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Journal articles on the topic 'Business Analytics'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 June 2025

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1

Julmi, Christian. "Business Analytics." WiSt - Wirtschaftswissenschaftliches Studium 49, no. 9 (2020): 53–55. http://dx.doi.org/10.15358/0340-1650-2020-9-53.

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Die Digitalisierung führt zu einer grundlegenden Veränderung von Entscheidungsprozessen in Unternehmen. Unter dem Stichwort Business Analytics wird das Potenzial datengestützter Entscheidung im Management diskutiert. Der Beitrag grenzt Business Analytics von Big Data ab, legt Möglichkeiten der Datenanalyse und -aufbereitung dar und zeigt Grenzen in der Anwendung auf.

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Horváth, Péter. "Business Analytics." Controlling 28, no. 8-9 (2016): 455–57. http://dx.doi.org/10.15358/0935-0381-2016-8-9-455.

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ZENKINA, Irina V. "Business analysis and business analytics: Development in the context of digitalization." Economic Analysis: Theory and Practice 22, no. 4 (2023): 646–71. http://dx.doi.org/10.24891/ea.22.4.646.

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Subject. The article addresses the analytical support for change management in conditions of orientation of economic entities to sustainable development and digital transformation of business. Objectives. The purpose is to identify modern features of business analysis, the content of the main types of business analytics, and the impact of digitalization processes on analytical activities development. Methods. The study draws on research methods, like analysis, synthesis, comparison, generalization, abstraction, systemic, strategic, and risk-oriented approaches. Results. The paper reveals the content, areas and tasks of business analysis in accordance with the standard of business analysis adopted by the International Institute of Business Analysis in November 2022. It assesses innovations in the field of regulation of business analysis at the global level; defines the specifics of business analysis and underpins prerequisites for its improvement under new challenges and large-scale digitalization; considers the interrelation of business analysis and business analytics, presents the systematization of the main types of analytics. Conclusions. The analysis of trends in the development of analytical activities of business entities enabled to reflect priority areas of business analytics, and perform their comparative assessment. The paper unveiled the emerging trend of divergence of business analysis and business analytics as separate types of analytical activity, and identified Business Intelligence, Big Data Analytics, and Data Science as the most promising areas of business analytics.

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Potančok, Martin, Jan Pour, and Wui Ip. "Factors Influencing Business Analytics Solutions and Views on Business Problems." Data 6, no. 8 (2021): 82. http://dx.doi.org/10.3390/data6080082.

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The main aim of this paper is to identify and specify factors that influence business analytics. A factor in this context refers to any significant characteristic that defines the environment in which business analytics and business in general are conducted. Factors and their understanding are essential for the quality of final business analytics solutions, given their complexity and interconnectedness. Factors play an extremely important role in analytic thinking and business analysts’ skills and knowledge. These factors determine effective approaches and procedures for business analytics, and, in some cases, they also aid in the decision to delay a business analytics solution given a situation. This paper has used the case study method, a qualitative research method, due to the need to carry out investigation within the actual business (company) environment, in order to be able to fully understand and verify factors affecting analytics from the viewpoint of all stakeholders. This study provides a set of 15 factors from business, company, and market environments, including their importance in business analytics.

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Ghorapade, Mr Kuldeep D. "Business Analytics and It’s Impact on Business and Industry." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-ICDEBI2018 (2018): 74–79. http://dx.doi.org/10.31142/ijtsrd18676.

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Marius Okafor, Chiedozie, Mercy Odochi Agho, Awele Vivian Ekwezia, Nsisong Louis Eyo-Udo, and Chibuike Daraojimba. "UTILIZING BUSINESS ANALYTICS FOR CYBERSECURITY: A PROPOSAL FOR PROTECTING BUSINESS SYSTEMS AGAINST CYBER ATTACKS." Acta Electronica Malaysia 7, no. 2 (2023): 34–48. http://dx.doi.org/10.26480/aem.02.2023.38.48.

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In the age of digital transformation, businesses face an escalating challenge in managing cyber threats. The paper “Utilizing Business Analytics for Cybersecurity: A Proposal for Protecting Business Systems Against Cyber Attacks” delves into an innovative approach where the power of business analytics is harnessed to bolster cybersecurity defenses. An exhaustive exploration elucidates how data, a seemingly intangible asset, can be transformed into actionable insights that preemptively detect, mitigate, and counteract cyber threats. The discourse emphasizes the convergence of two distinct domains: business analytics and cybersecurity. This union is demonstrated to be synergistic, enhancing the capabilities of traditional cybersecurity methods. Predictive analytics forecast potential threats, behavioral analytics discern anomalies in user activities, and network analytics spotlight vulnerabilities in real-time. Moreover, the iterative nature of these analytical processes ensures a proactive and evolving defense mechanism. The paper underscores the myriad benefits of this integration, including efficient resource allocation, enhanced incident response, and the cultivation of an organizational culture centered on continuous learning. While the advantages are manifold, challenges are inherent. Issues related to privacy, data quality, and the necessity for regular model updates are discussed in depth. Furthermore, a detailed framework is proposed, guiding businesses in seamlessly incorporating business analytics into their cybersecurity strategies. From data collection and validation to model deployment and continuous monitoring, each stage is meticulously crafted to ensure maximum efficacy. In summation, the paper serves as both an enlightening exploration and a clarion call for businesses. In an era where threats evolve rapidly, the amalgamation of business analytics with cybersecurity presents a formidable solution, ensuring robust and resilient defenses.

