Business Intelligence and Its Challenges

The adoption of Business Intelligence (BI), Big Data, and Advanced Data Analysis techniques (such as Artificial Intelligence) by organizations for decision support is seen as incontrovertible and imperative to be competitive in the global market. If first companies are concerned with the implementation on data platforms for Data Analysis/data science, as per example, Self-Service Business Intelligence, Cloud Data Lakes, Machine Learning frameworks, Mobile Intelligence, regulations (e.g., General Data Protection Regulation – GDPR) and information security (infosec) force organizations to put in place data governance programs and data maturity assessment. The foundations for business results on using data platforms are trusted data, improved risk and analytic decisions, cost reduction and operational efficiency, and regulatory compliance. Being the deployment of BI platforms necessary to store and extract knowledge from data, the data governance is mandatory to assure the central data management, data quality, regulation, security, and privacy. Another important topic, to establish the data roadmap and assess the maturity within an organization, is the adoption of mechanisms to measure the return of investments in BI and Big Data platforms. If currently organizations tend to be data centric, a transversal approach from tools to data governance and maturity control is required.

This study investigates the determinants and outcomes of business intelligence (BI) adoption within Vietnam's rapidly evolving retail sector, addressing critical gaps in understanding how technological capabilities drive strategic performance enhancement in emerging economies. Employing a mixed-method approach combining structural equation modelling (SEM) with fuzzy-set qualitative comparative analysis (fsQCA), the research examines data from 312 retail enterprises across Vietnam's major urban centres. The theoretical framework synthesises the technology-organisation-environment (TOE) framework with dynamic capabilities theory to explicate the complex pathways through which BI adoption influences operational efficiency, customer relationship management, and competitive advantage. The findings reveal that technological readiness, organisational culture, and environmental complexity collectively explain 68% of the variance in BI adoption intensity, whilst BI capabilities demonstrate significant positive effects on strategic performance outcomes (β = 0.742, p < 0.001). The fsQCA results identify four distinct configurational pathways to high performance, suggesting that successful BI implementation requires synergistic combinations of technological infrastructure, managerial support, and environmental alignment. This research contributes to the literature by advancing a comprehensive theoretical model that integrates institutional theory with technological diffusion perspectives, whilst providing practical insights for retail executives navigating digital transformation in emerging markets. The study's implications extend beyond Vietnam's retail context, offering valuable frameworks for understanding BI adoption patterns across developing economies experiencing rapid technological modernisation.

Business Intelligence (BI) is an advanced technology for producing highly intellectual information to ease decision-making, which strengthens growth in revenue and enables obtaining competitive advantages. BI is used in organizations across many industries, however, most struggle to properly implement it and get the desired benefits. Due to a variety of factors, BI adoption among SMEs has remained low, particularly in developing countries. This conceptual paper attempts to critically assess socio-technological network factors by developing a holistic framework for measuring the effects of various interdependent variables of BI adoption on achieving competitive advantages. The proposed model is the pioneer to combine Diffusion of Innovation (DOI) and Actor-Network Theory (ANT) theories for integrating different actors of innovation adoption processes in the organizational context. The study would provide academics and industry experts with detailed insights into the interrelated components of inter-organizational relationship (IOR) in which all players should be treated equally important in the BI adoption process.KeywordsBusiness intelligenceSMETechnology orientationOrganizational mindfulnessInter-organizational relationshipActor-network theory

In the last years the rise of mobile devices and mobile applications alike changed the working environment massively. Within this realm mobile Business Intelligence (mobile BI) currently is one of the trending topics, however, until now very little conceptual work exists in this field. Therefore, we provide an overview of related work in the fields of business intelligence and mobility. Based on our results we derived success factors for mobile BI which shall help academics as well as practitioners to better understand challenges of mobile BI services and their potential solutions.

Business Intelligence is still in its infancy stages in developing countries, particularly Kenya. Research has shown that different models are available to assess the usage and adoption of Business Intelligence (BI). In this case, the technological, organizational, and environmental (TOE) model was proposed as a suitable model for developing economies like Kenya. The study investigates how the TOE constructs affect BI adoption, the BI systems in Kenya, and whether managers influence BI adoption. The equivocal nature of the TOE framework allowed the creation of a structured interview questionnaire that was divided into two parts; the demographic profile and questions based on the TOE framework. The results demonstrated that the TOE factors led to more intensive BI adoption, but there might be a lack of awareness or technical skills to adopt advanced BI technologies. On this basis, it is recommended that managers within small- and medium-sized enterprises (SME) learn about better BI solutions and how they can leverage the advantage to enable them to stay profitable, competitive, and data driven. Further research is needed to better understand BI usage within SMEs preferably with larger and representative sample sizes and across different counties within Kenya.

