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Journal articles on the topic 'Business analytics and decision science'

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1

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
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Chidera, Victoria Ibeh, Franca Asuzu Onyeka, Olorunsogo Temidayo, Adijat Elufioye Oluwafunmi, Leonard Nduubuisi Ndubuisi, and Ifesinachi Daraojimba Andrew. "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. https://doi.org/10.5281/zenodo.14041802.

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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
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Goar, Vishal Kumar, and Nagendra Singh Yadav. "Business Decision Making by Big Data Analytics." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 5 (2022): 22–35. http://dx.doi.org/10.17762/ijritcc.v10i5.5550.

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Information is the key component towards success when it comes to controlling the decision-makers performance with the quality of a decision. In the modern era, an absolute amount of data is available to organizations for analysis usage. Data is the most important component of the business in the 21st century and a significant number of devices are already equipped with the internet. Based on this the solutions should be studied in order to control and capture the knowledge value pair out of the datasets. Following this, the decision-makers should have access to insightful and valuable data ba
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Wang, Jiangping. "Bridging the Gap between Business Practice and Data Science Approaches." Transactions on Engineering and Computing Sciences 13, no. 02 (2025): 01–09. https://doi.org/10.14738/tecs.1302.18289.

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In data science and analytics, the driving force is not on how to perform analytics tasks or how to use advanced technology in analytics projects. Business problems and goals should always drive the overall approaches. Projects and applications in data science and analytics should serve business goals and help business decision making. In this paper, a case study that serves various directions in answering business questions is presented.
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Wang, Jiangping. "Bridging the Gap between Business Practice and Data Science Approaches." Transactions on Machine Learning and Artificial Intelligence 13, no. 02 (2025): 01–09. https://doi.org/10.14738/tmlai.1302.18289.

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In data science and analytics, the driving force is not on how to perform analytics tasks or how to use advanced technology in analytics projects. Business problems and goals should always drive the overall approaches. Projects and applications in data science and analytics should serve business goals and help business decision making. In this paper, a case study that serves various directions in answering business questions is presented.
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Tayeb, Khabirun Maria. "Decision-Making & Data Science: How Large Businesses Can Use Analytics to Shape Decisions." Business & IT XIII, no. 2 (2023): 55–64. http://dx.doi.org/10.14311/bit.2023.02.06.

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Although big data will bring worth for business throughout the entire worth chain, the integration of details analytics about the decision-making procedure is nevertheless a battle. This specific study, based on an ordered literature review, thematic evaluation and qualitative interview findings, proposes 6 steps to create every relevance and rigor in the technique of analytics driven choice generating. Our findings illuminate the main key phases in this specific option procedure, for example matter characterization, evaluation of earlier outcomes, model development, data collection, informati
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Lee, Chee Sun, and Peck Yeng Sharon Cheang. "Predictive Analysis in Business Analytics: Application of Decision Tree in Business Decision Making." Advances in Decision Sciences 26, no. 1 (2021): 1–29. http://dx.doi.org/10.47654/v26y2022i1p1-29.

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Business Analytics was defined as one of the most important aspects of combinations of skills, technologies and practices which scrutinize a corporation’s data and performance to transpire a data driven decision making analysis for a corporation’s future direction and investment plans. In this paper, much of the focus will be given to the predictive analysis which is a branch of business analytics which scrutinize the application of input data, statistical combinations and intelligence machine learning (ML) statistics on predicting the plausibility of a particular event happening, forecast fut
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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
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Odogwu, Rosebenedicta, Jeffrey Chidera Ogeawuchi, Abraham Ayodeji Abayomi, Oluwademilade Aderemi Agboola, and Samuel Owoade. "Bridging the Gap between Data Science and Decision Makers: A Review of Augmented Analytics in Business Intelligence." International Journal of Management and Organizational Research 2, no. 3 (2023): 61–69. https://doi.org/10.54660/ijmor.2023.2.3.61-69.

