A business intelligence framework for sustainability information management in higher education

Business Intelligence (BI) is one of the fastest growing software sector and software vendors are rapidly developing multiple BI tools to support the growing data analysis needs of organisations. In order to be sustainable in a briskly changing turbulent environment, organisations need to have access to information about their operational performance. BI tools play a vital role in supporting the decision makers at different organisational levels. As these tools are becoming critical in decision making, it has become not only an information technology concern but also a management concern. Without proper governance it would be impractical to achieve the value that BI tools offer. Adopting a BI governance framework in organisations will lead to common principles and clear ownership over information. Additionally, appropriate alignment between corporate governance and BI governance can yield more benefits. This paper provides an insight into the importance and value of BI tools. Key functionalities of BI tools have also been highlighted. Different challenges in gaining true value from BI tools have been examined. Four phases of developing a BI governance framework have been illustrated. The alignment between BI governance and corporate governance has also been explored with a recommended model. Exploratory analysis of two organisations (Premier Healthcare Alliance & BellSouth Telecommunications) to identify how they have utilised BI tools and adopted BI governance has been briefly carried out. The paper posits that if the steps for developing a BI framework are adopted by organisations and the BI framework is aligned with the corporate framework, BI deployment and usage will be successful with reduced risk levels.

Oil and gas companies are increasingly using data analytics to improve drilling performance. This paper provides an example of using a business intelligence (BI) tool to analyze drilling data in the Permian Basin. The BI tool helped to improve operation decisions through the use of a visual report. A database, consisting of massive amounts of historical drilling data, is analyzed using the BI tool to better understand drilling performance and predict average operations performance in the area. A historical drilling database is created based on the bottomhole assembly (BHA) run data and analyzed by the BI tool to review the well performance, in addition to identifying any hazards and summarizing the optimum drilling system during the planning phase. With the help of the BI tool, the drilling database can be displayed in an interactive way to further understand the drilling performance in the area; e.g., the top performing drill bit, drilling system, downhole mud motor configuration, and estimated drilling time for the section of interest. As a result, engineers will find it easier to identify the potentially top performing wells along with drilling hazards in offset wells. The engineers can evaluate the well details and identify the best drilling practices to optimize drilling performance and eliminate downhole incidents. Using the BI tool helps reduce the data mining time and offers a fast, improved method for gaining technical insights into the drilling operation. These descriptive analytics help to simplify the complex data sets, which are valuable for uncovering patterns that offer data set understanding. With the visualization results, experts can focus on data diagnostics analytics to make suggestions for drilling operation improvements and corrections. Furthermore, these analytical data can be used as inputs for more advanced predictive (forecast drilling performance) or prescriptive analytics (drilling optimization) that deliver real-time insights for making improved business decisions.

- In today’s data-intensive business environment, organizations increasingly depend on Business Intelligence (BI) tools to support evidence-based decision-making and sustain competitive advantage. This study examines how managers at Mind Box LLC perceive and utilize BI tools as Decision Support Systems (DSS) in their routine and strategic decision processes. Using a descriptive research design, primary data were collected from 100 managers across departments through a structured questionnaire, complemented by secondary data from journals and company documents. The analysis employed percentage statistics, chi-square tests, correlation, and one-way ANOVA to evaluate BI usage patterns, perceived benefits, challenges, and satisfaction levels. Results show high adoption of BI tools, with 75% of managers using them at least weekly and a majority reporting moderate to advanced BI knowledge. Managers report that BI tools improve decision quality, reduce uncertainty and risk, enable faster decisions, and contribute to competitive advantage. Key barriers include insufficient training, data quality issues, and system complexity, while no significant differences in satisfaction were found across managerial levels. The findings underscore that BI tools are effective DSS enablers when supported by robust data governance, continuous training, user-friendly design, and a data-driven culture. The study offers practical recommendations for strengthening BI adoption and maximizing its decision support value at Mind Box and similar technology-driven organizations. This study contributes to the growing literature on BI-enabled decision-making in technology-driven organizations. Keywords: Business Intelligence, Decision Support Systems, managerial perception, data-driven decision-making, Mind Box.