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Ereth, Julian, and Hans-Georg Kemper. "Business Analytics und Business Intelligence." Controlling 28, no. 8-9 (2016): 458–64. http://dx.doi.org/10.15358/0935-0381-2016-8-9-458.

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Watson, Hugh J. "All About Analytics." International Journal of Business Intelligence Research 4, no. 1 (2013): 13–28. http://dx.doi.org/10.4018/jbir.2013010102.

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To understand and be successful with analytics, it is important to be precise in understanding what analytics means, the different targets or approaches that companies can take to using analytics, and the drivers that lead to the use of analytics. For companies that use advanced analytics, the keys to success include a clear business need; strong, committed sponsorship; a fact-based decision making culture; a strong data infrastructure; the right analytic tools; and strong analytical personnel in an appropriate organizational structure. These are the same factors for success for business intelligence in general, but there are important nuances when implementing advanced analytics, such as with the data infrastructure, analytical tools, and personnel. Companies like Amazon.com, Overstock.com, Harrah’s Entertainment, and First American Corporation are exemplars that illustrate concepts and best practices.

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Duan, Lian, and Ye Xiong. "Big data analytics and business analytics." Journal of Management Analytics 2, no. 1 (2015): 1–21. http://dx.doi.org/10.1080/23270012.2015.1020891.

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Chidera Victoria Ibeh, Onyeka Franca Asuzu, Temidayo Olorunsogo, Oluwafunmi Adijat Elufioye, Ndubuisi Leonard Nduubuisi, and Andrew Ifesinachi Daraojimba. "Business analytics and decision science: A review of techniques in strategic business decision making." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 1761–69. http://dx.doi.org/10.30574/wjarr.2024.21.2.0247.

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Business analytics and decision science have emerged as pivotal domains in enhancing strategic business decision-making processes. This review delves into various techniques that organizations employ to optimize their operations and achieve competitive advantages. At the forefront of strategic decision-making is data analytics, where vast amounts of data are analyzed to extract valuable insights. Descriptive analytics provides a historical perspective by examining past data trends, enabling businesses to understand their performance over time. This retrospective analysis serves as a foundation for predictive analytics, which utilizes statistical models and machine learning algorithms to forecast future trends and outcomes. By leveraging predictive analytics, organizations can anticipate market shifts, customer preferences, and potential risks, thereby making informed decisions. Prescriptive analytics uses predictive models to guide strategic decision-making, utilizing optimization algorithms and simulation tools to identify optimal actions. Decision science integrates analytical techniques with human judgment, focusing on consumer behavior and psychological factors to tailor marketing strategies and product offerings. Additionally, artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing strategic decision-making by automating complex tasks and providing real-time insights. Natural language processing (NLP) algorithms analyze unstructured data sources, such as customer reviews and social media posts, to extract valuable information and sentiment analysis. This enables businesses to gauge customer satisfaction levels and identify areas for improvement promptly. Decision trees, regression analysis, and clustering techniques are widely used in business analytics to segment customers, identify patterns, forecast sales trends, evaluate alternatives, assess risks, and optimize resource allocation. In conclusion, business analytics and decision science offer a plethora of techniques that empower organizations to make informed, data-driven decisions. By leveraging descriptive, predictive, and prescriptive analytics, along with AI and ML technologies, businesses can navigate complex environments, capitalize on opportunities, and mitigate risks effectively. This review underscores the importance of integrating analytical techniques with human expertise to achieve strategic objectives and sustainable growth.

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Knoll, Matthias. "Rezension „Business Analytics“." HMD Praxis der Wirtschaftsinformatik 56, no. 5 (2019): 1082–84. http://dx.doi.org/10.1365/s40702-019-00499-5.

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Eicher, Jill, and David Ruder. "Business Process Analytics." Journal of Alternative Investments 10, no. 2 (2007): 76–84. http://dx.doi.org/10.3905/jai.2007.695270.

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Zhao, Yifan. "Transformation of Business Analytics from Business Intelligence." E3S Web of Conferences 253 (2021): 03013. http://dx.doi.org/10.1051/e3sconf/202125303013.

