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Journal articles on the topic 'Data-Driven Analytics'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Darshini, T. Divya, and S. Kalaiselvi. "Data-Driven Dynamic Fashion Market Insights Using Data Analytics." International Journal of Research Publication and Reviews 5, no. 3 (2024): 4748–57. http://dx.doi.org/10.55248/gengpi.5.0324.0808.

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Middae, Vijaya lakshmi. "Enhancing Cloud Security with AI-Driven Big Data Analytics." American Journal of Engineering and Technology 07, no. 05 (2025): 185–91. https://doi.org/10.37547/tajet/volume07issue05-18.

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Since cloud computing is changing so rapidly, maintaining strong security is now a major issue for companies everywhere. Massive volumes of mixed data are constantly created in cloud environments at every layer, involving virtual machines, containers, storage, identity management and application activities. It is usually not possible for traditional security systems and old monitoring tools to manage vast and changing data flow in real time. Con- ventional methods fail to discover advanced persistent threats, attacks by team members and new vulnerabilities because they do not easily adjust to

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3

Ranjan, Rajiv, Zheng Li, Massimo Villari, Yan Liu, and Dimitrios Georgeakopoulos. "Software-driven big data analytics." Computing 102, no. 6 (2020): 1409–17. http://dx.doi.org/10.1007/s00607-020-00822-9.

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John Thayil, Jerry. "Data - Driven Decisions: The Future of AI in Healthcare Analytics." International Journal of Science and Research (IJSR) 13, no. 10 (2024): 1615–17. http://dx.doi.org/10.21275/sr241023065221.

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Kim, Hayeon, and Soojin Jun. "How to leverage data analytics tools for data-driven UX." Journal of Communication Design 90 (January 31, 2025): 516–28. https://doi.org/10.25111/jcd.2025.90.35.

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Gan, Wensheng, Jerry Chun-Wei Lin, Han-Chieh Chao, Athanasios V. Vasilakos, and Philip S. Yu. "Utility-Driven Data Analytics on Uncertain Data." IEEE Systems Journal 14, no. 3 (2020): 4442–53. http://dx.doi.org/10.1109/jsyst.2020.2979279.

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7

Oddleifson, Evan. "The Effects of Modern Data Analytics in Electoral Politics." Political Science Undergraduate Review 5, no. 1 (2020): 46–52. http://dx.doi.org/10.29173/psur130.

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New implementations of data analytical processes in democratic politics deeply affect voter-representative relationships and constitute a substantive challenge to voter agency. This paper examines the effects of social media driven data analytics on voter microtargeting and electoral politics using Cambridge Analytica’s (CA) involvement in the 2016 US Presidential election and the 2010 Trinidad and Tobago General election. It finds that data-driven voter targeting strategies developed by Cambridge Analytica from 2014-2015 are substantially more effective than previously employed strategies. Mo

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Alexander, Cheryl Ann, and Lidong Wang. "Healthcare Driven by Big Data Analytics." American Journal of Engineering and Applied Sciences 11, no. 3 (2018): 1154–63. http://dx.doi.org/10.3844/ajeassp.2018.1154.1163.

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9

Abaya, Sheila, and Froilan De Guzman. "Data Analytics Tool (DAT): A Data-Driven Decision- Making Tool for Business and Data Analytics Courses." Technologique: A Global Journal on Technological Developments and Scientific Innovations 3, no. 1 (2024): 37–50. http://dx.doi.org/10.62718/vmca.tech-gjtdsi.3.1.sc-1024-003.

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Data Analytics Tool (DAT) is an application that collects and analyzes data in order to improve business processes and aid in discovering hidden trends and patterns thereby uncovering insights to make future data- driven and intelligent decisions. This project, which is aligned with the business and data analytic course contents of the University of the East, Caloocan, is intended for particular users, such as the faculty members and students enrolled in these courses, so as to uniformly use the application in simulating business analytics cases with the given data set in their respective clas

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Berndtsson, Mikael, and Stefan Ekman. "Assessing Maturity in Data-Driven Culture." International Journal of Business Intelligence Research 14, no. 1 (2023): 1–17. http://dx.doi.org/10.4018/ijbir.332813.