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The integration of augmented analytics into business intelligence systems is reshaping how organizations transform complex datasets into actionable insights. This paper reviews the evolution, core components, and strategic value of augmented analytics as a bridge between technical data processes and executive decision-making. Emphasis is placed on the use of artificial intelligence, machine learning, and natural language generation to automate data preparation, pattern recognition, and insight delivery in a user-friendly format. The study highlights how these technologies enhance accessibility
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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, an
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Goldstein, Michael Sienna. "Firm level strategic decision-making with data science & analytics." Business & IT XII, no. 1 (2022): 211–18. http://dx.doi.org/10.14311/bit.2022.01.25.

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Even though the usage of big data will add value for business throughout the whole value chain, the integration of big data analytics on the decision-making process is still a struggle. This particular study, according to an organized literature review, thematic analysis as well as qualitative interview findings, proposes a set of six steps to build each relevance and rigor in the approach of analytics driven decision making. Our findings illuminate the primary key stages in this particular choice process such as issue definition, review of previous results, data collection, model development,
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Christenson Jr., Arthur Paul, and William Shalom Goldstein. "Impact of data analytics in transforming the decision-making process." Business & IT XII, no. 1 (2022): 74–82. http://dx.doi.org/10.14311/bit.2022.01.09.

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Although business analytics is becoming more and more used to provide data-driven insights to support decision making, there is little research on how business analytics may be used at an organizational level to enhance decision making effectiveness. This paper develops a study model linking company analytics to organizational decision-making effectiveness, using the info processing view as well as contingency theory. Based on 740 responses from UK business organizations, the research model is examined using structural situation modelling. Key findings show that business analytics can be done
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Yoo, Dong, and James J. Roh. "Value Chain Creation in Business Analytics." Journal of Global Information Management 29, no. 4 (2021): 131–47. http://dx.doi.org/10.4018/jgim.20210701.oa6.

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Firms are awash in big data and analytical technology as part of the process of deriving values in the current turbulent environment. The literature has reached a consensus that investments in technology only may not reap benefits from business analytics (BA). As such, the main purpose of BA is not about how to install technical capabilities, but about how to create a process whereby a firm builds a value chain converting data into insights and ultimately into quality outcomes. Drawing upon the theory of the information value chain, this study develops a BA value chain creation model and tests
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Daradkeh, Mohammad Kamel. "An Empirical Examination of the Relationship Between Data Storytelling Competency and Business Performance." Journal of Organizational and End User Computing 33, no. 5 (2021): 42–73. http://dx.doi.org/10.4018/joeuc.20210901.oa3.

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With the proliferation of big data and business analytics practices, data storytelling has gained increasing importance as an effective means for communicating analytical insights to the target audience to support decision-making and improve business performance. However, there is a limited empirical understanding of the relationship between data storytelling competency, decision-making quality, and business performance. Drawing on the resource-based view (RBV), this study develops and validates the concept of data storytelling competency as a multidimensional construct consisting of data qual
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Lytras, Miltiadis D., Vijay Raghavan, and Ernesto Damiani. "Big Data and Data Analytics Research." International Journal on Semantic Web and Information Systems 13, no. 1 (2017): 1–10. http://dx.doi.org/10.4018/ijswis.2017010101.

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The Big Data and Data Analytics is a brand new paradigm, for the integration of Internet Technology in the human and machine context. For the first time in the history of the human mankind we are able to transforming raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship. For the Web Science research, this is a new methodological and technological spectrum of advanced methods, frameworks and functionalities never experienced in the past. At the
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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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Mikhnenko, Pavel. "Multimodal business analytics: The concept and its application prospects in economic science and practice." Upravlenets 14, no. 6 (2024): 2–18. http://dx.doi.org/10.29141/2218-5003-2023-14-6-1.

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One of the problems of business analysis is obtaining and processing an ever-increasing volume of economic, financial, organizational, political and legal data. Multimodal business analytics is a new methodology combining the methods of classical business analysis with big data technologies, intelligent business analytics, multimodal data fusion, artificial neural networks and deep machine learning. The purpose of the study is to determine the conceptual foundations of the phenomenon of multimodal business analytics and substantiate the prospects for its use in economic science and practice. M
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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 busin
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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
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Gong, Zean. "Data Science Applications in Supply Chain Management Decision-making." Advances in Economics, Management and Political Sciences 108, no. 1 (2024): 36–43. http://dx.doi.org/10.54254/2754-1169/108/20241920.