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With the development of economy, numerous techniques, tools, and concepts have appeared to fit the intensive and competitive market. Especially for some enterprises in retail industry, they face the problem of digital transformation, which requires advanced organizational strategies, business analytics technology, dynamic capabilities, value-creating actions to help solve the problem. This paper analyzes the real cases of digital transformation in some Chinese enterprises to show how business intelligence gradually developed into business analysis and how it creates value to the business. Real experience of the author and the research resources of her internship in a consulting company are also shared. Enterprises often use SAP, ERP, IaaS, SaaS, PaaS to build the cloud services and infrastructure of data ware, which are the products of business analytics. The author analyzes how business analytics help enterprises use effective and intelligent analysis on the data and business to improve the performance of the enterprises, which can make the enterprises become competitive and outstanding. In addition, the difference between business intelligence and business analytics, and how the value business analytics creates to the enterprises in theoretical and practical way are introduced. Finally, the author finds the significant of data and analytical tools to the present and future development in different industries, and predicts the general trend that might happen in the future. People can also realize the impact that data brings to their daily life.

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ASHRAF, FAIZAN. "Social Media Analytics and Business Performance." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32945.

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This paper examines the crucial role of social media analytics in enhancing business performance. With the exponential growth of social media platforms, businesses are increasingly leveraging these platforms to connect with customers, drive engagement, and boost sales. Social media analytics enables businesses to extract valuable insights from the vast amount of data generated on these platforms, allowing for informed decision-making and targeted marketing strategies. Through a comprehensive review of existing literature and case studies, this paper highlights the various ways in which social media analytics contributes to improving business performance, including customer sentiment analysis, competitor benchmarking, trend identification, and campaign optimization. Additionally, it explores the challenges and opportunities associated with implementing social media analytics initiatives and provides recommendations for businesses seeking to maximize the impact of their social media efforts on overall performance. Overall, this paper underscores the transformative potential of social media analytics in driving business success in the digital age.

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Gooden, Joachim P., Rakeem R. Ford, and Jason T. Black. "Business analytics at Florida commuter colleges: The impact and effectiveness of implementing a business analytics program." Journal of Management and Engineering Integration 15, no. 2 (2022): 34–47. http://dx.doi.org/10.62704/10057/24823.

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Only forty of Florida's one-hundred seventy-eight colleges and universities are public, meaning the vast majority are privately run. Many of these schools provide associate degrees or certificates (Community College Review, 2021). Commuter colleges hold the distinction of providing off-campus student living, with the majority offering two-year degrees. Despite the growing need in industry for individuals who can manage large amounts of data, commuter colleges rarely offer business analytics courses, causing many students to miss out on such employment opportunities. Most businesses struggle with analytical planning and are looking for experts who can turn data into insight (Albright & Winston, 2016). The absence of data analytics capabilities causes organizations to waste data resources at a rate of approximately sixty to seventy percent (Forrester, 2017). Intrinsically, universities of all sizes, including commuter colleges, need to train and produce more data analysts. Many major universities now offer business analytics degrees, yet business analytics degrees are available at less than twenty percent of commuter colleges. The goal of this research is to investigate whether commuter colleges would indeed benefit from business analytics programs and to determine the appropriate analytics degree curricula most optimal for these types of institutions. The paper will present an exhaustive comparison of the state of Florida's colleges and universities, including commuter colleges, examining both business intelligence and business analytics degree programs. The objective is to analyze the impact and effectiveness of these programs at larger universities and present a model for developing such programs at commuter colleges.

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Gupta, Manish, Weiguo Fan, and Aviral Kumar Tiwari. "Analytics for business decisions." Management Decision 60, no. 2 (2022): 297–99. http://dx.doi.org/10.1108/md-02-2022-174.

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Lim, Ee-Peng, Hsinchun Chen, and Guoqing Chen. "Business Intelligence and Analytics." ACM Transactions on Management Information Systems 3, no. 4 (2013): 1–10. http://dx.doi.org/10.1145/2407740.2407741.

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Apte, C., B. Dietrich, and M. Fleming. "Business leadership through analytics." IBM Journal of Research and Development 56, no. 6 (2012): 7:1–7:5. http://dx.doi.org/10.1147/jrd.2012.2214555.

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Nalchigar, Soroosh, and Eric Yu. "Designing Business Analytics Solutions." Business & Information Systems Engineering 62, no. 1 (2018): 61–75. http://dx.doi.org/10.1007/s12599-018-0555-z.

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Hohenberger, Rebecca, and Sascha Kemmeter. "Business Analytics im Controlling." Controlling 34, no. 3 (2022): 81–83. http://dx.doi.org/10.15358/0935-0381-2022-3-81.