Effective use of business intelligence has become essential for small and medium-sized organizations (SMEs) in the era of digitalization due to the introduction of new technologies. Therefore, this study aims to measure the influence of the Technology Acceptance Model (TAM) and other factors, for example, the quality of information, organization readiness and technology infrastructure, on business intelligence. A quantitative research methodology was used, with a sample size of 281 participants who were owners, managers and information system staff in Iraqi SMEs who had experience using business intelligence. The findings of this study indicated that the quality of information has a significant impact on perceived usefulness (PU) and perceived ease of use (PEOU). Similarly, PU, PEOU, organization readiness and technology infrastructure positively and significantly impact business intelligence adoption. This study offers a comprehensive analysis of the crucial aspects that contribute to the successful deployment of business intelligence, thereby influencing the outcomes of SMEs. The results of this study will help entrepreneurs, SME owners managers, and academics develop a business intelligence system that can enhance overall organizational efficiency in a dynamic economic setting. Putting in place a good business intelligence system will help managers make better decisions, boost economic growth for businesses, support new ideas in businesses, and improve their overall performance and output.

This paper proposes an ontology of big data analytics and examines how to enhance business intelligence through big data analytics as a service by presenting a big data analytics services-oriented architecture (BASOA), and applying BASOA to business intelligence, where our surveyed data analysis showed that the proposed BASOA is viable for developing business intelligence and enterprise information systems. This paper also discusses the interrelationship between business intelligence and big data analytics. The proposed approach in this paper might facilitate the research and development of business analytics, big data analytics, and business intelligence as well as intelligent agents.KeywordsBig data analyticse-commerceBusiness intelligenceIntelligent agents

Purpose Textile and contemporary apparel manufacturers are adopting and integrating cutting-edge technologies to reduce their impact on the environment and gain an advantage in the marketplace. Most previous studies have ignored business intelligence systems (BIS), notably in the textile and apparel industry (T&A), in favor of looking at the larger picture of how big data would affect retail and distribution in a company. This is especially true for the T&As.Design/methodology/approach The authors report that they conducted 14 semi-structured interviews with 12 international luxury tourism service providers. In this case, researchers use snowball features and systematic techniques to select participants. A qualitative content analysis strategy is used to capture the focus of the interviews.Findings Problems with T&A company sustainability, opportunities to increase value creation via use of industry-leading business intelligence (BI) solutions and perceived roadblocks to BIS adoption were all found by the poll. Garment retail and distribution sector has benefited greatly from the increased use of Industry 4.0 technologies, especially those that provide better BI solutions. Determine the extent to which industry participation slows down or speeds up the process. The Company Information System (BIS) will help convince non-tech-savvy business owners of the financial, economic and environmental benefits of adopting certain technologies developed as part of the industry 4.0 movement.Research limitations/implications The authors of this research claim theirs is one of the first to investigate what variables affect the uptake of BIS, ultimately hoping to find out how BIS may be used by T&A businesses to tackle environmental issues through the use of Industry 4.0 technologies. The purpose of this study was to see whether BIS might aid T&A firms with their sustainability issues.Practical implications In the last several years, there has been a meteoric rise in interest in big data and business analytics among firms and educational institutions alike. This paper tries to introduce readers to the concept of business analytics in a way that is both academic and accessible, considering both the present and future of the field. This paper begins with a quick introduction, followed by a summary of the three distinct forms of predictive modeling discussed.Originality/value In an effort to help aspiring analytics professionals, they have identified, categorized and evaluated the nine distinct players that are now active in the analytics market. Following this, they will provide a high-level summary of the many different research projects currently being worked on by their group.

This study explores the transformative role of big data tools in enhancing business efficiency within Ukraine's digital economy. Using a cross-sectional design, data were collected from 200 managers and experts across diverse industries through a semi-structured questionnaire. The analysis encompassed descriptive statistics, reliability testing, exploratory factor analysis (EFA), regression analysis, and cluster analysis to examine the adoption of predictive analytics, business intelligence, and process automation. Results highlight process automation as the most significant efficiency driver, followed by predictive analytics and business intelligence, enabling streamlined workflows, faster decision-making, and reduced operational costs. Cluster analysis identified three distinct groups of organizations: high adopters achieving notable efficiency gains, moderate adopters facing substantial barriers, and low adopters with targeted benefits but limited efficiency gains. Barriers such as skill shortages, infrastructure gaps, and organizational resistance were prominent among moderate adopters, underscoring the need for targeted interventions. Larger organizations and those led by experienced managers demonstrated greater efficiency, highlighting the importance of resources and leadership in digital transformation. The study emphasizes the need for investment in infrastructure, workforce development, and tailored support for SMEs to unlock the full potential of big data. Future research should focus on longitudinal impacts, sector-specific challenges, and integrating emerging technologies such as AI and IoT. These findings provide actionable insights for policymakers and organizations to foster a data-driven, competitive, and inclusive digital economy in Ukraine.

