Business Data Analytics Research Articles

Unlocking Business Intelligence and Data Analytics Adoption Patterns: Insights from Jordanian Higher Education Institutions

Unlocking Business Intelligence and Data Analytics Adoption Patterns: Insights from Jordanian Higher Education Institutions

Transparency as a Determinant of Financial Performance in State-Owned Commercial Enterprises in Kenya

State-owned commercial enterprises (SOCEs) in Kenya register below-par output in fiscal standards. Many explanations have been proffered for this state of affairs, including competition and fluctuations in the local and global economies. Additionally, it is important to examine how factors internal to these enterprises shape their financial performance. As such, this study aimed to examine the role of transparency as a corporate governance strategy in the fiscal output of state-owned commercial enterprises as anchored by legitimacy theory. Further, the study adopted explanatory research design. It targeted 476 board members and 379 corporate executives from 46 SOCEs in Kenya. The sample size was 295, consisting of 142 corporate executives and 153 board members, who were selected via stratified random sampling. To collect primary data, a questionnaire and document analysis were deployed. Data was analysed using descriptive statistics. Test for relationship between variables was done using covariance-based structural equation modelling (CB-SEM) along with Analysis of Moment Structures (AMOS) software. The study results revealed that transparency (estimate = 1.946, critical ratio = 3.676, p < 0.001) positively and significantly influenced the financial performance of SOCEs. It was thus deduced that transparency was a determinant of SOCEs’ fiscal output in Kenya. Therefore, SOCEs should deploy centralized real-time monitoring of financial performance through dashboards, build data management capacities and adopt business intelligence and data analytics technologies that allow for data driven and intelligence-based decision-making by the SOCEs’ board and corporate executives.

HARNESSING BUSINESS DATA ANALYTICS FOR COMPETITIVE ADVANTAGE IN THE DIGITAL ERA

In the fast-paced, data-driven landscape of modern business, the ability to harness the power of data analytics has become paramount for organizations seeking to gain a competitive edge, drive innovation, and navigate complex market dynamics. Business Data Analytics, the practice of collecting, analysing, and interpreting data to derive actionable insights, lies at the heart of this digital transformation journey. This presentation offers a comprehensive exploration of Business Data Analytics, providing insights into its foundational principles, cutting-edge methodologies, and real-world applications across diverse industries. This presentation serves as a roadmap for organizations seeking to unlock the transformative power of Business Data Analytics. By embracing a data-driven mindset, investing in cutting-edge analytics capabilities, and fostering a culture of continuous learning and experimentation, organizations can position themselves for success in an ever-evolving business environment. As we look to the future, the opportunities for Business Data Analytics are limitless, offering organizations the tools they need to thrive and innovate in the digital age.

Harnessing Business Data Analytics for Competitive Advantage in the Digital Era

In the fast-paced, data-driven landscape of modern business, the ability to harness the power of data analytics has become paramount for organizations seeking to gain a competitive edge, drive innovation, and navigate complex market dynamics. Business Data Analytics, the practice of collecting, analysing, and interpreting data to derive actionable insights, lies at the heart of this digital transformation journey. This presentation offers a comprehensive exploration of Business Data Analytics, providing insights into its foundational principles, cutting-edge methodologies, and real-world applications across diverse industries. This presentation serves as a roadmap for organizations seeking to unlock the transformative power of Business Data Analytics. By embracing a data-driven mindset, investing in cutting-edge analytics capabilities, and fostering a culture of continuous learning and experimentation, organizations can position themselves for success in an ever-evolving business environment. As we look to the future, the opportunities for Business Data Analytics are limitless, offering organizations the tools they need to thrive and innovate in the digital age.

Utilizing the Internet of Things (IoT), Artificial Intelligence, Machine Learning, and Vehicle Telematics for Sustainable Growth in Small and Medium Firms (SMEs)

New technologies like the Internet of Things (IoT), artificial intelligence, machine learning, and vehicle telematics have tremendous potential to improve SMEs business processes, increase efficiency, and reduce costs to obtain a competitive advantage. However, the application of these technologies is also associated with certain difficulties for SMEs to adopt and incorporate them in their business processes due to limited resources, knowledge and funds. The advancement in technologies such as IoT and the digitization and datafication of physical infrastructure and processes are causing massive shifts across fields. While an increasing number of devices are being connected to the internet and are capturing large volumes of information about operations, users and the physical environment, new opportunities are arising to leverage that big data for better analytics and automation. The purpose of this paper is to assess how SMEs can apply IoT, AI, machine learning and vehicle telematics for sustainable development by enhancing business processes, data analysis, predictive maintenance and efficient supply chain and transportation.