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Research on assessing a group's maturity in data-driven culture is rare and fragmented. This article investigates how maturity in data-driven culture can be assessed from a historical perspective. A case study was done on how the Education Council evolved in analytics maturity and as a group during 2014-2023. The assessment showed that the Education Council experienced both successful progression of group development and usage of analytics, as well as regression in group development and analytics usage. The practical implications of the findings are that group leaders need to be aware of the i

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Majed, Abdul. "A Data-driven Digital Analytics Framework for Mobile App Analytics." International Journal of Computer Applications 175, no. 8 (2017): 21–23. http://dx.doi.org/10.5120/ijca2017915619.

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Parmar, Ahaan. "AI-DRIVEN DATA ANALYTICS FOR REAL-TIME DECISION-MAKING." IINTERNATIONAL JOURNAL OF PROGRESSIVE RESEARCH IN ENGINEERING MANAGEMENT AND SCIENCE 5, no. 5 (2025): 372–86. https://doi.org/10.58257/IJPREMS40153.

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Artificial intelligence-powered data analytics functions as an indispensable transformational force that helps organizations obtain immediately useful information from large databases while responding rapidly to shifting market conditions across different business sectors. This research analyzes how artificial intelligence, when connected to data analytics, drives transformational development through analyses of real-time applications along with advantages and obstacles that exist in addition to future analytical patterns. Through their union, data analytics and artificial intelligence systems

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Cho, Young-Jun, Seok-Yeong Bang, Hyeon-Min Yoo, and Een-Kee Hong. "Network Data Analytics for Data-Driven Control Loop of RAN Intelligent Controller." Journal of Korean Institute of Communications and Information Sciences 48, no. 9 (2023): 1190–93. http://dx.doi.org/10.7840/kics.2023.48.9.1190.

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Deva, Abhas, Vojtech Krs, Lucas D. Robinson, et al. "Data driven analytics of porous battery microstructures." Energy & Environmental Science 14, no. 4 (2021): 2485–93. http://dx.doi.org/10.1039/d1ee00454a.

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Zahaib Nabeel, Muhammad. "Big Data Analytics-Driven Project Management Strategies." Journal of Science & Technology 5, no. 1 (2024): 117–63. http://dx.doi.org/10.55662/jst.2024.5104.

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The integration of Artificial Intelligence (AI) and Big Data Analytics (BDA) in project management has become a critical enabler of efficiency in managing large-scale, complex projects. This research paper delves into how AI-driven big data analytics can revolutionize traditional project management methodologies by introducing dynamic scheduling, real-time risk prediction, and automated task prioritization strategies. These advanced techniques, which leverage machine learning (ML) models and extensive historical project data, enable a shift from reactive to proactive project management, ensuri

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Al-Zahrani, Abdulrahman M., and Talal Alasmari. "Learning Analytics for Data-Driven Decision Making." International Journal of Online Pedagogy and Course Design 13, no. 1 (2023): 1–18. http://dx.doi.org/10.4018/ijopcd.331751.

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This study examines the use of learning analytics to enhance instructional personalization and student engagement in online higher education. The research focuses on the engagement levels of students based on different access methods (mobile and non-mobile), the relationships among engagement indicators, and the strategies for instructional personalization. Quantitative research methodology is employed to analyse and measure students' engagement levels. The findings indicate that students using non-mobile devices exhibit higher engagement in terms of average minutes, item accesses, and content

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Smilowitz, Daniel, and Ashish Tiwari. "Supporting Grid Infrastructure Through Data‐Driven Analytics." Climate and Energy 39, no. 12 (2023): 1–8. http://dx.doi.org/10.1002/gas.22352.

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18

Yeolekar, Varun, Riya Joshi, Chanchal Bansal, and Nisha Rathi. "Customer Churn Analytics: A Data-Driven Approach." International Journal of Advancement and Innovation in Technology and Research 2, no. 1 (2025): 22–29. https://doi.org/10.63844/ijaitr.v2.i1.2025.22-29.

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19

Narendranath Yenuganti. "Advanced data analytics: Transforming business operations through data-driven insights." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1243–49. https://doi.org/10.30574/wjaets.2025.15.1.0352.