Developed countries are constantly evolving their information systems by assessing the use of information and incorporating data analysis for decision making and forecasting. Like, developing countries are incorporate information technology standards and business intelligence technologies into their analysis of data to help them make some informed decisions. However, developing countries still have a long way to go to fully realize the potential of business intelligence. Although the spread of information technology in developing countries is rapid, the did not come with a large development benefit of it. Therefore, this study aims to identify factors influencing the adoption of business intelligence in developing countries, such technologies to raise overall socio-economic standards in developing countries. The purpose of this study is how well business intelligence have been adopted, how useful they have been, and the potential facing developing countries in adopting business intelligence and other information. Keywords: Business Intelligence, developing countries.

This research study has focused on the impact of business intelligence and data analytics in the field of accounting. Based on the information collected from scholarly sources, data analytics, and business intelligence is helpful for business organizations in the improvement of their accounting processes and enhancing accuracy in real-time data storing and analysis. The research paper has analyzed the possible impact of big data analytics and business intelligence regarding the transformation of accounting practices. This research paper has also highlighted three main benefits or advantages of data analytics and business intelligence adoptions in accounting systems which include: increasing accuracy, enhancing efficiency, and improvement in the decision-making processes. Based on the research findings we suggest a significant impact of data analytics on the accounting industry which has enabled accounting professionals to draw appropriate decisions based on real-time data. However, some challenges and issues are associated with the implementation and adoption of advanced analytics and business intelligence in accounting systems. These challenges and issues are required to be focused on by the business managers while implementing technological advancement plans in business organizations, especially accounting departments. This research paper has also highlighted the possible issues and challenges with those analytics in accounting systems. The overall objective of this research paper is to review existing literature containing secondary and primary research information on similar research topics.

Business intelligence (BI) is a vital component for businesses of all scales, offering actionable insights crucial for timely decision-making. This technology has become integral across diverse enterprises. Recognizing the factors influencing BI adoption is imperative, and this article employs the organization, complexity, knowledge, technology, user perception and experience, economic, environmental, and social (OCKTUEES) framework to identify key aspects. Building upon the TOE framework, it pinpoints significant variables, emphasizing the importance of factors like user perception and experience, technology, social, economical, and environmental. Employing structural equation modelling on primary data yields actionable insights to address BI adoption challenges. Analysis reveals the user perception and experience, technology, social, economic, and environmental as the top factors. However, the organization appears vulnerable, necessitating a mitigation strategy for successful BI adoption. The study predicts insignificant variables requiring mitigation, such as high costs, inadequate resources, organizational size, security and privacy concerns, risk of open-source adoption, and perception of analytics impacting jobs. This research aids those navigating the BI implementation journey.

While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.

This paper examines big data and the opportunities it presents for improved business intelligence and decision making. Big data comes in multiple forms. It can be structured, semi-structured, or unstructured. The opportunity it presents is that there is so much of it and it is readily available to organizations. Organizations use big data for business intelligence (BI). They can apply analytics in BI activities to assess big data in order to gain new insights and opportunities for decision making. The problem is that oftentimes the data is of poor quality and it contains personal information. This paper explores these issues and examines the importance of effective data management in facilitating sound business intelligence. The Master Data Management methodology is reviewed and the importance of management support in its deployment is emphasized. With the advent of new sources of big data from IoT devices, the need for even more management involvement is stressed to ensure that organizational BI yield sound decisions and that use of data are in compliance with new regulations.

Business Intelligence (BI) is a collection of tools, technologies, and practices that include the entire process of collecting, processing, and analyzing qualitative information, to help entrepreneurs better understand their business and marketplace. Every day, social networks expand at a faster rate and pace, which sees them as a source of Big Data. Therefore, BI is developed in the same way on VoC (Voice of Customer) expressed in social media as qualitative data for company decision-makers, who desire to have a clear perception of customers’ behaviour. In this article, we present a comparative study between traditional BI and social BI, then examine an approach to social business intelligence. Next, we are going to demonstrate the power of Big Data that can be integrated into BI so that we can finally describe in detail how Big Data technologies, like Apache Flume, help to collect unstructured data from various sources such as social media networks and store it in Hadoop storage.