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Damit mittelständische Unternehmen sich Wettbewerbsvorteile sichern und zukunftsfähige Geschäftsmodelle betreiben können, ist es erforderlich, deren Controlling zu digitalisieren. Der vorliegende Beitrag beschäftigt sich damit, welche Potenziale Business Analytics für das Controlling aufweist und welche Hürden für eine nachhaltige Implementierung im Mittelstand genommen werden müssen.

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Messer, Uwe, and Nadine Lukas. "Controlling und Business Analytics." Controlling 35, no. 2 (2023): 43–49. http://dx.doi.org/10.15358/0935-0381-2023-2-43.

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Durch das Aufkommen immer größerer Datenmengen gewinnt Business Analytics an Bedeutung für das Controlling. Im folgenden Beitrag beleuchten wir auf Basis von qualitativen Interviews mit Fach- und Führungskräften das Verhältnis dieser beiden Disziplinen. Dabei ergeben sich drei Integrationsgrade mit jeweils eigenen Implikationen für die Zukunft des Controllings.

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MANDYCH, Oleksandra, Tetiana KVIATKO, and Olena NAHOLIUK. "BUSINESS ANALYTICAL DEVELOPMENT TRENDS." Ukrainian Journal of Applied Economics 5, no. 1 (2020): 304–11. http://dx.doi.org/10.36887/2415-8453-2020-1-36.

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The article examines the impact of business intelligence on strategic decision-making by company executives. It was determined that the acceleration of information flows caused the need to introduce a change management system and the development of a new line of business intelligence. The results of the study are presented, which show that most of the business analytics are conducted in order to determine and check the effectiveness of decisions concerning the needs, goals and objectives of the business activities, already carried out by the company and the one planned. The main stages of the formation and development of HIV systems in the world are also considered, the main factors influencing the pace of development of business analytics processes are highlighted. It is emphasized that as a result of business analytics in the practical activity of the subject of economic relations the following are combined: marketing research; technical capabilities; investments and financial risks; competitive position of the company and its competitors; new information and marketing solutions; socio-economic and political environment. The main tasks of business analytics include: providing truthful and necessary information for making management decisions; determining the need and timeliness of business processes; assessment of current and strategic requirements of the company regarding the effectiveness of business analysis; formation of the general strategy of the organization; determining ways to achieve the set goals; coordinating the activities of all units; assessment and control of existing and potential risks; an assessment of the need to adjust company actions in order to be able to quickly adapt to changes in the target market. The main problematic aspects that had emerged in this field have been identified, as well as the role and place of the analyst in the company's activity have been determined. The current state of development of the domestic market of HIV-systems is considered. It is proved that under the current conditions of economic development, companies cannot make effective decisions without the implementation of business analytical processes in practice, and the success of companies in competition and retaining leading positions in the target markets in the future depends on the ability to effectively apply business analytics. Keywords: analytic business-geometry, strategic decisions, company, information technologies.

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Dallo, Khan Ali Marwani. "Natural language processing for business analytics." Advances in Engineering Innovation 3, no. 1 (2023): 37–40. http://dx.doi.org/10.54254/2977-3903/3/2023038.

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Natural Language Processing (NLP), a branch of artificial intelligence, is gaining traction as a potent tool for business analytics. With the proliferation of unstructured textual data, businesses are actively seeking methodologies to distill valuable insights from vast textual repositories. The introduction of NLP in the realm of business analytics offers a transformative approach, automating traditional manual processes and fostering real-time, data-driven decision-making. From sentiment analysis to text summarization, NLP is facilitating businesses in deciphering consumer feedback, predicting market trends, and breaking down linguistic barriers in the age of globalization. This paper sheds light on the evolution of NLP techniques in business analytics, their applications, and the inherent challenges and opportunities they present.

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Vashishtha, Atul. "Business Analytics: A Simplified Review." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (2022): 1835–38. http://dx.doi.org/10.22214/ijraset.2022.46526.

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Abstract: Business analytics is primarily about getting the most out of data. Data has lately been dubbed "the new oil" rather than the "sludge of the information era." While data can be used to develop new products and services, identify market niches, and spot new opportunities, it is also notoriously amorphous and difficult to extract value from. It involves different steps to get the insights from the data present majorly involving approaches like Aligning strategy, desired behaviors, and business performance management with analytical activities and capabilities is necessary to derive value from data. This article uses both conventional and qualitative research methods to examine the expanding body of work on business analytics (BA).In this paper, an attempt is being made to review several viewpoints on how business analytics is defined and how it relates to business intelligence. Additionally, we highlight business education and demonstrate how business analytics are applied in both company and industrial sectors

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Sprongl, Peter. "Gaining competitive advantage through business analytics." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61, no. 7 (2013): 2779–85. http://dx.doi.org/10.11118/actaun201361072779.

