Implementation Of Business Intelligence System Research Articles

Critical Success Factors for BI Systems Implementation and Delivery

Despite the widespread acquisition of business intelligence (BI) systems, their implementation has not always been successful. This study examines the critical success factors (CSFs) that impact the implementation of BI systems in organizations. The systematic literature review follows the guidelines of Kitchenham and Charter's research that was published in 2007. A total of 93 articles published between 2011 and 2021 were analyzed for CSF related to BI systems implementation and delivery. The study identified 56 CSFs linked to organization empowerment & operations, 52 CSFs related to system implementation, and 28 CSFs associated with user enablement. The study found a paucity of research on user enablement in the context of BI implementation and delivery, highlighting a gap in the literature. The findings of this study can help organizations better understand the factors that contribute to successful BI system implementation and delivery, and guide future research in this field.

Strategies for success: A theoretical model for implementing business intelligence systems to enhance organizational performance

The use of Business Intelligence Systems (BIS) has seen a significant rise worldwide in recent years, aiming to support organizations in navigating the competitive business environment. Despite this, many organizations struggle to fully benefit from BIS due to challenges in its implementation. A key reason identified for these challenges is the lack of effective measurement strategies. This paper seeks to provide a clear overview of business intelligence and the key factors that influence its successful implementation in organizations. Through a review of existing literature, the study identifies the most critical components necessary for the effective use of a business intelligence system. It proposes a theoretical model for evaluating BIS performance at the organizational level inspired by the Information System Performance Model. This model suggests that system quality, information quality, service quality, relationship quality, and process quality all play a vital role in enhancing perceived usefulness and user satisfaction, thereby leading to organizational benefits. By integrating insights from relevant literature, this paper offers a detailed understanding of how to assess the success of BIS within an organization. The findings highlight the positive impact of business intelligence systems on organizational performance and decision-making processes, aiding organizations in making informed decisions. This research is unique in that it presents a theoretical model for evaluating the success of BIS in organizations based on an extensive review of the literature. Additionally, it extends the application of the Information System Success Model to the domain of BIS for analyzing performance at the management level.

Roadmap for Implementing Business Intelligence Systems in Higher Education Institutions: Systematic Literature Review

Higher education institutions (HEIs) make decisions in several domains, namely strategic and internal management, without using systematized data that support these decisions, which may jeopardize the success of their actions or even their efficiency. Thus, HEIs must define and monitor strategies and policies essential for decision making in their various areas and levels, in which business intelligence (BI) plays a leading role. This study presents a systematic literature review (SLR) aimed at identifying and analyzing primary studies that propose a roadmap for the implementation of a BI system in HEIs. The objectives of the SLR are to identify and characterize (i) the strategic objectives that underlie decision making, activities, processes, and information in HEIs; (ii) the BI systems used in HEIs; (iii) the methods and techniques applied in the design of a BI architecture in HEIs. The results showed that there is space for developing research in this area since it was possible to identify several studies on the use of BI in HEIs, although a roadmap for its implementation was not identified, making it necessary to define a roadmap for the implementation of BI systems that can serve as a reference for HEIs.

The achievement of digital leadership sustainability and business performance through the implementation of business intelligence, artificial intelligence, and quality learning in private universities in Jordan

The objective of research is to explore the impact of business intelligence systems, artificial intelligence, and digital leadership sustainability on the quality of learning and business performance at business schools of private universities in Jordan. Upon gathering and scrutinizing the research materials, a grand total of 281 samples were judged suitable for analysis using PLS software. The findings of this study suggest that the implementation of business intelligence systems, artificial intelligence, and digital leadership sustainability positively contribute to improving both the quality of learning and the performance of enterprises. The results demonstrate that the implementation of a business intelligence system, and Competitive Intelligence, directly and positively impacts the enhancement of learning quality and company performance. Furthermore, Artificial intelligence has a positive influence on the quality of learning and the performance of businesses, namely through the utilization of Deep learning, Digital Data, Graphical Processing Unit (GPU), and Data Safety and Security. Furthermore, digital leadership sustainability exerts a direct and favorable impact on the caliber of education and organizational achievement, encompassing Digital Competence, Flexibility and Agility, Interconnected Thinking, and Employee Orientation. Furthermore, the findings indicate that the level of education, encompassing Summative Assessment, Formative Assessment, Quizzes, and Presentations, is positively associated with enhancing firm productivity. Curiously, the present results contradict previous studies suggesting that the variables being examined had an influence on achieving digital leadership sustainability and business performance. Generally, the main research outcomes recommend that corporations should start adopting strategies related to enhancing learning effectiveness and improving business performance. This research focuses on the major aspects of business cleverness systems, artificial intelligence, and digital leadership sustainability to enhance the quality of learning and business performance in the business capabilities of private institutions of higher education in Jordan.