This paper provides a comprehensive overview of the current trends in Business Intelligence (BI) tools within the finance sector, with a specific focus on the divergent landscapes of the USA and Africa. As the financial industry evolves, the integration of advanced BI tools has become imperative for informed decision-making and sustainable growth. This review examines key developments, challenges, and opportunities shaping the implementation of BI tools in both regions. In the USA, financial institutions are witnessing a surge in BI tool adoption, driven by the increasing need for real-time data analytics, regulatory compliance, and risk management. The integration of artificial intelligence and machine learning algorithms in BI tools is enabling predictive analytics, empowering financial professionals to make strategic decisions in a rapidly changing market environment. Additionally, the emergence of cloud-based BI solutions is enhancing scalability and accessibility, facilitating seamless collaboration and data-driven insights. Contrastingly, the African financial landscape is experiencing a gradual but promising shift towards BI tool utilization. The continent, characterized by diverse economies and regulatory frameworks, faces unique challenges such as infrastructure limitations and varying levels of technological readiness. However, the potential for BI tools to revolutionize financial operations, improve transparency, and facilitate financial inclusion is evident. Governments and financial institutions are investing in technology infrastructure and skill development to harness the transformative power of BI tools. This review underscores the importance of understanding regional nuances in the adoption of BI tools, recognizing that effective implementation requires tailored strategies. As both the USA and Africa navigate the evolving financial landscape, the convergence of technological advancements and strategic planning will undoubtedly play a pivotal role in shaping the future of BI tools in the finance sector

The concept of Business Intelligence (BI) was first proposed in the late 1980-s. Since then the field of BI has experienced the rapid extension and development. More than 50 billion dollars have already been invested in different BI tools and application software, such as new desktop data analysis tools, data warehousing technologies, data extraction middleware and many other tools and techniques into the market. But not all the BI initiatives are successful. This paper posits that a key antecedent of BI success is having the right BI Capabilities, and right BI Capabilities depend on the Decision Environment in which the BI is used. Research suggests that a lack of fit between an organization and its BI is one of the reasons for lack of success. The match between the Decision Environment and what a BI provides has been studied as an indicator of success. The theoretical framework is proposed in this paper to study relationship between BI Capabilities, BI Success and Decision Environment

This study explores the integration of survey-based student feedback with Business Intelligence (BI) tools to optimize the student experience in higher education. As institutions increasingly aim to improve student satisfaction and engagement, leveraging survey data in combination with BI tools provides a data-driven approach to identifying key areas for enhancement. The research proposes a conceptual framework that facilitates the collection, analysis, and application of student feedback using BI technologies, enabling higher education institutions to make informed, real-time decisions. The study reveals that while students report high satisfaction with academic quality, there are notable gaps in support services, indicating a need for improvement. Furthermore, the use of BI tools uncovered trends and correlations in student feedback that traditional analysis methods overlooked, allowing for more proactive and personalized interventions. The findings suggest that the integration of BI tools not only improves institutional responsiveness but also leads to higher student satisfaction and retention rates. This paper concludes with recommendations for future research, emphasizing the importance of ethical data use and exploring the long-term impact of BI-driven interventions. Educational institutions are encouraged to invest in BI infrastructure and adopt data-driven decision-making to enhance student experiences and outcomes.

The act of applying game mechanics and dynamics (which have been traditionally used in video games) into business applications is being widely trialled in an effort to make conventional business software a bit more participative, fun and engaging. This new trend, named `gamification' has its believers and of course, its critics who still need convincing that the concept is an effective and beneficial business tool worthy of investment. The literature reveals that user engagement of business intelligence (BI) tools is much lower than expected and investors are failing to get a good return on their investment (ROI). So, a software prototype will be designed and developed to add gamification to a BI tool to determine its effect upon the user engagement levels of test participants. The experimental study will be evaluated using the comprehensive User Engagement Scale (UES) to see if there are improvements in areas such as; aesthetics, perceived usability, endurability, novelty, felt involvement and focused attention. The results of this unique study should demonstrate whether or not `gamifying' a BI tool has the potential to increase an individual's motivation to use BI software more often.