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This research views data science as the basis of the decision-making process at SCM. Tough international trade environment characterized by complex supply chain and inventory issues as well as unpredictable demand for goods necessitates powerful analytics tools. Using the latest technologies - machine learning, predictive analytics, and big data - data science generates data-driven decisions for more accurate, efficient, and prompt SCM decision-making. The study intends to study the current trends and evaluate the influence of data science in SCM decision-making processes. It also delves into
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Gong, Zean. "Data Science Applications in Supply Chain Management Decision-making." Advances in Economics, Management and Political Sciences 89, no. 1 (2024): 121–28. http://dx.doi.org/10.54254/2754-1169/89/20241920.

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This research views data science as the basis of the decision-making process at SCM. Tough international trade environment characterized by complex supply chain and inventory issues as well as unpredictable demand for goods necessitates powerful analytics tools. Using the latest technologies - machine learning, predictive analytics, and big data - data science generates data-driven decisions for more accurate, efficient, and prompt SCM decision-making. The study intends to study the current trends and evaluate the influence of data science in SCM decision-making processes. It also delves into
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Pranay, Mungara. "The Power of Data Analytics: Driving Business Growth in the Digital Age." European Journal of Advances in Engineering and Technology 9, no. 8 (2022): 52–60. https://doi.org/10.5281/zenodo.12737401.

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The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process aut
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Abiola Moshood Komolafe, Iyadunni Adewola Aderotoye, Oluwatosin Oluwatimileyin Abiona, et al. "HARNESSING BUSINESS ANALYTICS FOR GAINING COMPETITIVE ADVANTAGE IN EMERGING MARKETS: A SYSTEMATIC REVIEW OF APPROACHES AND OUTCOMES." International Journal of Management & Entrepreneurship Research 6, no. 3 (2024): 838–62. http://dx.doi.org/10.51594/ijmer.v6i3.939.

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This study systematically reviews the impact of business analytics on achieving competitive advantage in emerging markets, focusing on the integration of advanced analytical tools and strategies within organizational processes. Employing a systematic literature review and content analysis methodology, this research scrutinizes peer-reviewed articles, conference papers, and grey literature from 2018 to 2023, sourced from databases such as Web of Science, Scopus, and IEEE Xplore. The inclusion criteria targeted studies that explore the application, outcomes, and strategic implications of busines
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Jintana, J., A. Sopadang, and S. Ramingwong. "Idea selection of new service for courier business: The opportunity of data analytics." International Journal of Engineering Business Management 13 (January 1, 2021): 184797902110421. http://dx.doi.org/10.1177/18479790211042191.

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E-commerce growth enforces the courier business to focus on developing a new business model decision. This paper aims to explore a suitable new business idea for courier business if the Data Analytics (DA) can be advantageous, using small courier company as a case study. The study investigates Logistics Service Provider (LSP) activities and the Gap Analysis and SWOT analysis were conducted to explore Data Analytics (DA) opportunity. Then, the alternative business models were pre-screened by the requirements of company, i.e. reasonable investment cost and the opportunity in using Data Analytics
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Lakhan, Nidhi. "Applications of Data Science and AI in Business." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 4115–18. http://dx.doi.org/10.22214/ijraset.2022.43343.

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Abstract: Just like natural human intelligence, Artificial Intelligence (AI) can be explained as the intelligence demonstrated by machines, or rather how the machines and models are trained to predict outcomes and give answers in a way that mimic human intelligence. The goal of artificial intelligence is to provide software that can reason on input and explain the output through its various methods. Artificial Intelligence has found its applications in almost every sector in every industry – ranging from finance to marketing to general business management. Similarly, Data Science deals with de
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Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen. "The role of data science in transforming business operations: Case studies from enterprises." Computer Science & IT Research Journal 5, no. 8 (2024): 2026–39. http://dx.doi.org/10.51594/csitrj.v5i8.1490.