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Advanced data analytics has emerged as a transformative force in modern business operations, revolutionizing how organizations make decisions and optimize their processes. The integration of sophisticated analytical tools and methodologies enables companies to extract actionable insights from large datasets, driving improvements across order trend analysis, customer behavior understanding, and inventory management. Through the implementation of modern data architectures, cloud computing, and machine learning capabilities, organizations can enhance their operational efficiency while reducing co

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Massmann, Melina, Maurice Meyer, Maximilian Frank, Sebastian von Enzberg, Arno Kühn, and Roman Dumitrescu. "Framework for Data Analytics in Data-Driven Product Planning." Procedia Manufacturing 52 (2020): 350–55. http://dx.doi.org/10.1016/j.promfg.2020.11.058.

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Johnson, Devon S., Laurent Muzellec, Debika Sihi, and Debra Zahay. "The marketing organization’s journey to become data-driven." Journal of Research in Interactive Marketing 13, no. 2 (2019): 162–78. http://dx.doi.org/10.1108/jrim-12-2018-0157.

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Purpose This paper aims to improve understanding of data-driven marketing by examining the experiences of managers implementing big data analytics in the marketing function. Through a series of research questions, this exploratory study seeks to define what big data analytics means in marketing practice. It also seeks to uncover the challenges and identifiable stages of big data analytics implementation. Design/methodology/approach A total of 15 open-ended in-depth interviews were conducted with marketing and analytics executives in a variety of industries in Ireland and the USA. Interview tra

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Researcher. "THE ROLE OF DATA QUALITY IN MODERN ANALYTICS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 174–86. https://doi.org/10.5281/zenodo.13744345.

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This comprehensive article explores the critical role of data quality in modern business analytics and decision-making processes. It examines the multifaceted nature of data quality, including its key dimensions of accuracy, completeness, consistency, timeliness, and reliability. The article delves into the significant impacts of both poor and high-quality data on business operations, customer satisfaction, and regulatory compliance. It also addresses the challenges organizations face in maintaining data quality in an increasingly complex digital landscape and proposes effective strategies for

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Kandi, Anudeep, and Maria Anurag Reddy Basani. "Real-time Analytics on AWS and Google Cloud to Unlock Data Driven Insights." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 302–8. http://dx.doi.org/10.21275/sr241105211600.

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24

Raji, Ibrahim, and Tochukwu Kennedy Njoku. "Data-Driven Decision Making in Agriculture: Enhancing Productivity and Sustainability through Predictive Analytics." International Journal of Research Publication and Reviews 5, no. 9 (2024): 2708–19. http://dx.doi.org/10.55248/gengpi.5.0924.2656.

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Bala, Vignesh Charllo. "Efficient Data Harmonization in Distributed Systems: A Scalable Approach for Multi-Site Analytics." Journal of Scientific and Engineering Research 5, no. 7 (2018): 443–49. https://doi.org/10.5281/zenodo.13752722.

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Data harmonization in distributed systems, particularly within distributed environments, presents considerable challenges due to the heterogeneity and geographical dispersion of data sources. This research introduces a scalable and efficient framework designed to integrate, standardize, and cleanse data across diverse sources, thereby ensuring consistency, accuracy, and reliability. The framework employs a multi-phase process that includes advanced data integration and cleaning techniques, combined with the power of distributed computing and parallel processing to efficiently handle large-scal

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26

Talekar, P. R. "Data-Driven Marketing: Leveraging Data for Targeted Marketing Success." International Journal of Advance and Applied Research 5, no. 10 (2024): 29–31. https://doi.org/10.5281/zenodo.11298367.

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Data-driven marketing gathers and builds strategies based on big data to inform marketing decisions and personalize the customer experience. Now, in today’s digital world businesses have access to extensive amounts of data from various sources including customer interactions, Online behavior demographic information, and market trends harnessing this data through advanced analytics and technology markets can acquire a profound perception of targeted audiences, and create more personalized, relevant, and impactful campaigns. A data-driven media arrangement is now assisted by the massi

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27

Gondane, Mukesh, and Prof. D. G. Ingale. "Enhancing Agricultural Productivity through Big Data Analytics." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 5 (2024): 338–47. https://doi.org/10.5281/zenodo.13998563.