Utilizing the Internet of Things (IoT), Artificial Intelligence, Machine Learning, and Vehicle Telematics for Sustainable Growth in Small and Medium Firms (SMEs)

New technologies like the Internet of Things (IoT), artificial intelligence, machine learning, and vehicle telematics have tremendous potential to improve SMEs business processes, increase efficiency, and reduce costs to obtain a competitive advantage. However, the application of these technologies is also associated with certain difficulties for SMEs to adopt and incorporate them in their business processes due to limited resources, knowledge, and funds. The advancement in technologies such as IoT and the digitization and datafication of physical infrastructure and processes are causing massive shifts across fields. While an increasing number of devices are being connected to the internet and are capturing large volumes of information about operations, users, and the physical environment, new opportunities are arising to leverage that big data for better analytics and automation. The purpose of this paper is to assess how SMEs can apply IoT, AI, machine learning, and vehicle telematics for sustainable development by enhancing business processes, data analysis, predictive maintenance, and efficient supply chain and transportation.

Navigating business intelligence and data analytics: Trends, foundations, strategies, and future directions

In the current digital age, business intelligence (BI) and data analysis are rapidly evolving, but the driving force is in the progress of technology and exponential increase in data. In this paper, we consider a new trend, theoretical basis, implementation strategy, and the future direction of BI and data analysis. In this paper, we consider what innovative roles in artificial intelligence (AI), the Internet (IOT), and block chain technology reconstruct data analysis practices. In addition, this paper investigates ethical considerations and privacy issues related to data driven decisions, emphasizing the importance of responsible data management practices. In this paper, we introduce the strategy of implementing data analysis in business processes, overcoming challenges, and successful case studies. Finally, we emphasize the new trend and emphasize the essence of BI that will continue to evolve through advanced analysis.

Nexus of Big Data and Big Data Analytics for Managerial Business Decision-Making: A Systematic Review Analysis

The growing usage of big data and big data analytics in business has prompted academics and professionals to widen their examination of their implications in business decision-making procedures. Until now, academics and business leaders have concentrated solely on the technical components of big data and analytics, ignoring the impact they have on the effectiveness of commercial decision-making systems. To begin, this paper intends to review the literature on the study of the relationship between the use of big data and big data analytics for its effectiveness in business industrial decision-making systems. Second, it gives important facts to assess whether big data and big data analytics catalyze the deployment of sophisticated business intelligence and informed decision-making representations. In this regard, the paper identifies the essential concerns that underpin the business-driven decision-making processes such as efficiency, and preciseness among others. Fundamentally, the current work contributes to the literature on big data and big data analytics for business-driven decision-making in both a theoretical fashion and provides a shot for future agenda possibilities to develop knowledge in this area.

Application and Exploration of Business Data Analysis Competency in the Course "Fundamentals of Statistics and Business Data Analysis"

Application and Exploration of Business Data Analysis Competency in the Course "Fundamentals of Statistics and Business Data Analysis"

Big data research in the business, management and accounting field: Revealing the thematic structure based on co-word analysis

Objective. This study aims to describe the thematic structure of big data research in business, management, and accounting. Design/Methodology/Approach. A co-word analysis was performed on 12,345 documents retrieved from Scopus from 2014-2023 in the category “business, management, and accounting”. Modularity algorithms were used to identify themes and subthemes, and the clustering of terms was qualitatively analyzed. Results/Discussion. Five main themes were identified: (1) Business and economic data analysis, (2) big data analytics in marketing, consumer behavior, and employee dynamics, (3) scalable machine learning and big data optimization, smart cities and urban development, (4) IoT-Driven innovations in industry 4.0 for optimized supply chain management, and (5) Social media and sentiment analysis in modern tourism and hospitality. The grouping of terms demonstrated the broad application of big data in healthcare, education, tourism, industry, organizational development, finance, social media, marketing, and hospitality. Conclusion. Big data is a field of application. This is evident in each cluster, where there are sub-themes that are nothing more than applying big data principles in sectors such as manufacturing, tourism services, education, health, and urbanization. Generally, the findings here are similar to other studies that have analyzed broader or more selective literature.

