Relevant bibliographies by topics / Data-Driven Analytics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data-Driven Analytics'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Data-Driven Analytics"

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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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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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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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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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Dissertations / Theses on the topic "Data-Driven Analytics"

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Hahmann, Martin. "Feedback-Driven Data Clustering." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-135647.

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The acquisition of data and its analysis has become a common yet critical task in many areas of modern economy and research. Unfortunately, the ever-increasing scale of datasets has long outgrown the capacities and abilities humans can muster to extract information from them and gain new knowledge. For this reason, research areas like data mining and knowledge discovery steadily gain importance. The algorithms they provide for the extraction of knowledge are mandatory prerequisites that enable people to analyze large amounts of information. Among the approaches offered by these areas, clusteri
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Uichanco, Joline Ann Villaranda. "Data-driven optimization and analytics for operations management applications." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85695.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 163-166).<br>In this thesis, we study data-driven decision making in operation management contexts, with a focus on both theoretical and practical aspects. The first part of the thesis analyzes the well-known newsvendor model bu
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Plevoets, Christina, and Rodrigo Fernandes. "Exploring the role of Big Data and Analytics : Creating Data-Driven Innovation." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13463.

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Purpose: The purpose of this thesis is to understand the role of big data in the innovation process and specify the conditions or scenarios where big data and innovation can join forces. Research design, approach and method: The theoretical framework used is based on the innovation framework with big data from Kusiak (2015) and it is validated by three propositions: 1) Big data analytics (BDA) gives the possibility to discover new insights, such as increase profitability, expansion of customer base and market growth, hence it is a contributor to innovation. 2) Firms are investing or plan to in
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Alam, Joy Alam. "Building business analytics capabilities to become data-driven energy retailers." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223723.

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Carlsson, Ståbi Jenny. "Data driven marketing : How to gain relevant insights through Google Analytics." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254988.

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In this report, problems regarding the retrieving, measuring, and analysis of data when analysing marketing effects in the web analytics tool Google Analytics will be discussed. A correct setup, configuration, maintenance, campaign tracking and the understanding of the data in Google Analytics is essential to be able to achieve relevant insights. This is important since many Swedish marketing departments experience issues related to their setup of Google Analytics as well as the ongoing configuration and maintenance. A literature study has been conducted to gather information, focusing on coll
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Sim, Jisoo. "Data-Driven Park Planning: Comparative Study of Survey with Social Media Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97978.

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The purpose of this study was (1) to identify visitors’ behaviors in and perceptions of linear parks, (2) to identify social media users’ behaviors in and perceptions of linear parks, and (3) to compare small data with big data. This chapter discusses the main findings and their implications for practitioners such as landscape architects and urban planners. It has three sections. The first addresses the main findings in the order of the research questions at the center of the study. The second describes implications and recommendations for practitioners. The final section discusses the limitat
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Liu, Dapeng. "Towards developing a goal-driven data integration framework for counter-terrorism analytics." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5986.

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Terrorist attacks can cause massive casualties and severe property damage, resulting in terrorism crises surging across the world; accordingly, counter-terrorism analytics that take advantage of big data have been attracting increasing attention. The knowledge and clues essential for analyzing terrorist activities are often spread across heterogeneous data sources, which calls for an effective data integration solution. In this study, employing the goal definition template in the Goal-Question-Metric approach, we design and implement an automated goal-driven data integration framework for coun
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Flatt, Taylor. "CrowdCloud: Combining Crowdsourcing with Cloud Computing for SLO Driven Big Data Analysis." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2234.

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The evolution of structured data from simple rows and columns on a spreadsheet to more complex unstructured data such as tweets, videos, voice, and others, has resulted in a need for more adaptive analytical platforms. It is estimated that upwards of 80% of data on the Internet today is unstructured. There is a drastic need for crowdsourcing platforms to perform better in the wake of the tsunami of data. We investigated the employment of a monitoring service which would allow the system take corrective action in the event the results were trending in away from meeting the accuracy, budget, and
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Arkstål, Emil. "Interactive Analytics and Visualization for Data Driven Calculation of Individualized COPD Risk." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151925.

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Chronic obstructive pulmonary disease (COPD) is a high mortality disease, second to stroke and ischemic heart disease. This non-curable disease progressively exacerbates, leading to high personal and societal economic impact, reduced quality of life and often death. General treatment plans for COPD risk mistreating the individuals’ condition. To be effective, the treatment should be individualized following the practices of precision medicine. The aim of this thesis was to develop a data driven algorithm and system with visualization to assess individual COPD risk. With MRI body composition pr
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Evans, Ben Richard. "Data-driven prediction of saltmarsh morphodynamics." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/276823.

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Saltmarshes provide a diverse range of ecosystem services and are protected under a number of international designations. Nevertheless they are generally declining in extent in the United Kingdom and North West Europe. The drivers of this decline are complex and poorly understood. When considering mitigation and management for future ecosystem service provision it will be important to understand why, where, and to what extent decline is likely to occur. Few studies have attempted to forecast saltmarsh morphodynamics at a system level over decadal time scales. There is no synthesis of existing
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Books on the topic "Data-Driven Analytics"

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Sharma, Meghna, Priyanka Vashisht, A. V. Senthil Kumar, Chitra Singh, and Abdelmalek Amine. Data-Driven Analytics for Healthcare. Apple Academic Press, 2024. http://dx.doi.org/10.1201/9781003558743.

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Gandhi, Parul, Surbhi Bhatia, and Kapal Dev. Data Driven Decision Making Using Analytics. CRC Press, 2021. http://dx.doi.org/10.1201/9781003199403.

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Zhong, Shaopeng, and Daniel Sun. Logic-Driven Traffic Big Data Analytics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8016-8.