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Big data analytics is revolutionizing the agricultural sector by enabling data-driven decision-making that enhances productivity, sustainability, and profitability. This paper explores the role of big data analytics in agriculture, focusing on its applications in precision farming, crop management, and supply chain optimization. By analyzing case studies and real-world implementations, we demonstrate how data-driven insights lead to improved resource allocation, better pest management, and increased yield. Furthermore, we discuss the challenges associated with the adoption of big data analytic

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Haland, Christoffer, and Anders Granmo. "Machine Learning for Anomaly Detection: Insights into Data-Driven Applications." International journal of data science and machine learning 05, no. 01 (2025): 36–41. https://doi.org/10.55640/ijdsml-05-01-07.

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Anomaly detection plays a pivotal role in data-driven machine learning applications, enabling the identification of rare or unexpected patterns that deviate from the norm. These anomalies, which can indicate critical events such as fraud, security breaches, equipment failures, or medical conditions, are invaluable in a variety of fields. This paper provides an in-depth review of anomaly analytics, focusing on the various techniques used in machine learning to detect anomalies in complex, high-dimensional data. We explore statistical methods, machine learning-based approaches, and hybrid models

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Yusuf Onifade, Abiodun, Jeffrey Chidera Ogeawuchi, and Abraham Ayodeji Abayomi. "Scaling AI-Driven Sales Analytics for Predicting Consumer Behavior and Enhancing Data-Driven Business Decisions." International Journal of Advanced Multidisciplinary Research and Studies 4, no. 6 (2024): 2181–201. https://doi.org/10.62225/2583049x.2025.5.3.4269.

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The rapid advancement of artificial intelligence (AI) has transformed sales analytics, enabling businesses to predict consumer behavior with unprecedented accuracy. AI-driven sales analytics leverages machine learning, natural language processing (NLP), and predictive modeling to extract actionable insights from vast datasets, enhancing decision-making and driving revenue growth. This study explores strategies for scaling AI-driven sales analytics, focusing on key challenges, technological innovations, and the impact on data-driven business decisions. The research highlights how AI-powered mod

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Yusuf Onifade, Abiodun, Jeffrey Chidera Ogeawuchi, and Abraham Ayodeji Abayomi. "Scaling AI-Driven Sales Analytics for Predicting Consumer Behavior and Enhancing Data-Driven Business Decisions." International Journal of Advanced Multidisciplinary Research and Studies 4, no. 6 (2024): 2181–201. https://doi.org/10.62225/2583049x.2024.4.6.4269.

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The rapid advancement of artificial intelligence (AI) has transformed sales analytics, enabling businesses to predict consumer behavior with unprecedented accuracy. AI-driven sales analytics leverages machine learning, natural language processing (NLP), and predictive modeling to extract actionable insights from vast datasets, enhancing decision-making and driving revenue growth. This study explores strategies for scaling AI-driven sales analytics, focusing on key challenges, technological innovations, and the impact on data-driven business decisions. The research highlights how AI-powered mod

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31

Fan, Cheng, Da Yan, Fu Xiao, Ao Li, Jingjing An, and Xuyuan Kang. "Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches." Building Simulation 14, no. 1 (2020): 3–24. http://dx.doi.org/10.1007/s12273-020-0723-1.

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32

Faiyaad, Chisis Mohammed, and Abayomi Bin Hakim Sadiki. "How healthcare industry in Arabs can use data science for sustainable healthcare practices." Business & IT XII, no. 1 (2022): 184–92. http://dx.doi.org/10.14311/bit.2022.01.22.

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To date, the healthcare business hasn't fully understood the prospective bene?ts to be acquired from big data analytics. Although the continuously growing body of academic investigation on large data analytics is mainly technology-oriented, a clear understanding of the strategic implications of big data is urgently needed. To handle the absence, this particular analysis examines the historical development, architectural style, and portion functionalities of big data analytics. From content evaluation of twenty six BDA implementation instances in healthcare, we could determine five big data ana

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Venkata Siva Prasad Maddala. "Data-Driven Manufacturing: Leveraging Analytics for Operational Excellence." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 884–93. https://doi.org/10.32628/cseit25111291.