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Data science has emerged as a pivotal force in transforming business operations across various industries, driving innovation, operational efficiency, and strategic decision-making. This review paper explores the multifaceted role of data science in business, examining key concepts, historical integration, and strategic advantages. It discusses the application of data science in diverse business domains, highlighting techniques such as predictive analytics, sentiment analysis, and optimization that have revolutionized marketing, supply chain management, finance, and customer service. The paper
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Dileesh, Chandra Bikkasani. "Leveraging Artificial Intelligence for Business Analytics: A Data-Science based Decision Support System Framework." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 2 (2025): 1507–15. https://doi.org/10.5281/zenodo.14964501.

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Artificial Intelligence (AI) transforms business intelligence (BI) by enhancing decision-making speed, accuracy, and depth in today’s data-driven landscape. Traditional Decision Support Systems (DSS), once foundational to BI, struggle to handle modern data's complexity, scale, and diversity, often resulting in limited decision-making agility. Integrating AI into DSS has become essential to bridge this gap, enabling these systems to process vast datasets in real-time and make predictive, data-informed recommendations. This study presents an AI-powered DSS framework designed to address the
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Virani, Dr Farida. "Application of HR Analytics in Business." MET MANAGEMENT REVIEW 07, no. 02 (2023): 05–19. http://dx.doi.org/10.34047/mmr.2020.7201.

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In the ever-evolving landscape of business management, Human Resources (HR) analytics has emerged as a crucial tool for optimizing workforce strategies and enhancing organizational performance. This study delves into the application of HR analytics in the business context, investigating how data-driven insights from various HR processes and employee-related metrics can inform decision-making. By leveraging quantitative analysis and case studies, this research explores how HR analytics offers valuable insights into employee recruitment, retention, training, performance evaluation, and overall w
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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 intelligen
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O‘g‘li Tuychibayev, Sanjarbek Akmaljon. "Using Data Science in Business: A Focus on Uzbekistan." European Journal of Management, Economics and Business 1, no. 3 (2024): 234–37. https://doi.org/10.59324/ejmeb.2024.1(3).20.

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This article examines the increasing role of Data Science in business, with a focus on Uzbekistan. It discusses the application of key Data Science techniques, such as machine learning, big data analytics, and data visualization, in sectors like banking, telecommunications, and retail. The benefits of Data Science, including market forecasting and risk management, are contrasted with the challenges faced by businesses in Uzbekistan, such as a shortage of skilled professionals and inadequate technological infrastructure. Additionally, successful case studies from both Uzbekistan and internation
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Sanjarbek, Akmaljon o'g'li Tuychibayev. "Using Data Science in Business: A Focus on Uzbekistan." European Journal of Management, Economics and Business 1, no. 3 (2024): 234–37. https://doi.org/10.59324/ejmeb.2024.1(3).20.

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This article examines the increasing role of Data Science in business, with a focus on Uzbekistan. It discusses the application of key Data Science techniques, such as machine learning, big data analytics, and data visualization, in sectors like banking, telecommunications, and retail. The benefits of Data Science, including market forecasting and risk management, are contrasted with the challenges faced by businesses in Uzbekistan, such as a shortage of skilled professionals and inadequate technological infrastructure. Additionally, successful case studies from both Uzbekistan and internation
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Niu, Yanfang, Limeng Ying, Jie Yang, Mengqi Bao, and C. B. Sivaparthipan. "Organizational business intelligence and decision making using big data analytics." Information Processing & Management 58, no. 6 (2021): 102725. http://dx.doi.org/10.1016/j.ipm.2021.102725.

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BURKINA, N.V. "Influence of Big Data and Business Analytics use on Ukrainian business." Market Relations Development in Ukraine №3(226)2020 122 (May 15, 2020): 51–56. https://doi.org/10.5281/zenodo.3829617.

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Corporate information management implies its prompt collection, efficient organization and optimal use for the benefit of the business. Along with quantitative, or structured, data that can be collected and organized in tables, most of the information is represented by a huge number of documents, emails, videos, and other unstructured content. It is no less important but implies more detailed methods of systematization and the application of new methods, technologies and data processing tools. One of them is Big Data. Due to big data, companies can gain tangible competitive advantages. Today,
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Yu, Huan, Ye Shi, Yugang Yu, Jie Liu, Feng Yang, and Jie Wu. "Business analytics: online promotion with gift rewards." Annals of Operations Research 291, no. 1-2 (2019): 1061–76. http://dx.doi.org/10.1007/s10479-019-03193-3.

