects in Indian retail, project managers need to analyse and understand the impact of a risk factor on BIAS implementation. The main objective of this paper is to identify the key risk factors solely or primarily for the Indian retail sector. A theoretical model is developed that explains the risk factors which may impact the success of BIAS implementation. Using the SEM, it is found that 40% of the variations in BIAS implementation success can be explained with the help of the model suggested in the research study. The findings from this paper can provide a greater understanding of BIAS implementations. Researchers, practicing managers, and those seeking to implement BIAS in the retail organisation can also use the findings of this study as a vehicle for improving BIAS implementation success in the retail sector.

Identifying key effective factors on the implementation process of business intelligence in the banking industry of Iran

Though many organizations have turned to developing and using businessintelligence systems, not all have been successful in implementing such systems. These systemshave social-technical dimensions with many elements and are very complicated. Numerousstudies have been carried out on implementation and employment of business intelligence, butin the past studies only specific aspects and dimensions have been addressed. The aim of thisstudy is to identify key factors in the implementation process of business intelligence in theIranian banking industry. The present research is objectively applied as a survey study inimplementation strategy. Also it is a descriptive study in terms of the research plan and datacollection where two documentary and field study methods have been used for collecting data.The statistical population of this study comprises experts and professionals in informationtechnology who are active in implementing solutions for business intelligence in the bankingindustry of Iran. In this study, 16 people were chosen based on non-random judgment samplingcombined with targeted and snowball sampling as a statistical sample and their viewpointswere extracted and refined using the Fuzzy Delphi Technique. First through studying pastresearch records and reviewing literature of effective factors in implementing businessintelligence process, 37 factors were identified. Then by implementing five rounds of the FuzzyDelphi Technique, 39 factors were confirmed as significant among 37 factors affecting thebusiness intelligence implementation process in past studies and 10 factors proposed by experts.Also, these 39 factors were classified in nine main groups including organizational, human, dataquality, environmental, system ability, strategic, service quality, technical infrastructure, andmanagerial factors. Managers and executives of business intelligence projects in Iran's bankingindustry can achieve the given objectives and results by considering such significant factors inplanning and taking measures related to effective implementation of business intelligence.

Effectiveness of Agile Compared to Waterfall Implementation Methods in it Projects: Analysis Based on Business Intelligence Projects

Abstract The global Business Intelligence (BI) market grew by 7.3% in 2016 according to the Gartner report (2017). Today, organizations require better use of data and analytics to support their business decisions. Internet power and business trend changes have provided a broad term for data analytics - Big Data. To be able to handle it and leverage a value of having access to Big Data, organizations have no other choice than to get proper systems implemented and working. However, traditional methods are not efficient for changing business needs. Long time between project start and go-live causes a gap between initial solution blueprint and actual user requirements at the end of the project. This article presents the latest market trends in BI systems implementation by comparing agile with traditional methods. It presents a case study provided in a large telecommunications company (350 BI users) and the results of a pilot research provided in the three large companies: media, digital, and insurance. Both studies prove that agile methods might be more effective in BI projects from an end-user perspective and give first results and added value in a much shorter time compared to a traditional approach.