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The industry for business analytics within the BI sphere is growing significantly and the distinction in organizations between transactional information systems and decision-oriented systems breaks down. Firms need to understand both the opportunity and the potential of business analytics. Reporting, which is getting a handle on what happened in organizations, is complemented by analytics that is rather explanatory and predictive. Leveraging business analytics means to use analytics applications in order to analyse business problems and produce related business recommendations to improve business process performance. Business analytics must but be a part of a value creating process operating together with other systems and organisational factors in a synergistic manner, including people, processes, knowledge and relationship assets, culture, structure, and policies. In order for companies to be efficient, they need to automate processes, workflows and make rules. Effectiveness, on the other hand, is about making better decisions, perhaps using the same data that their competitors may have. What matters is not necessarily the technologies deployed, but emerging competence that the firm uses to support its business. A specific “mindset” needs to be installed for companies to invest into business analytics. Organisations need to better understand how best to exploit their data and convert them into information and sense-making capabilities. Business capabilities can be enhanced not only by exploitation of analytical tools, but also by the sophisticated use of information. This leads to a truly sense-making capability or “analytical mindset”. The primary data covers 398 data sets, where firms have been asked about the specifics of their information management. The data is used as input to statistical tests and the value of business analytics is being analyzed in an empirical way.

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Yin, Jiarui, and Vicenc Fernandez. "A systematic review on business analytics." Journal of Industrial Engineering and Management 13, no. 2 (2020): 283. http://dx.doi.org/10.3926/jiem.3030.

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Purpose: Business analytics, a buzzword of the recent decade, has been applied by thousands of enterprises to help generate more values and enhance their business performance. However, many aspects of business analytics remain unclear. This study clarifies the definition of business analytics combined with its functionality and the relation between business analytics and business intelligence. Moreover, we illustrate the applications of business analytics in both business areas and industry sectors and shed light on the education in business analytics. Ultimately, to facilitate future research, we summarize several research techniques used in the literature reviewed.Design/methodology/approach: We set well-established selection criteria to select relevant literature from two widely recognized databases: Scopus and Web of Science. Afterward, we reviewed the literature and coded relevant sections in an inductive way using MAXQDA. Then we compared and synthesized the coded information.Findings: There are mainly four findings. Firstly, according to the bibliometric analysis, literature about business analytics is growing exponentially. Secondly, business analytics is a system that enabled by machine learning techniques aiming at promoting the efficiency and performance of an organization by supporting the decision-making process. Thirdly, the application of business analytics is comprehensive, not only in specific areas of a company but also in different industry sectors. Finally, business analytics is interdisciplinary, and the successful training should involve technical, analytical, and business skills.Originality/value: This systematic review, as a synthesis of the current research on business analytics, can serve as a quick guide for new researchers and practitioners in the field, while experienced scholars can also benefit from this work, taking it as a practical reference.

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Attaran, Mohsen, and Sharmin Attaran. "The Rise of Embedded Analytics." International Journal of Business Intelligence Research 9, no. 1 (2018): 16–37. http://dx.doi.org/10.4018/ijbir.2018010102.

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This article describes how in today's hyper-competitive environment, business leaders around the world are using analytics technologies to create business values, and to gain a better understanding of their customer's needs and wants. However, traditional business analytics are undergoing major changes. The Internet revolution, cloud computing, and the evolution to self-service analytics have all contributed to the changing dimensions of business intelligence. To compete effectively in a digitally driven world, business leaders must understand and address the critical shifts taking place in the field of analytics, and how these shifts impact their overall strategy. The key objective of this article is to propose a conceptual model for successful implementation of Embedded Analytics in organizations. This article also covers some of the potential benefits of analytics, explores the changing dimensions of analytics, and provides a guide to some of the opportunities that are available for using embedded analytics in business. Furthermore, this study reviews key attributes of a successful modern analytics platform and illustrates how to overcome some of the key challenges of incorporating embedded analytics into an analytic strategy in business. Finally, this article highlights successful implementation of analytics solutions in manufacturing and service industry.

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Singh, Ayush, Garima Jain, Neha Khandelwal, and Priya Singh. "Future of Artificial Intelligence & Business Analytics." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28831.

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Artificial intelligence (AI) and business analytics are two rapidly developing technologies transforming how businesses operate. AI is the branch of computer science that focuses on developing intelligent machines that can perform tasks that typically require human intelligence, while business analytics refers to the process of using data to make informed business decisions. The purpose of this paper is to explore the future of AI and business analytics and how these technologies will shape the business landscape in the coming years. The increasing availability and use of artificial intelligence (AI) technologies in business analytics have the potential to revolutionize the way organizations operate. This research paper explores the future of AI and business analytics, examining how advances in AI technology are shaping the field and what changes we can expect to see in the coming years. The paper examines the benefits and challenges of using AI in business analytics, as well as the ethical considerations that arise with the increased use of this technology. Additionally, the paper explores how organizations can best leverage AI in their business analytics strategies to maximize the potential benefits while minimizing the risks. Through a review of existing literature and analysis of emerging trends, this paper provides valuable insights for businesses and policymakers seeking to understand the evolving landscape of AI and business analytics. Looking ahead, the future of AI and business analytics is likely to be shaped by a number of key trends. These include the increasing use of machine learning and deep learning algorithms, the integration of AI with other emerging technologies such as blockchain and the Internet of Things, and the growing importance of explainable AI that can provide transparency and accountability in decision-making.