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Mohna, Hosne Ara. "A SYSTEMATIC REVIEW OF DATA ANALYTICS IN BUSINESS STRATEGY: MODELS, TOOLS, AND COMPETITIVE ADVANTAGE." Journal of Sustainable Development and Policy 01, no. 01 (2025): 44–64. https://doi.org/10.63125/np6jdt81.

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In the age of digital transformation and data abundance, data analytics has become a fundamental driver of strategic decision-making in modern enterprises. This systematic review critically examines the role of data analytics in shaping business strategy, with a focus on analytical typologies, technological tools, and the mechanisms through which analytics contributes to competitive advantage. Guided by the PRISMA methodology, a total of 162 peer-reviewed journal articles published between 2015 and 2025 were systematically identified, screened, and analyzed from major academic databases includ
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Abayomi, Abraham Ayodeji, Olanrewaju Oluwaseun Ajayi, Jeffrey Chidera Ogeawuchi, Andrew Ifesinachi Daraojimba, Bright Chibunna Ubanadu, and Chisom Elizabeth Alozie. "A conceptual framework for accelerating data-centric decision-making in agile business environments using cloud-based platforms." International Journal of Social Science Exceptional Research 1, no. 1 (2022): 270–76. https://doi.org/10.54660/ijsser.2022.1.1.270-276.

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In today’s volatile and fast-paced digital economy, organizations are increasingly required to make timely, data-informed decisions while maintaining flexibility and responsiveness. This paper presents a robust conceptual framework designed to accelerate data-centric decision-making in agile business environments through the strategic integration of cloud-based platforms. The framework comprises four interdependent components: data acquisition and integration, analytics and insight generation, decision orchestration, and feedback and learning loops. Leveraging a design science research methodo
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Pacis, Arkhe, and P. Dela Cruz. "BUSINESS ANALYTICS IMPLEMENTATION: A SYSTEMATIC LITERATURE REVIEW." International Journal of Industrial Management 19, no. 2 (2025): 104–15. https://doi.org/10.15282/ijim.19.2.2025.11462.

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Business analytics (BA) has emerged as a pivotal tool for enhancing decision-making and business performance through data-driven insights. However, its implementation remains a significant challenge. This study employed a systematic literature review methodology and utilised Scopus, Web of Science, and Google Scholar as a backup database to retrieve relevant literature. The review followed a structured process comprising identification, screening, eligibility, and data collection/extraction stages. The searches initially retrieved 83 articles, of which 41 met the inclusion criteria based on pe
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Fattah, Ikhsan A., Rano Kartono, Mohammad Hamsal, and Asnan Furinto. "Decision Making Performance of Business Data Analytical Capabilities: The Mediating Effect of Analytics Competency." International Journal of Membrane Science and Technology 10, no. 2 (2023): 3859–73. http://dx.doi.org/10.15379/ijmst.v10i2.3249.

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This research study investigates the mediating influence of analytics competency (AC) on the relationship between business data analytical capabilities (BDAC) and decision-making performance (DMP). Using a quantitative approach, 167 managers with experience and competence in using data in Indonesian public service sector organizations were empirically evaluated. Structural Equation Modeling analysis was applied to examine the impact of BDAC on DMP and mediating effects of AC on this relationship. The results demonstrate that BDAC and AC significantly influence DMP, and BDAC significantly affec
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Prasath, A. Rama, S. Leelavathy, P. S. G. Aruna Sri, G. Saranya, and S. V. Manikanthan. "Data-Driven Decision-Making Use Case: Applying Big Data Analytics to Forecast Important Decisions." International Journal of Engineering, Science and Information Technology 5, no. 3 (2025): 402–8. https://doi.org/10.52088/ijesty.v5i3.1122.

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Due to decreased material and resource usage and other tooling needs, additive manufacturing (AM) has rapidly developed over the past 10 years. It has shown significant promise for energy-efficient and environmentally friendly production. As manufacturing technologies have advanced in the modern period, intelligent manufacturing has gained greater attention from academia and business to increase the sustainability and efficiency of their output. Few studies have examined the effects of big data analytics (BDA) in CSR activities on CSR performance, despite the growing number of businesses imple
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Yi, Ran. "Optimizing Operations Management and Business Analytics Strategies under Uncertainty: Dynamic Programming." Advances in Economics, Management and Political Sciences 49, no. 1 (2023): 150–56. http://dx.doi.org/10.54254/2754-1169/49/20230507.