Application and Effectiveness Evaluation of Business Data Analytics in Marketing Strategy

The application of data analytics is becoming increasingly important in the field of marketing. This chapter explores in detail the application of data analytics in marketing strategy, including market segmentation, consumer behavior analysis, competitive analysis, and market trend forecasting. Through descriptive, predictive, and prescriptive analyses, companies are able to gain a deeper understanding of the current state of the market, predict future trends, and formulate a course of action accordingly. The chapter also discusses various data analytics tools and software, as well as the application of big data technologies in marketing, emphasizing the importance of data privacy and security. Finally, the chapter emphasizes the importance of evaluating the effectiveness of marketing campaigns, including ROI and long-term effectiveness evaluation, as well as how to combine quantitative and qualitative methods for a comprehensive evaluation. Through these methods, organizations can allocate resources more efficiently and improve the efficiency and effectiveness of marketing campaigns.

A STUDY ON IMPROVING SUPPLY CHAIN USING BUSINESS INTELLIGENCE CONCERNING DHARA LOGISTICS

Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs. BI tools enable business users to access different types of data — historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Users can analyze this information to gain insights into how the business is performing. According to CIO magazine: “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.” Organizations can use the insights gained from business intelligence and data analysis to improve business decisions, identify problems or issues, spot market trends, and find new revenue or business opportunities. BI platforms traditionally rely on data warehouses for their baseline information. A data warehouse aggregates data from multiple data sources into one central system to support business analytics and reporting. Business intelligence software queries the warehouse and presents the results to the user in the form of reports, charts and maps. Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. “OLAP provides powerful technology for data discovery, facilitating business intelligence, complex analytic calculations and predictive analytics,” says IBM offering manager Doug Dailey in his data warehousing blog. “One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data to improve product quality, customer interactions and process improvements.” Some newer business intelligence solutions can extract and ingest raw data directly using technology such as Hadoop, but data warehouses are still the data source of choice in many cases.

Cross-industry frameworks for business process reengineering: Conceptual models and practical executions

Cross-industry frameworks for business process reengineering (BPR) have emerged as pivotal tools in enhancing organizational efficiency, agility, and competitiveness across diverse sectors. These frameworks amalgamate conceptual models with practical executions to streamline operations, optimize resource allocation, and foster innovation. This review delves into the essence of cross-industry frameworks for BPR, elucidating their conceptual underpinnings and real-world applications. At the core of cross-industry frameworks lies the recognition that business processes transcend sector boundaries, encompassing common elements such as customer interactions, supply chain management, and operational workflows. Conceptual models form the foundational framework by articulating key principles, methodologies, and best practices for BPR initiatives. One prominent conceptual model is the Business Process Reengineering (BPR) methodology, which advocates for radical redesign rather than incremental improvement of processes. This approach emphasizes fundamental questioning of existing practices, aiming to achieve dramatic enhancements in efficiency, quality, and customer satisfaction. Another influential model is the Capability Maturity Model Integration (CMMI), which provides a roadmap for organizations to systematically improve their processes by advancing through defined maturity levels. Practical executions of cross-industry frameworks entail the translation of conceptual models into actionable strategies tailored to specific organizational contexts. This involves a multifaceted approach encompassing process analysis, stakeholder engagement, technology integration, and change management. Process analysis entails mapping current workflows, identifying bottlenecks, and pinpointing areas for optimization. Stakeholder engagement is crucial for garnering buy-in and fostering a culture of continuous improvement. Technology integration plays a pivotal role in modern BPR initiatives, leveraging digital tools such as business process management (BPM) software, data analytics, and robotic process automation (RPA) to streamline operations and enhance decision-making. Moreover, emerging technologies like artificial intelligence (AI) and blockchain offer new avenues for process innovation and optimization. Change management constitutes a critical component of practical executions, as BPR initiatives often entail significant organizational transformation. Effective change management involves communication, training, and leadership support to mitigate resistance and facilitate smooth transition. Real-world applications exemplify the effectiveness of cross-industry frameworks in driving tangible business outcomes. Case studies spanning diverse sectors, including manufacturing, healthcare, finance, and retail, demonstrate how organizations have leveraged BPR to achieve breakthrough improvements in efficiency, cost reduction, and customer satisfaction. In the manufacturing sector, for instance, companies have adopted lean manufacturing principles to streamline production processes, minimize waste, and enhance flexibility. In healthcare, BPR initiatives have led to streamlined patient care pathways, reduced administrative burdens, and improved clinical outcomes. Financial institutions have leveraged BPR to optimize loan approval processes, mitigate risk, and enhance regulatory compliance. Similarly, retailers have utilized BPR to optimize supply chain management, enhance omnichannel capabilities, and personalize customer experiences. Cross-industry frameworks for business process reengineering represent a potent paradigm for organizational transformation in today's dynamic business landscape. By integrating conceptual models with practical executions, these frameworks empower organizations to enhance operational efficiency, drive innovation, and maintain competitive advantage across diverse sectors.