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Cho, Minseok, Michael Cottingham, Billy Hawkins, and Don Lee. "Sport Analytics Business: Exploring Fan Engagement on Analytical Content." IJASS(International Journal of Applied Sports Sciences) 35, no. 2 (2023): 201–14. http://dx.doi.org/10.24985/ijass.2023.35.2.201.

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With the emerging trend of technological advancement in sport media production, sport analytics has provided fan-oriented game contents through multimedia platforms. This study aims to empirically investigate sport fans’ perceptions toward sport analytics contents (i.e., statistics, video, data visualization, decision-aid) by exploring their impact on fan engagement (i.e., information, entertainment, personal identity, social interaction) and employing the uses and gratifications theory. Using multiple regressions, this study found that sport fans are more likely to consume statistics and video contents for information search and entertainment. Moreover, data visualization and decision-aid contents showed a positive impact on subjects’ personal identity and social interaction. The present study highlights key aspects of sport analytics in its application to sport media production as well as sport management at large.

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Chen, Xiaofeng, and Keng Siau. "Business Analytics/Business Intelligence and IT Infrastructure." Journal of Organizational and End User Computing 32, no. 4 (2020): 138–61. http://dx.doi.org/10.4018/joeuc.2020100107.

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This is an empirical research investigating the impact of business analytics (BA) and business intelligence (BI) use, IT infrastructure flexibility, and their interactions on organizational agility. Synthesizing the systems theory and awareness-motivation-capability framework, the authors propose that BA-Use, IT infrastructure flexibility, and their interactions significantly influence organizational agility. The results show the significant association of BA use and IT infrastructure flexibility with organizational agility. The results also suggest that BA use may demand corporations to build a more flexible IT infrastructure. However, the data does not reveal the proposed interaction between the two drivers of organizational agility.

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Choi, Byounggu, Kunsoo Han, and Zhuo (June) Cheng. "Knowledge Management, Business Intelligence, and Business Analytics." Asia Pacific Journal of Information Systems 25, no. 3 (2015): 540–47. http://dx.doi.org/10.14329/apjis.2015.25.3.540.

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Mohmmed Pasha Shaik, John. "A Study on Business Analytics with Human Resource." International Journal of Science and Research (IJSR) 12, no. 5 (2023): 806–8. http://dx.doi.org/10.21275/sr23510142017.

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KIM, JENNIFER, DAVID A. OSTROWSKI, HIROSHI YAMAGUCHI, and PHILLIP C. Y. SHEU. "SEMANTIC COMPUTING AND BUSINESS INTELLIGENCE." International Journal of Semantic Computing 07, no. 01 (2013): 87–117. http://dx.doi.org/10.1142/s1793351x13500013.

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With rapidly expanding data collections becoming increasingly available, the application of Semantic Computing has become imperative to leverage this resource for industrial applications. This paper presents a survey of Semantic Computing in the area of Business Intelligence. We examine semantic analytical techniques and tools as applied for prediction analysis and decision support. We also define the role of Semantic Computing as applied in the context of Data Mining, Text Mining and Big Data Analytics. Additionally, we describe how business data is queried with Structured Natural Language as well as the use of On-Line Analytic Processing.

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Kim, Sookhyun. "Mapping social media analytics for small business: A case study of business analytics." International Journal of Fashion Design, Technology and Education 14, no. 2 (2021): 218–31. http://dx.doi.org/10.1080/17543266.2021.1915392.

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Raghupathi, Wullianallur, and Viju Raghupathi. "Contemporary Business Analytics: An Overview." Data 6, no. 8 (2021): 86. http://dx.doi.org/10.3390/data6080086.

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We examine the state-of-the-art of the business analytics field by identifying and describing the four types of analytics and the three pillars of modeling. Further, we offer a framework of the interplay between the types of analytics and those pillars of modeling. The article describes the architectural framework and outlines an analytics methodology life cycle. Additionally, key contemporary design issues and challenges are highlighted. In this paper, we offer researchers and practitioners a contemporary overview of business analytics. As business analytics has emerged as a distinct discipline with the key objective to gain insight to make informed decisions, this state-of-the art survey sheds light on recent developments in the business analytics discipline.