This study extends the existing literature of users' acceptance of business intelligence technologies by proposing a comprehensive model integrating personality five factors model (FFM) and technology acceptance model (TAM) to investigate the intention to use business intelligence (BI) tools to support business decision making. We expect that this study will present an example of the integration between FFM and TAM to provide a better understanding about the relationship between personality and intention to use within the context of BI.

IntroductionThe growing competitiveness and the importance of data availability for organizations have created a demand for intelligent information systems capable of analyzing data to support strategy and decision-making. Organizations are generating more and more data due to new technologies associated with Industry 4.0 and Logistics 4.0, making it essential to transform this data into relevant information to streamline decision-making processes. This paper examines the influence of these technologies on gaining a competitive advantage, specifically in a logistics company, which is scarce in the literature.MethodsA case study was conducted in a Portuguese company using the Delphi method with 61 participants—employees who use the company’s integrated BI tool daily. The participants were presented with a questionnaire via the online platform Welphi, requiring qualitative responses to various statements based on the literature review and the results of semi-structured meetings with the company.ResultsThe study aimed to identify areas where employees believe more investment/ development is needed to optimize processes and improve the use of the BI tool in the future. The results indicate that BI is a crucial technology when aligned with a company’s objectives and needs, highlighting the necessity of top management’s involvement in optimizing the BI tool. Encouraging employees to use the BI tool emerged as a significant factor, underscoring the importance of leadership in innovative projects to achieve greater competitive advantage for the company.DiscussionThis study aims to understand the importance of Business Intelligence (BI) and how its functionalities should be adapted according to a company’s strategy and objectives to optimize decision-making processes. Thereby, the discussion focused on the essential role of BI technologies in leveraging the company’s competitive advantage.