BUSINESS INTELLIGENCE IMPLEMENTATION ROADMAP FOR HOSPITALITY AND TOURISM INDUSTRY: EXPLORATY WORK

Hospitality and tourism are sectors that generate a large amount of data, from hotel bookings, flights and customer information to data on tourist destinations and tourist preferences. The use of business intelligence in the hospitality and tourism industry enables companies to intelligently analyse this data and make informed decisions to improve their services, optimise operational efficiency, and enhance the customer experience. This research addresses the adoption of business intelligence in hospitality and tourism companies and, in the first phase, aims to identify and characterise the strategic objectives underlying decision-making in hospitality and tourism companies. After reviewing the literature, it was found that there are some studies on the use of business intelligence in companies in the hospitality and tourism sectors, although there is no roadmap to serve as a reference for the implementation of a business intelligence system in companies in these sectors. The objective of this research is to present the methodology of a roadmap proposal for the implementation of business intelligence systems in companies in the hospitality and tourism sectors, which will allow them to improve their ability to analyse and evaluate the data and information available in the different systems and platforms.

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.

Impact of Business Intelligence Systems on Profitability - Empirical Study

There is a research gap regarding the impact of Business Intelligence (BI) implementation on banks' profitability ratios. In particular, this relates to the financial profitability ratios of banks operating under the conditions of the Polish economy after 2015. The main research problem addressed in this study is whether the implementation of Business Intelligence systems has a positive impact on the profitability indicators of commercial banks. The main hypothesis formulated is that the use of a Business Intelligence management system between 2009 and 2020 improves the profitability of Polish commercial banks. The First Differences Generalized Method of Moments (FDGMM) panel dynamic model estimation method was used to evaluate this impact. The parameters of the models based on financial indicators were calculated for each of the selected indicators, and the analysis used those models for which the lagged BI variable, related to the timing of system implementation, proved statistically significant. Based on the obtained parameters of the models, long-term multipliers were calculated. The study determined the probability of the impact of using the business intelligence system on the selected indicators. The study was conducted on a group of the five largest out of the thirteen commercial banks listed on the Warsaw Stock Exchange in 2020. The assets of the surveyed banks comprise 50% of the assets of commercial banks in Poland. The study identified the impact of BI system use on selected profitability indicators of commercial banks. The generalized results of the study enable us to identify causal relationships between the use of the BI system in commercial banks and profitability, as measured by ROE and ROA. The research implications of this study may suggest to top management that implementing business intelligence systems can benefit the organization.

Exploring the Role of Business Intelligence Systems in Digital Healthcare Marketing:

This study explores the potential of business intelligence (BI) systems in facilitating digital healthcare marketing. The healthcare industry has undergone a digital transformation in recent years, with increasing use of digital channels for marketing and customer engagement. BI systems have emerged as a promising technology that can enable healthcare organizations to gain valuable insights into customer behavior, preferences, and trends. Despite the potential benefits of BI systems in healthcare marketing, there is a notable research gap in understanding the role and impact of these systems. This study aims to fill this gap by conducting a qualitative investigation into the experiences and perceptions of marketing professionals in the healthcare sector regarding the use of BI systems for marketing. The study employs a qualitative research design and collects data through in-depth interviews with marketing professionals in the healthcare industry. The analysis of the data reveals that BI systems can provide valuable insights for healthcare marketing, including customer segmentation, personalized marketing, and performance monitoring. However, the study also highlights challenges associated with the implementation and use of BI systems, including data privacy concerns and the need for specialized technical expertise. The findings of this study contribute to the existing literature on BI systems and digital healthcare marketing. The study suggests that BI systems can enable healthcare organizations to make informed decisions about marketing strategies and customer engagement. Furthermore, the study highlights the importance of careful planning and implementation of BI systems to overcome potential challenges and ensure successful adoption in the healthcare sector.

Empirical study of business intelligence systems and their influence on innovation performance

This study aims to empirically measure and test a conceptual model of the magnitude of the influence of organizational culture, IT Maturity Level, and data quality on the successful implementation of business intelligence systems and their implications for innovation performance. The areas of our empirical study through the hypotheses testing were: the estimation of the effect of business intelligence on financial performance; the impact of business intelligence on innovation; the effect of business intelligence on brand success; the impact of innovation on brand success; the impact of innovation on financial performance. The study sample included 4 commercial banks and 58 conventional commercial banks in the category of national private commercial banks of Indonesia, hence, the total sample in this study was 62 banks. This study used non-probability sampling techniques with purposive sampling techniques at conventional commercial banks in Indonesia. The data analysis method uses Covarian Based-Structural Modeling (CB-SEM) with programming tools that support primary data analysis in Lisrell 8.5. The results of this study found that the higher the level of implementation of organizational culture, IT Maturity Level, and data quality, the higher the success rate of implementing business intelligence systems which have implications for improving innovation performance, while the higher the level of application of business intelligence systems, the higher the level of innovation performance. The implementation of organizational culture is the factor that has the most significant influence on the success of implementing business intelligence systems compared to other factors studied in this study.