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Schafer, Brad A., Sarah Bee, and Margaret Garnsey. "The Lemonade Stand: An Elementary Case for Introducing Data Analytics." AIS Educator Journal 13, no. 1 (2018): 29–43. http://dx.doi.org/10.3194/1935-8156-13.1.29.

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Accounting education has been encouraged to increase the business knowledge, analytical skills, and data analytic skills of accounting students. This case blends these areas in a single, multi-part project for Accounting Information Systems (AIS) courses. The case includes the technical function of extracting data from databases, integrating multiple data stores and using multiple software tools (MS Access and Tableau). Additionally, students learn to assess the business needs driving the use of integrated data stores to produce quality information for decision making. Using a basic business scenario (lemonade stand), this case provides a stand-alone project focusing on incorporating data analytics into an AIS course. Students assume the role of a professional consultant to a lemonade stand and will become familiar with the business processes and the data of the company, develop queries to answer various business questions, and integrate internal and external data to graphically analyze the combined data for a business analysis. The case allows integration of the course content of data extraction and reporting elements with data analytics. Students indicated that they perceived that they increased their knowledge about business analysis and data analytics tools. Student also indicated they enjoyed the case and had many positive comments about their experience. Results from a pre-/post-test quiz reflect that students did significantly increase their knowledge of business analysis and data analytics.

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Anderson, Charlotte Katie, Agueda Antonia Serra, and Lucas Casper Abrahamsen. "Impact of big data analytics & machine learning on innovation of manufacturing companies." Business & IT XII, no. 1 (2022): 116–24. http://dx.doi.org/10.14311/bit.2022.01.14.

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Developments in Business Analytics in the era of Big Data have furnished unprecedented possibilities for businesses to innovate. With insights gained from Business Analytics, businesses are in a position to cultivate new or even enhanced products/services. Nevertheless, not many scientific studies have examined the mechanism whereby Business Analytics plays a role in a firm 's innovation results. This particular analysis aims to deal with this gap by empirically and theoretically checking out the connection between Business Analytics as well as innovation. In order to do this aim, absorptive capability principle is employed as a theoretical lens to understand the improvement of a research version. Absorptive capacity theory describes a firm 's potential to understand the importance of fresh, external info, assimilate it and put it on to commercial ends. The study model covers the usage of Business Analytics, innovation, data-driven culture, environmental scanning, along with competitive advantage. The study design is examined by way of a questionnaire survey of 228 USA companies. The results suggest that Business Analytics specifically increases green scanning which helps you to improve a company 's development. Business Analytics also specifically enhances data driven culture which in turn impacts on green scanning. Data-driven culture plays another essential function by moderating the impact of green scanning on new product meaningfulness. The findings show the beneficial effect of company analytics on development and the pivotal roles of green scanning as well as data driven culture. Organizations wishing to realize the possibility of Business Analytics thus require changes in both their internal and external focus.

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Adaileh, Mohammad J., Muneer Alrwashdeh, Hasan Z. Abu AlZeat, and Nesrin salim Almatarneh. "The antecedents of supply chain performance: Business analytics, business process orientation, and information systems support." Uncertain Supply Chain Management 10, no. 2 (2022): 399–408. http://dx.doi.org/10.5267/j.uscm.2021.12.012.

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This study investigates the impact of business analytics (BA) on supply chain performance (SCP) in Saudi industrial companies. It also investigates the mediating impact of information systems supporting (ISS) and business process orientation (BPO). A total of 373 respondents working in 38 manufacturing companies in the Kingdom of Saudi Arabia (KSA) were selected. A scale with acceptable validity and reliability indicators was developed to measure the study variables. The results indicated significant indirect impact of the business analytics (planning, supply, make, delivery) on supply chain performance as ISS and BPO mediate this impact. Based on the results, a set of recommendations were proposed, industrial companies in KSA must develop the analytical capabilities for managers by increasing awareness of the benefits achieved from using business analytics approaches. It is a critical precedent for supply chain SC efficiency, and for companies to enhance their analytical capabilities with good ISS and process orientation to utilize in analyzing vast amounts of internal and external data. Directions for future research are also presented in this paper.

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Gerhart, Natalie, Russell Torres, and Laurie Giddens. "Challenges in the Model Development Process: Discussions with Data Scientists." Communications of the Association for Information Systems 53, no. 1 (2023): 591–611. http://dx.doi.org/10.17705/1cais.05325.

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Businesses are increasingly seeking out analytics to improve decision-making processes, although often with hesitations. Decision makers often do not have the sophisticated analytical skills needed to fully understand the analytics process. Contrastingly, data scientists may lack the business acumen needed to fully grasp the business context of the decision. In this research, we consider the perspective of the data scientist through a series of interviews to draw out challenges in the analytics process. We use principal-agent theory as a lens to shape our understanding of the conflict that arises due to goal misalignment and information asymmetry between the principal and agent. Findings are presented in the CRISP-DM process and a future research agenda is proposed.