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This comprehensive article explores the transformative impact of data-driven analytics on modern manufacturing operations, emphasizing its role in enhancing operational excellence and innovation. The article examines five key areas: production intelligence, supply chain optimization, sustainable manufacturing analytics, data-driven innovation, and implementation frameworks. Through detailed analysis, the article demonstrates how advanced analytics capabilities are revolutionizing manufacturing processes by enabling predictive maintenance, optimizing supply chains, promoting sustainable practic

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Olufunke Olawale, Funmilayo Aribidesi Ajayi, Chioma Ann Udeh, and Opeyemi Abayomi Odejide. "LEVERAGING WORKFORCE ANALYTICS FOR SUPPLY CHAIN EFFICIENCY: A REVIEW OF HR DATA-DRIVEN PRACTICES." International Journal of Applied Research in Social Sciences 6, no. 4 (2024): 664–84. http://dx.doi.org/10.51594/ijarss.v6i4.1061.

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This study explores the pivotal role of HR analytics in enhancing supply chain management (SCM) efficiency, marking a significant shift from traditional HR practices to data-driven decision-making. The main objective was to bridge the gap between HR analytics and SCM performance, highlighting the transformative potential of integrating advanced data analysis in workforce management to optimize supply chain operations. Employing a systematic literature review and content analysis methodology, this research meticulously analyzed peer-reviewed articles, conference papers, and scholarly books from

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35

Ahmad, Aakash, Mahdi Fahmideh, Ahmed B. Altamimi, et al. "Software Engineering for IoT-Driven Data Analytics Applications." IEEE Access 9 (2021): 48197–217. http://dx.doi.org/10.1109/access.2021.3065528.

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36

Chiou, Yu-Chiun, and Chih-Wei Hsieh. "Travel pattern analytics driven by cellular signaling data." Asian Transport Studies 7 (2021): 100042. http://dx.doi.org/10.1016/j.eastsj.2021.100042.

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37

Melnykova, Nataliia, Nataliya Shakhovska, Michal Gregus, Volodymyr Melnykov, Mariana Zakharchuk, and Olena Vovk. "Data-Driven Analytics for Personalized Medical Decision Making." Mathematics 8, no. 8 (2020): 1211. http://dx.doi.org/10.3390/math8081211.

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The study was conducted by applying machine learning and data mining methods to treatment personalization. This allows individual patient characteristics to be investigated. The personalization method was built on the clustering method and associative rules. It was suggested to determine the average distance between instances in order to find the optimal performance metrics. The formalization of the medical data preprocessing stage was proposed in order to find personalized solutions based on current standards and pharmaceutical protocols. The patient data model was built using time-dependent

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Gomes, Albert, Nishat Margia Islam, and Md Rashidul Karim. "Data-Driven Environmental Risk Management and Sustainability Analytics." Non human journal 1, no. 01 (2024): 100–113. http://dx.doi.org/10.70008/jmldeds.v1i01.46.

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This paper explores the intersection of data-driven approaches and environmental risk management, emphasizing the critical role of technology in enhancing sustainability. It provides a systematic review of current literature on public-private partnerships, data quality challenges, and innovative methodologies such as machine learning and Internet of Things (IoT) applications for environmental monitoring. Key themes include the integration of interoperable data platforms, the implications of big data on climate change, and the importance of fostering government policies that promote data sharin

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Sun, Yannan, Weituo Hao, Yan Chen, and Bing Liu. "Data-driven occupant-behavior analytics for residential buildings." Energy 206 (September 2020): 118100. http://dx.doi.org/10.1016/j.energy.2020.118100.

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Hossain, Md Afnan, Shahriar Akter, and Venkata Yanamandram. "Revisiting customer analytics capability for data-driven retailing." Journal of Retailing and Consumer Services 56 (September 2020): 102187. http://dx.doi.org/10.1016/j.jretconser.2020.102187.

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41

Boto, Elizeu. "Achieving data-driven efficiencies with integrated planning analytics." APPEA Journal 56, no. 2 (2016): 583. http://dx.doi.org/10.1071/aj15089.