Power, dominance and control: implementing a new business intelligence system

PurposeThe purpose of this paper is to expose the playout of power dynamics when a new business intelligence (BI) system is implemented in a central pharmacy department in a National Health Service (NHS) hospital. The authors aim to explore the assumptions, experiences and actions of organisational stakeholders and ascertain how different professional groups obtain influence, power and control during this process.Design/methodology/approachIn this research the authors employ structuration theory (ST), to establish how and where domination is achieved. To achieve this, the authors investigate the production and reproduction of structure as part of a longitudinal assessment using interviews and questionnaires.FindingsConstant renewal and evolution are crucial in the implementation of a BI system. During the process of implementation and change many stakeholders resent the change. Disempowering these users leads to new power structures led by BI analysts.Practical implicationsThe findings from this paper can help strengthen implications of BI systems implementation and better understand the impact these systems have on wider stakeholders. With coherent communication and an engaged attitude new BI systems can be implemented without alienating the key user stakeholders.Originality/valueThis paper differs from other papers by advocating that new systems and processes alter individual power structures in organisations, disrupting internal dynamics and introducing new aspects of control and dominance.

Implementation of business intelligence system to analyze the data for mining mechanization – case study

In this article, we focused on the experimental implementation of the Power BI application in the field of mining mechanization. This application was created to demonstrate how an interactive data visualization tool can streamline data exploration and provide new insights that can be used to optimize selected processes. The monitored company uses a large number of reports in connection with the organization of the company. They collect many data from the attendance system and the electronic resource planning system, but also a lot of operational data related to the performance of mining mechanization. These data are evaluated in the company only at one-month intervals. Our intention was to transform the data for the year 2021 and create various visual reports through the power BI program, which, thanks to detailed analysis and evaluation, will help to improve the management of mining mechanization. The source of information in our case were tables that contained very poorly arranged data that could not be imported into the Power BI program in the given state. After collecting the data, we had to transform them, which was a very time-consuming process. Due to limited resources, we had to adjust our original plan. From the entire portfolio of mining mechanization, which consists mainly of mining drills, loaders and trucks, we decided to only process data from mining trucks. The result will be a demonstration of the usability of the power BI system in a mining company to manage information related to mining trucks, but the same approach can be used for all types of mining mechanisms. Implementation of the power BI system was performed on data from one of the biggest underground mines in Slovakia.

A Hybrid Framework for The Implementation of Business Intelligence Systems in Small Scale Enterprises

Small scale enterprises can improve their operations by implementing business intelligence systems. The business intelligence systems are complex and require expertise to ensure successful implementation, hence the need for small scale enterprises to determine their readiness before undertaking the project. To improve chances for successful implementation, this study proposed a framework to guide small scale enterprises on the requirements for business intelligence systems. The design steps defined by Edwards and Goodrich & Tamassia were followed to design the framework. The framework components were informed by the Diffusion of Innovation and Technology Organization and Environment theories, the Information Evaluation Model, and the critical success factors for BIS implementation. A small business may evaluate its resources against the framework components to determine whether to implement a business intelligence system. In future, the framework may be extended to include weights and other criteria to calculate a business’s status.

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.

Capabilities of business intelligence for improving the efficiency of economic analysis in stressful economic conditions caused by pandemic

From the perspective of economic analysis, the phenomenon of business intelligence (BI), which is the main subject of this research, is described as a version of artificial intelligence oriented towards comprehensive analysis of high data volume and its transformation into applied knowledge on the economic activity of organizations. Emphasis is placed on the feasibility and peculiarity of implementation of business intelligence systems in stressful economic conditions caused by pandemic on the global and national scale. The empirical part of research lies in the development and testing of the intelligent solution in the area of information and analytical support for economic analysis. The acquired conclusions prove that the current situation on the global economic market promotes further development of methodology for implementing information technologies, and business intelligence in particular, in the economic analysis of business activity. In order to meet the specific needs of end-users of business intelligence systems, was developed the tool for calculating the economic efficiency of medical insurance for health improvement of the personnel. It is substantiate that innovative BI- technologies help to integrate and successfully apply the solutions of business analysis at any level of corporate management. The authors outline the leading parameters of the ideal version of business intelligence, as well as justify that the use of business intelligence systems in the area of information support for economic analysis gives a multiplicative effect: - contributes to the improvement of economic analysis, efficiency of implementation and accuracy of data; - ensures the effectiveness of economic analysis for achieving  best result with relatively low cost and maximum utilization time; - provides acceptable relevance of the data that underlie the management decisions, leaving out subjectivism.