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Dynamic programming is a method used in mathematics, management science, computer science, economics, and bioinformatics to solve complex problems by decomposing the original problem into relatively simple sub-problems. Dynamic programming is often applicable to problems with overlapping subproblems and optimal substructure properties. This paper employs a comprehensive research approach of literature review, as well as empirical analysis and case studies to investigate the topic and demonstrate the practicality and effectiveness of dynamic programming in solving complex decision-making proble
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Upadhyay, Devesh, Ritanshi Jain, Vasudev Sharma, Ishika Pal, and Ayush Singhal. "Unlocking insights: telecom customer churn analysis with power BI." International Journal of Research in Engineering and Innovation 09, no. 03 (2025): 143–53. https://doi.org/10.36037/ijrei.2025.9309.

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In this work, a comprehensive customer churn analysis is conducted using Microsoft Power BI to support data-driven business decisions in competitive markets, where customer retention is more cost-effective than acquisition. The study focuses on a telecom customer dataset, combining data preprocessing, visual analytics, and predictive modeling to uncover churn patterns and critical risk factors. Through the use of interactive dashboards, stakeholders gain clear, actionable insights into customer behavior and potential churn triggers. The integration of Power BI enables dynamic exploration of da
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Sachin, Kumar, and S. Aithal P. "How Lucrative & Challenging the Boundary less Opportunities for Data Scientists?" International Journal of Case Studies in Business, IT, and Education (IJCSBE) 4, no. 1 (2020): 223–36. https://doi.org/10.5281/zenodo.3966222.

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The data scientist is a new profession which is considered as a key profession in the world of technologies and is one of the best paid jobs. A data scientist is a person who has developed expertise in the mathematical modelling and statistics that dominates programming and its different languages, computer science, and analytics. Data science comprises of data gathering, data warehousing, data analysis, data mining, online analytical processing, artificial intelligence, machine learning, and decision science for Predictive and prescriptive analytics for supporting managers for future decision
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Anil Tiwari. "Integrating Computer Science with Management Education: A Framework for Enhancing Decision-Making Skills in the Digital Age." Journal of Information Systems Engineering and Management 10, no. 21s (2025): 339–51. https://doi.org/10.52783/jisem.v10i21s.3340.

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Management education cannot afford to remain bereft of computer science in the age of the digital. Integration of computer science in management education is important so as to improve decision making skills. Referred in this research: A framework using artificial intelligence (AI), machine learning, gamification and data driven analytics to enhance strategic thinking in management studies. The study examines four main algorithms i.e. Decision Trees, Random Forest, Support Vector Machine (SVM) and Artificial Neural Network (ANN) to figure out which one best suits in predictive decision making.
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Ocaña, Luis Llerena Oca�, Dionisio Ponce Ruiz, and Betty Valle Fiallos. "Optimizing Retail Business Strategies with Advanced Analytics and Improved Business Intelligence Techniques." Journal of Intelligent Systems and Internet of Things 11, no. 1 (2024): 75–83. http://dx.doi.org/10.54216/jisiot.110108.

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The retail landscape thrives on the synthesis of advanced analytics and business intelligence techniques, pivotal in navigating the complexities of consumer behavior and market dynamics. This study addresses the imperative to optimize retail strategies by leveraging historical sales data from 45 diverse stores with multifaceted departments. The challenge of predicting retail sales prices guided our methodology, employing convolutional neural network architectures and Root Mean Square Error (RMSE) as the principal error metric. Through iterative computations and feature extractions, our model a
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Grabovy, Pyotr, and Nikolai Siniak. "Using AI and big data in decision making: A framework across disciplines." E3S Web of Conferences 535 (2024): 05011. http://dx.doi.org/10.1051/e3sconf/202453505011.