THE STRATEGIC VALUE OF BUSINESS ANALYSTS IN ENHANCING ORGANIZATIONAL EFFICIENCY AND OPERATIONS

Business analysts play a crucial role in enhancing organizational efficiency and operations by bridging the gap between business needs and technological solutions. This review explores the strategic value of business analysts in driving organizational success through effective analysis, communication, and stakeholder engagement. Business analysts act as key facilitators in organizations, translating business objectives into actionable requirements for technology solutions. By conducting thorough analysis of business processes, systems, and data, business analysts identify opportunities for improvement and recommend strategic solutions to enhance operational efficiency. Their role extends beyond mere problem-solving; they also play a pivotal role in aligning business goals with technology initiatives, ensuring that projects deliver tangible value to the organization. Furthermore, business analysts excel in communication and stakeholder engagement, acting as liaisons between business units, IT teams, and external vendors. Their ability to translate complex technical concepts into clear, understandable language enables them to facilitate collaboration and consensus among diverse stakeholders. This skill is particularly valuable in Agile environments, where collaboration and communication are key principles. Moreover, business analysts are instrumental in driving organizational change and transformation. By conducting impact assessments, developing change management strategies, and providing training and support, business analysts ensure that technology implementations are successful and sustainable. Their holistic approach to problem-solving and their focus on delivering value make them invaluable assets to organizations seeking to improve efficiency and operations. In conclusion, the strategic value of business analysts in enhancing organizational efficiency and operations cannot be overstated. Their unique blend of analytical skills, communication prowess, and stakeholder engagement abilities make them indispensable in driving organizational success. By leveraging the expertise of business analysts, organizations can streamline processes, improve decision-making, and achieve their strategic objectives with greater efficiency and effectiveness. Keywords: Strategic Value, Business Analysts, Enhancing, Organizational Efficiency, Operations.

ПРАКТИЧНІ РЕКОМЕНДАЦІЇ ЩОДО ВИКОРИСТАННЯ GENERATIVE AI В БІЗНЕСІ

Generative Artificial Intelligence (GenAI) is transforming the economic landscape by introducing innovative solutions that significantly enhance business efficiency, drive innovation, and create competitive advantages. This article delves into the economic implications and practical applications of GenAI across various business domains, including marketing, customer support, product design, and data analysis. By leveraging advanced AI models, companies can automate routine tasks, generate personalized content, and optimize operations, leading to substantial economic benefits. The implementation of GenAI necessitates a systematic approach, starting with business concept validation and progressing through stages of technical solution identification, proof of technology, and project planning. These stages ensure that AI solutions are economically viable, effective, and aligned with business objectives. Businesses can adopt different strategies to integrate GenAI, ranging from the rapid deployment of third-party applications to the development of customized in-house models. Each approach offers unique economic benefits, balancing customization and control with implementation time and value. This article provides practical recommendations for strategy of implementing Generative AI, emphasizing the importance of careful economic analysis, stakeholder engagement, and continuous improvement. By adopting a structured approach and selecting the appropriate integration strategy, businesses can harness the transformative power of GenAI. This enables them to innovate and thrive in an increasingly competitive economic environment, positioning themselves as leaders in the digital age.