The Design and Implementation of Business Intelligence System for Lecturer Qualification Analysis based on ETL’s QOX Approach

The Design and Implementation of Business Intelligence System for Lecturer Qualification Analysis based on ETL’s QOX Approach

BUSINESS INTELLIGENCE SYSTEMS SUPPORTING SUSTAINABILITY ON FIRM’S DECISION-MAKING PROCESSES AND PERFORMANCE

In today’s competitive business environment, there exist the need to investigate the adequacy of infrastructural flexibility for Business Intelligence Systems to support Sustainability on Firm’s Decision-making processes and performance. The Business Intelligence Systems consist of unique functions that are intrinsic aimed at supporting better quality decision-making within the organization. It is critical to the smooth operations of every organization. The intention of this study is not to discuss the technical details of implementing Business Intelligence Systems but to explore how to gain maximum benefits from the applications and usage of Business Intelligence Systems. An intensive literature review that highlighted the impact of Business Intelligence on firm performance and decision-making processes were initiated. The focus was on how to apply the Business Intelligence Systems to gain competitive advantage and to create more value from information by aligning it with the needs of employees who are engaged in complex business decision-making in today’s business environment. The study contributed in both academic and industrial operations by providing first time evidence of Business Intelligence solutions with particular reference to Nigerian firms located in Lagos State, Nigeria. The study employed the partial least square analysis with a sample size of 985 respondents randomly selected from five manufacturing companies. The findings suggested that the more effective use of Business Intelligence implementation, the more effective the decision-making processes and firm performance. The study offers a number of implications for theory and practice, noting that Business Intelligence System is in effect allowing firms to shift their structure to more carefully and thoughtfully align with the needs of customers and partners.

ВЗАИМОСВЯЗЬ СОЦИОТЕХНИЧЕСКИХ И ТЕХНОЛОГИЧЕСКИХ АСПЕКТОВ ВНЕДРЕНИЯ И ПРИМЕНЕНИЯ СИСТЕМ БИЗНЕС-АНАЛИТИКИ

This article describes a relation between sociotechnical and technological factors involved in launching and implementing Business Intelligence systems. Advanced BI systems include business analytics, data mining, data visualization, data tools and infrastructure, and advanced IT solutions to support business decisions based on big data. Various industries and businesses handle large amounts of data to adapt to changing markets and demand fluctuations, push new technologies, and repair ineffective strategies, etc. With an upsurge in data sizes, more and more new research papers are published today to describe BI implemen-tation, use and results. However, today most studies and scientific publications focus on Business Intelligence technological challenges, while sociotechnical aspects – that is processes involved in business decision mak-ing based on big data – are studied in much rarer cases.

Opportunities and risks of implementing business intelligence systems in small business activities

The article analyzed the opportunities of Business Intelligence (BI) systems as an effective solution in eliminating existing problems in small business activities and determining prospects. As a result of the analysis, the advantages and disadvantages of BI systems in determining the prospects of small business activities were discussed. Further stages of the study were identified as a result of the analysis. As “data”, it is possible to understand the daily incoming voluntary view data collected in the process of business activities can be collected by software-hardware systems, that is, “raw irregular data”.

Business intelligence systems and operational capability: an empirical analysis of high-tech sectors

PurposeIn this research, we empirically examine the impact of Business Intelligence (BI) systems on operational capability in high-tech sectors. We also seek to understand the contextual factors that facilitate the adoption of BI systems.Design/methodology/approachWe adopt Propensity Score Matching (PSM) and event study methodology, and analyze the financial data for a sample of 144 US firms which adopted BI systems from 2005–2014, and compare them to control firms without BI systems.FindingsWe find that the implementation of BI systems leads to higher operational capability, particularly for large high-tech firms with high technology intensity. We further show that technology intensity and firm size are important contextual factors for firms to reap the benefits of BI systems.Practical implicationsWe demonstrate how benefits from the adoption of BI systems are likely to be strengthened. The benefits of BI systems depend on firms' technology intensity and firm size of high-tech firms. Accessing relevant and timely reports for decision-making is particularly important in the highly dynamic, volatile and competitive high-tech sectors.Originality/valueWe contribute to the literature by providing empirical evidence that the adoption of BI systems can improve firms' operational capability and show that technology intensity and firm size are important contextual factors for firms to reap the benefits of BI systems. We advance the understanding regarding the contextual factors in which firms are more likely to gain additional benefits from their adoptions of BI systems.

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.