Globally, enterprises are undergoing significant transformation in line with developments based on industrial revolution by leveraging extensive computing resources, data capture technologies, information processing systems, and advanced data science models that span analytics, optimization, and algorithmic intelligence. The global market's increasing demand, diverse resource supply options, global competition, and environmental protection needs are driving organizations to adopt sustainable strategies. These involve utilizing information technology and analytics more effectively, innovating manufacturing and service support systems, and employing novel problem-solving methods. The awareness of these challenges and opportunities inspired the theme of the joint event: 56th Annual Convention of ORSI (2023-ORSI) and the 10th International Conference on Business Analytics and Intelligence (2023-ICBAI), held at Indian Institute of Science, Bangalore, India, from December 18 to 20, 2023. The Operational Research Society of India (ORSI) Karnataka, the Department of Management Studies, IISc Bangalore, and the Analytics Society of India (ASI), DCAL, IIM Bangalore jointly organized this event. The joint event aimed to establish a premier platform for knowledge sharing among distinguished practitioners, academics, and researchers from industry and academia, focusing on the current applications of Operations Research (OR), Business Analytics (BA), and Business Intelligence (BI). The conference received over 655+ paper submissions, with 455 selected for presentation. 66 papers were deemed particularly interesting, and authors of 13 promising articles were invited to submit extended versions for a special issue of International Journal of Mathematical, Engineering and Management Sciences (IJMEMS). After rigorous peer review, eight papers were accepted for publication in this special issue, addressing common challenges in Operations Research, Business Analytics, and Business Intelligence. This Special Issue of the IJMEMS explores recent developments in Operations Research, Business Analytics, and Business Intelligence. It presents cutting-edge trends and substantial contributions to key areas such as Scheduling problems, Transshipment problems, E-commerce, Nanofluids, Blockchain Technology, Generative AI, Augmented Analytics, Machine Learning, and real-time Anomaly Detection. This Special Issue delves into following eight topics: Unmasking Content Clarity: Advancements in Defining, Measuring and Enhancing Readability: The authors present a novel method using natural language processing and Generative AI to quantitatively evaluate readability and comprehension. This approach surpasses traditional readability indices, offering substantial benefits for content creation and knowledge management in fields like education, business, technical support, and policy platforms. Strategic Insights into Blockchain Adoption in Automotive Supply Chains: A Comparative AHP-TOPSIS and TISM-MICMAC Analysis The authors explore blockchain adoption in the automotive industry using a multidisciplinary approach involving AHP, TOPSIS, TISM, and MICMAC analyses. This study identifies key enablers and their relationships, offering actionable insights and practical recommendations for automotive managers considering blockchain adoption. Avoid Maximum Cost Method for Solving Linear Fractional Transshipment Problem: The authors introduce a mathematical model for the linear fractional transshipment problem (LFTP) and suggest “Avoid Maximum Cost Method” to obtain an initial basic feasible solution for LFTP. This study conducts a comparative analysis with existing methods to demonstrate the efficiency of the proposed approach. Mathematical Study of Dispersion of Nano Biosensors in an Artery with Multiple Stenosis: This work examines nano-biosensors in a diseased artery with multiple stenoses, determining the temperature, velocity of nanofluid, and transport coefficients. The results lay the groundwork for developing nano-biosensors to diagnose, treat, and manage cardiovascular disease. The mathematical model had possible scope for target detection and drug delivery at stenosed sites. Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics: This study offers a practical framework for integrating generative AI (Gen AI) into Business Intelligence (BI). By adopting it, businesses can maximize GenAI and BI's potential, enhancing analytics, operations, and fostering a collaborative, data-driven culture. Data Monetization Through Cross Industry Collaboration in Retail Banking: This paper examines how data sharing between banks and e-commerce platforms, facilitated by data monetization, can improve banking customer experiences. This study proposes a framework using propensity models to identify promising customers and offer personalized products and promotions. Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction: This study tackles a realistic problem in semiconductor manufacturing by scheduling non-identical parallel Burn-in ovens. The study proposes 25 heuristic algorithms for real-time dynamic scheduling with machine eligibility restrictions. Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. Retraining and false positives were minimized, outperforming traditional methods in experiments. Challenges and Future Directions 1. Enhanced Decision-Making: Addressing uncertainty and complexity in decision-making processes to improve outcomes. 2. Regulatory Frameworks: Conducting further research to establish flexible yet robust regulatory structures that can effectively adapt to the rapid evolution of blockchain technology. 3. Optimization Algorithms: Developing efficient algorithms to solve linear fractional transshipment problem with multi-objective linear fractional functions. 4. AI in Business Intelligence: Investigating long-term impacts of AI-enabled Business Intelligence tools on data-driven decision-making and organizational performance. 5. Ethical AI Considerations: Addressing ethical concerns such as data privacy, fairness, and biases in AI systems to ensure responsible use. 6. Digital Collaboration Frameworks: Developing frameworks that integrate digital footprints and cross-industry collaboration data to enhance strategic partnerships. 7. Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. Advanced Text Generation: Utilizing advanced text generation techniques like prompt engineering and fine-tuning to produce more readable and engaging content. We extend our sincere appreciation to all contributing authors for their significant contributions and anonymous reviewers for their dedication and sincere evaluation of submissions. Their timely and excellent responses have been truly gratifying. Additionally, we would like to express our heartfelt thanks to Professor Mangey Ram, Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, for his support in accepting this special issue and providing unwavering backing from its inception. Guest Editors

In recent years, a wide range of Business Intelligence (BI) technologies have been applied to different areas in order to support the decision-making process. BI enables the extraction of knowledge from the data stored. The healthcare industry is no exception, and so BI applications have been under investigation across multiple units of different institutions. Thus, in this article, we intend to analyze some open-source/free BI tools on the market and their applicability in the clinical sphere, taking into consideration the general characteristics of the clinical environment. For this purpose, six BI tools were selected, analyzed, and tested in a practical environment. Then, a comparison metric and a ranking were defined for the tested applications in order to choose the one that best applies to the extraction of useful knowledge and clinical data in a healthcare environment. Finally, a pervasive BI platform was developed using a real case in order to prove the tool viability.