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The logistical challenges of drilling in the Surat and Bowen basins, and the high operational costs allied to an oil price downturn, is forcing the CSG to LNG players in Queensland to reinvent their delivery models. The concept of a drilling factory has been the holy grail of the oil and gas industry for many years, and many local operators have turned their attention to the lessons learned by the North American shale players. These companies, with virtually no increase in capital costs, were able to improve time to drill, wells per rig and total distance drilled by 50–150% in fewer than five

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42

Nalchigar, Soroosh, and Eric Yu. "Business-driven data analytics: A conceptual modeling framework." Data & Knowledge Engineering 117 (September 2018): 359–72. http://dx.doi.org/10.1016/j.datak.2018.04.006.

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43

Zander, Patrik, and Valentin Zieglmeier. "Data Owner Benefit-Driven Design of People Analytics." Proceedings of the ACM on Human-Computer Interaction 7, EICS (2023): 1–38. http://dx.doi.org/10.1145/3593225.

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With increasingly digitalized workplaces, the potential for sophisticated analyses of employee data rises. This increases the relevance of people analytics (PA), which are tools for the behavioral analysis of employees. Despite this potential, the successful usage of PA is hindered by employee concerns. Especially in Europe, where the GDPR or equivalent laws apply, employee consent is required before data can be processed in PA. Therefore, PA can only provide relevant insights if employees are willing to share their data. One potential way of achieving this is the use of appeal strategies. In

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Kharraz, Nezha, and István Szabó. "Cloud-Driven Data Analytics for Growing Plants Indoor." AgriEngineering 7, no. 4 (2025): 101. https://doi.org/10.3390/agriengineering7040101.

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The integration of cloud computing, IoT (Internet of Things), and artificial intelligence (AI) is transforming precision agriculture by enabling real-time monitoring, data analytics, and dynamic control of environmental factors. This study develops a cloud-driven data analytics pipeline for indoor agriculture, using lettuce as a test crop due to its suitability for controlled environments. Built with Apache NiFi (Niagara Files), the pipeline facilitates real-time ingestion, processing, and storage of IoT sensor data measuring light, moisture, and nutrient levels. Machine learning models, inclu

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Singh, Alok. "Optimizing Retail Operations with AI-Driven Data Analytics." British Journal of Marketing Studies 13, no. 3 (2025): 72–84. https://doi.org/10.37745/bjms.2013/vol13n37284.

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This comprehensive article explores the transformative impact of artificial intelligence on retail operations, with a focus on data-driven strategies that optimize business performance. The article examines how AI technologies are revolutionizing inventory management through advanced demand forecasting and stock level optimization while enhancing customer experiences via personalized recommendations and targeted marketing initiatives. By investigating implementation methodologies, technological requirements, and potential challenges, the article provides retail executives and technology leader

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Sá, Jorge Oliveira e., and Maribel Yasmina Santos. "Process-driven data analytics supported by a data warehouse model." International Journal of Business Intelligence and Data Mining 12, no. 4 (2017): 383. http://dx.doi.org/10.1504/ijbidm.2017.086986.

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Oliveira e Sá, Jorge, and Maribel Yasmina Santos. "Process-driven Data Analytics supported by a Data Warehouse Model." International Journal of Business Intelligence and Data Mining 1, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijbidm.2017.10004786.

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Lytras, Miltiadis, Anna Visvizi, Xi Zhang, and Naif Radi Aljohani. "Cognitive computing, Big Data Analytics and data driven industrial marketing." Industrial Marketing Management 90 (October 2020): 663–66. http://dx.doi.org/10.1016/j.indmarman.2020.03.024.

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Welbourne, Theresa M. "Data-Driven Storytelling: The Missing Link in HR Data Analytics." Employment Relations Today 41, no. 4 (2015): 27–33. http://dx.doi.org/10.1002/ert.21471.

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Adeyeye, Oladele J., Ibrahim Akanbi, Isaac Emeteveke, and Oluwatobi Emehin. "Leveraging Secured Ai-Driven Data Analytics For Cybersecurity: Safeguarding Information And Enhancing Threat Detection." International Journal of Research Publication and Reviews 5, no. 10 (2024): 3208–23. http://dx.doi.org/10.55248/gengpi.5.1024.2911.

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