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The recent growth in computer architecture has changed the face of education, science and engineering. Technology does not stand still, and today, when assessing the economic development of the organization, university or industry, the attention is paid to its willingness to use new technologies, especially in the field of Artificial Intelligence (AI) and big data analytics. In this research, the topic of AI and Big Data and its integration into decision-making process for competitiveness is examined. The emergence of Big Data and related analytics technologies led to changes in the business w
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Putsenteilo, Petro, Andrii Dovbush, Tetiana Bincharovska, and Viktoriia Homotiuk. "MODERN TECHNOLOGIES OF BUSINESS ANALYTICS AS A TOOL FOR IMPROVING THE COMPANY'S BUSINESS COMMUNICATIONS." INSTITUTE OF ACCOUNTING, CONTROL AND ANALYSIS IN THE GLOBALIZATION CIRCUMSTANCES, no. 1-2 (June 30, 2022): 29–40. http://dx.doi.org/10.35774/ibo2022.01-02.029.

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Introduction. Modern information and digital solutions qualitatively change all chains of business processes of companies and open wide opportunities for business communications. However, companies need to independently determine their needs in the development and implementation of appropriate information and software solutions, substantiation the reasons for the rational choice of adequate options for analytical procedures from the multitude of digital options currently available. In fact, the problems of choosing directions for the analysis of business indicators are in the general direction
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Wang, Zhongxian, Zhi Pei, and Vicky Ching Gu. "Strategic Applications of Business Analytics to Healthcare and Hospital Management." International Journal of Applied Research on Public Health Management 4, no. 2 (2019): 47–64. http://dx.doi.org/10.4018/ijarphm.2019070104.

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Business analytics provides the framework to exploit the synergies among traditionally-diverse topics, such as the fields of data mining, quantitative methods, operations research/management science, decision support systems, and so forth, in a more practical, application-driven format. The authors investigate and explore business analytics applications in improving hospital and healthcare management, as well as controlling its ascending cost. Research frontiers confront many areas, such as prevention management, inpatients management, postoperative management, supply chain management, perform
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Tung, Pooi Lee, Yu Shin Chan, Jun Hong Lim, and NUR AZALIAH ABU BAKAR. "Leveraging Business Analytics for Optimized Supply Chain Management in Healthcare: A Comprehensive Review." Open International Journal of Informatics 12, no. 2 (2024): 67–77. https://doi.org/10.11113/oiji2024.12n2.319.

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This paper examines the integration of Business Analytics (BA) within Supply Chain Management (SCM) in the healthcare sector. The healthcare sector has increasingly embraced Business Analytics to enhance Supply Chain Management, promising to streamline operations and improve decision- making. for future research directions. The systematic review method was employed to gather relevant literature from databases such as Scopus, Web of Science, and Google Scholar, focusing on themes related to BA in healthcare SCM. The study identifies key themes such as operational efficiency, decision-making enh
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Husen, Amir, and Syed Mustafizur Rahman Chowdhury. "Interdisciplinary Nature of Computational Science Cases in Business Studies." Global Disclosure of Economics and Business 12, no. 1 (2023): 1–14. http://dx.doi.org/10.18034/gdeb.v12i1.692.

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The intricate confluence of computational science and business studies heralds an unprecedented era in the academic and industrial landscape. This paper comprehensively explores this interdisciplinary nexus, delving deep into the transformative potential that computational methodologies bring to business arenas. From leveraging vast datasets in business analytics to pioneering financial models, computational tools are reshaping the very paradigms of business decision-making. However, these advancements are not without challenges. Ethical considerations, over-reliance on models, and the essenti
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Ramalakshmi, S., and G. Asha. "Embracing Innovative Approaches in Data Science: Investigating Contemporary Trends in Data Collection, Analysis, and Visualization Methods." Journal of Trends in Computer Science and Smart Technology 5, no. 3 (2023): 343–66. http://dx.doi.org/10.36548/jtcsst.2023.3.008.

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Today businesses are becoming more productive and their return on investment (ROI) is increasing with the development of new technologies like data science, artificial intelligence and data analytics. In today's trend organizations are dealing with big data and these data can drive the whole organization in many ways. The process of doing data analysis and extracting meaningful insight is known as data science. Most business organizations are taking data driven models to ease their work and for making intelligent business decisions. The life cycle of a data science involves so many steps like
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