This systematic literature review explores the impact of Natural Language Processing (NLP) in developing Business Intelligence (BI) systems focusing on the rise of Conversational Business Intelligence (CBI). It seeks to determine how NLP can improve user accessibility, decision making, and options available in navigating integration concerns in BI frameworks. Using the PRISMA 2020 guidelines, the review examined 18 peer-reviewed studies presented in the period between 2019 and 2024 through the Google Scholar and the Saudi Digital Library. Inclusion criteria based on pre-set criteria of NLP’s utilization in BI were applied to studies, and for their quality – methodological rigor and relevance, were considered. Findings had to be thematically grouped to handle issues of user accessibility, decision consequences and technical issues. NLP obviously increases BI accessibility with conversational interfaces that empower non-technical users, up to 30% more adoption rates in self-service systems. It enhances decision making using advanced analytics; sentiment analysis (85% accuracy) and predictive modeling (>95% accuracy) enable real time insights. However, scalability limitation, computational requirement and ethical issues such as bias and privacy call for strong solutions for CBI’s effective deployment. NLP integration of BI systems creates transformative value in terms of organizational data application, but facing technical and ethical challenges, adoption is not an easy task. In future research, building of scalable architectures, domain-specific NLP applications and use of ethical frameworks should be considered for CBI systems to be accessible, efficient and trustworthy. These have an implication that calls for interdisciplinary activities in ensuring that technological innovation is matched with practical utility.

Data Mining (DM) is being applied with success in Business Intelligence (BI) environments, and several examples of applications can be found. BI and DM have different roots and, as a consequence, have significantly different characteristics. DM came up from scientific environments; thus, it is not business oriented. DM tools still demand heavy work in order to obtain the intended results. On the contrary, BI is rooted in industry and business. As a result, BI tools are user-friendly. This chapter reflects on this difference from a historical perspective. Starting with a separated historical perspective of each one, BI and DM, the author then discusses how they converged into the current situation, when DM is used, and integrated, in BI environments with success.

Information is one of the most important assets for organizations. In the current scenario of a stagnant economy and fierce competition, it is imperative for organizations to be proactive, and for this to be possible, it is necessary to have timely access to the necessary information, so that it supports the decision-making process, thus allowing anticipate business strategies to react in a timely manner. It is in this context that Business Intelligence (BI) and its tools arise. Through BI it is possible to have access to an integrated set of solutions that allow data analysis in order to capture the information and knowledge necessary to leverage the business. BI allows organizations to use tools to analyze, plan, predict, solve problems, understand, innovate and learn in order to increase organizational knowledge, allowing effective actions that help to establish and achieve concrete business objectives in order to increase its efficiency and competitiveness. There is a wide variety of BI tools available on the market, which facilitate the use of the right data and visualization in ways that allow you to understand what it means. It is pertinent that organizations know how to select the appropriate tools for their needs. In this sense, a comparative analysis will be carried out between the different BI tools. The characteristics, specifications, and attributes of each one of them will be detailed to know key aspects of the tools that help the different organizations, regardless of the service they offer, to achieve their processes in an effective and efficient way. This comparison will allow companies to have a new perspective when using BI tools and generating business strategies. KeywordsBusiness intelligenceBI toolsAnalytics toolsCompetitive advantage

Knowledge management (KM) and business intelligence (BI) allow businesses to acquire, store, and access important information to create competitive advantages. While large businesses have used BI and KM integrations, small- and medium-sized enterprises (SMEs) have lagged behind in these areas. This qualitative exploratory case study used interviews from 11 knowledge management (KM) experts to gather their perceptions of integrating business intelligence (BI) into KM programs. The goal was to identify factors that could improve the performance of structured KM programs within SMEs. A scholarly literature review identified a gap in practice and knowledge; few studies have covered SMEs’ adoption of KM and BI tools. The study’s framework included a technology acceptance model and absorptive capacity (ACAP) theory. Research findings showed that integrating KM and BI tools can lead to KM and BI tool adoption, knowledge discovery, performance improvement, tacit knowledge assets, knowledge sharing, KMS/KB, and ACAP.