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Henry Ejiga Adama and Chukwuekem David Okeke. "Harnessing business analytics for gaining competitive advantage in emerging markets: A systematic review of approaches and outcomes." International Journal of Science and Research Archive 11, no. 2 (2024): 1848–54. http://dx.doi.org/10.30574/ijsra.2024.11.2.0683.

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In today's rapidly evolving global economy, businesses operating in emerging markets face unique challenges and opportunities. To gain a competitive edge in such dynamic environments, harnessing the power of business analytics has become imperative. This systematic review aims to explore the diverse approaches and outcomes associated with leveraging business analytics for competitive advantage in emerging markets. The study employs a comprehensive review methodology, analyzing a wide range of scholarly articles, case studies, and industry reports. Through this systematic approach, key themes and patterns emerge, shedding light on the multifaceted strategies employed by organizations to leverage business analytics effectively. Findings reveal that successful utilization of business analytics in emerging markets involves several interconnected dimensions. These include data collection and management, advanced analytics techniques such as predictive modeling and data mining, and the integration of analytics into strategic decision-making processes. Moreover, organizational factors such as leadership support, organizational culture, and resource allocation significantly influence the effectiveness of business analytics initiatives. Furthermore, the review highlights the diverse outcomes associated with adopting business analytics in emerging markets. These outcomes encompass enhanced operational efficiency, improved customer insights, better risk management, and the identification of new market opportunities. Additionally, business analytics enables organizations to adapt swiftly to market changes and gain a deeper understanding of customer behaviors and preferences. Overall, this systematic review underscores the critical role of business analytics in enabling organizations to navigate the complexities of emerging markets and achieve sustainable competitive advantage. It provides valuable insights for practitioners, policymakers, and researchers seeking to leverage analytics-driven strategies for success in dynamic business environments.

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Kumar, Amit, Bala Krishnamoorthy, Divakar Kamath, Christine D', and N. A. Lima. "Business analytics adoption in firms." International Journal of Business Information Systems 40, no. 4 (2022): 464. http://dx.doi.org/10.1504/ijbis.2022.124928.

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Zhao, Kang, Junjie Wu, and Dirk Neumann. "Business analytics for social goods." Electronic Commerce Research and Applications 54 (July 2022): 101168. http://dx.doi.org/10.1016/j.elerap.2022.101168.

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Raisinghani, Mahesh S. "Data Analytics for Business Value." Journal of Information Technology Case and Application Research 23, no. 2 (2021): 145–51. http://dx.doi.org/10.1080/15228053.2021.1916232.

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Bergmann, Mareike, Christian Brück, Thorsten Knauer, and Anja Schwering. "Business Analytics in der Budgetierung." Controlling 33, no. 3 (2021): 51–58. http://dx.doi.org/10.15358/0935-0381-2021-3-51.

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Im Zuge der Digitalisierung bietet Business Analytics das Potenzial, die Budgetierung insbesondere durch eine Automatisierung von Prozessschritten der Budgetierung maßgeblich weiterzuentwickeln. Dieser Beitrag zeigt mittels einer empirischen Untersuchung den Status quo des Einsatzes von Business Analytics im Rahmen der Budgetierung in Deutschland und geht auf die Beurteilung einer Automatisierung der Budgetierung durch Unternehmen ein.

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Garrow, Laurie A., Mark E. Ferguson, and Robert G. Cross. "Breakthrough analytics for business acceleration." Journal of Revenue and Pricing Management 11, no. 2 (2011): 243–49. http://dx.doi.org/10.1057/rpm.2011.39.

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de Vasconcelos, José Braga, and Álvaro Rocha. "Business analytics and big data." International Journal of Information Management 46 (June 2019): 320–21. http://dx.doi.org/10.1016/j.ijinfomgt.2018.10.019.

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Vasconcelos, José Braga de, and Álvaro Rocha. "Business Analytics and Big Data." International Journal of Information Management 46 (June 2019): 250–51. http://dx.doi.org/10.1016/j.ijinfomgt.2019.03.001.

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Mazouz, Abdelkader. "Quality management business analytics framework." International Journal of Economics and Business Research 12, no. 1 (2016): 1. http://dx.doi.org/10.1504/ijebr.2016.078793.

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Kohavi, Ron, Neal J. Rothleder, and Evangelos Simoudis. "Emerging trends in business analytics." Communications of the ACM 45, no. 8 (2002): 45–48. http://dx.doi.org/10.1145/545151.545177.

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Schäffer, Utz, and Jürgen Weber. "Business Analytics: Yes, we can!" Controlling & Management Review 61, no. 4 (2017): 3. http://dx.doi.org/10.1007/s12176-017-0038-y.

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