Enterprise Digital Retail Business Data Analysis and Forecasting Based on Time Series Analysis

Enterprise Digital Retail Business Data Analysis and Forecasting Based on Time Series Analysis

Self-empowerment of Small and Medium-sized Enterprises

In the process of development, small and medium-sized enterprises will face a lot of pressure brought by changes in the market environment and industry policy adjustments, and some even have to make major adjustments and transformations. How to stabilize the situation in the face of pressure and change and ensure the normal operation of the company is an objective and realistic problem that small and medium-sized enterprises will encounter. ??Through the in-depth analysis of the business situation of enterprises, it can help enterprises find problems, optimize processes, and formulate targeted strategies and measures. The analysis of business data is the most important and direct way to find out their own advantages and disadvantages. Through the analysis of these data, enterprises can understand their own financial status, market sales and resource utilization, etc., on this basis, enterprises can find out the existing problems and bottlenecks. ??Through innovation, talent cultivation, cooperation, market development and technology application, smes can enhance their capabilities and competitiveness, adapt to market changes and achieve long-term success. Therefore, small and medium-sized enterprises should actively take actions to continuously pursue opportunities and methods of self-empowerment, and achieve continuous self-empowerment by establishing mechanisms and teams for business analysis.

Artificial Intelligence-Enhanced Learning: A New Paradigm in the “Business Data Analysis and Application” Course

This paper explores the transformative impact of generative artificial intelligence (AI) on the “Business DataAnalysis and Application” course in the post-2023 era, marking a significant paradigm shift in educational methodologies. It investigates how generative AI reshapes teaching and learning dynamics, enhancing the processing of complex data sets and nurturing critical thinking skills. The study highlights the role of AI in fostering dynamic, personalized, and adaptive learning experiences, addressing the evolving pedagogical needs of the business sector. Key challenges, including equitable access, academic integrity, and ethical considerations such as data privacy and algorithmic bias, are thoroughly examined. The research reveals that the integration of generative AI aligns with current professional demands, equipping students with cutting-edge AI tools, and tailoring learning to individual needs through real-time feedback mechanisms. The study concludes that the incorporation of generative AI into this course signifies a substantial evolution in educationalapproaches, offering profound implications for student learning and professional development.

An Inductive Heterogeneous Graph Recommendation Model for High-Scoring Items Applied to Business Intelligence

Business intelligence (BI), as a system for business data integration, processing, and analysis, is receiving increasing attention from enterprises. Data visualization is an important feature of BI, which allows users to visually observe the distribution and direction of data and assists them in making correct decisions. The core of this feature is visual analysis charts, which need to be pre-created and integrated into the dashboard by the chart makers, so there are situations where user needs cannot be accurately grasped. At the same time, there may be omissions in the work of users, and a method is needed to remind them. Introducing recommendation models into data visualization is a good solution; therefore, this paper proposes a recommendation model suitable for this type of scenario, which recommends high-scoring items (charts) to users. This model consists of a inductive heterogeneous graph recommendation algorithm with user preferences and a slow-acting collaborative filtering method. The experimental results in two datasets showed an improvement of 0.020/0.045, 0.083/0.019 and 0.076/0.023 in Hit, F-score, and NDCG compared to baselines, which proves that it is more suitable for data visualization requirements and other similar scenarios that require inductive recommendations based on user preferences.

Entrepreneurs of memory: Selling history in the GDR Museum shop in Berlin

The field of memory studies rarely deals with commercial enterprises, which, as we argue in this article, play just as prominent a role in shaping collective memories as state actors or nongovernmental organizations. The aim of our study is therefore to discuss the role of private entrepreneurs and their businesses in the context of GDR memory. We focus on the privately run GDR Museum in Berlin and its best-selling products. Through the lens of the museum store, we analyze the exhibition, an iconic eggcup, and a book on the history of the GDR that has enjoyed sustained sales over a lengthy period. By tracing the intertwined distribution chains of these memory goods, we emphasize the importance of private entrepreneurs and their networks in the current German memory culture. We argue that economic interests and developments are just as important as political decisions and public institutions regarding the memory of the communist past. Thus memory studies should also focus on enterprises of memory by analyzing business data. This poses an empirical challenge that is worth tackling, since it broadens our understanding of current memory culture.