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Zhang, Xingxing, ed. Data-driven Analytics for Sustainable Buildings and Cities. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2778-1.

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Chakraborty, Chinmay, Jerry Chun-Wei Lin, and Mamoun Alazab, eds. Data-Driven Mining, Learning and Analytics for Secured Smart Cities. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72139-8.

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Khang, Alex, Rashmi Gujrati, Hayri Uygun, R. K. Tailor, and Sanjaya Gaur. Data-Driven Modelling and Predictive Analytics in Business and Finance. Auerbach Publications, 2024. http://dx.doi.org/10.1201/9781032618845.

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K, Hemachandran, Debdutta Choudhury, Raul Villamarin Rodriguez, Jorge A. Wise, and Revathi T. Predictive Analytics and Generative AI for Data-Driven Marketing Strategies. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003472544.

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Shale Analytics: Data-Driven Analytics in Unconventional Resources. Springer, 2017.

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Mohaghegh, Shahab D. Shale Analytics: Data-Driven Analytics in Unconventional Resources. Springer, 2017.

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Mohaghegh, Shahab D. Shale Analytics: Data-Driven Analytics in Unconventional Resources. Springer International Publishing AG, 2018.

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Book chapters on the topic "Data-Driven Analytics"

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Rackley, Jerry. "Becoming Data Driven." In Marketing Analytics Roadmap. Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-0259-3_9.

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Mandl, Christian. "Data-Driven Supplier Management." In Procurement Analytics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43281-1_4.

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Mandl, Christian. "Data-Driven Inventory Management." In Procurement Analytics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43281-1_5.

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Mandl, Christian. "Data-Driven Risk Management." In Procurement Analytics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43281-1_6.

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Mandl, Christian. "Data-Driven Spend Management." In Procurement Analytics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43281-1_3.

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Jansen, Bernard J., Joni Salminen, Soon-gyo Jung, and Kathleen Guan. "Data-Driven Personas as Interfaces for Persona Analytics System." In Data-Driven Personas. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02231-9_5.

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Beulen, Erik, and Marla A. Dans. "Data-driven decision-making." In Data Analytics and Digital Transformation. Routledge, 2023. http://dx.doi.org/10.4324/9781003246770-5.

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Saleem, Saima, and Monica Mehrotra. "Data Analytics and Mining." In Data Driven Decision Making Using Analytics. CRC Press, 2021. http://dx.doi.org/10.1201/9781003199403-5.

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Morabito, Vincenzo. "Big Data Driven Business Models." In Big Data and Analytics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10665-6_4.

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Nagarathinam, Aishwarya, Aarthy Chellasamy, and Sangeetha Rangasamy. "Strategic Data Analytics for Sustainable Competitive Advantage." In Data-Driven Decision Making. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2902-9_4.

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Conference papers on the topic "Data-Driven Analytics"

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M.Sadeghi, Hassan, Halil Erhan, and Ahmed M.Abuzuraiq. "Sustainability Data Analytics an Integrated Data-Informed Approach for Assessing the Sustainability of Design Alternatives." In eCAADe 2024: Data-Driven Intelligence. eCAADe, 2024. http://dx.doi.org/10.52842/conf.ecaade.2024.1.725.

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Mottaghi, Esmaeil, Halil Erhan, AhmedM Abuzuraiq, et al. "Integrating Surrogate Modeling and Design Analytics for Data-informed Exploration in the Early Phases of Building Design." In eCAADe 2024: Data-Driven Intelligence. eCAADe, 2024. http://dx.doi.org/10.52842/conf.ecaade.2024.2.301.

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Bansu, Shilpali, and Sangita Chaudhari. "Ontology-Driven Integration and Visual Analytics for Geospatial Data." In 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS). IEEE, 2024. https://doi.org/10.1109/ingarss61818.2024.10984104.

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Jafri, Muhammad Jawad, Fawad Salam Khan, Sajjad Ahmed, and Furqan Amjad. "Patient Data-Driven Scratch Intensity Classification with LSTM." In 2024 2nd International Conference on Computing and Data Analytics (ICCDA). IEEE, 2024. https://doi.org/10.1109/iccda64887.2024.10867368.

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Wang, Tengyun, Jibing Wu, Kaiming Xiao, et al. "Enhancing Tabular Data Generation through Data and Knowledge Dual-Driven Approaches." In 2024 10th International Conference on Big Data and Information Analytics (BigDIA). IEEE, 2024. https://doi.org/10.1109/bigdia63733.2024.10808817.

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Purwar, Mitali, Upasana Deka, Himadri Raj, and Ritu. "Data-Driven Insights: Leveraging Analytics for Predictive Modeling in Finance." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10841301.

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Rakshitha, Priya S, Asmita Namjoshi, Mahesh Mane, Irshadullah Asim Mohammed, and Rashmitha Shettigar. "Data Analytics for Decision Support Systems in Techno-Driven Enterprises." In 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT). IEEE, 2025. https://doi.org/10.1109/ce2ct64011.2025.10941224.

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Jeyaprabha, B., SR Rameshk Kumar, Ramya Lakshmi Bolla, Alka Singh Bhatt, Robin Joseph Sera, and Kajol Arora. "Data-Driven Decision Making in Management: Leveraging Big Data Analytics for Strategic Planning." In 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT). IEEE, 2025. https://doi.org/10.1109/ce2ct64011.2025.10939548.

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Tu, Qiang, Sihao Choy, Zhenfeng Liu, Huaijun Li, Danyang Zhao, and Zhenchao Tao. "Causal Insights into Cervical Cancer: A Data-Driven Approach." In 2024 10th International Conference on Big Data and Information Analytics (BigDIA). IEEE, 2024. https://doi.org/10.1109/bigdia63733.2024.10808697.

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Singh Miglani, Navjot, and Nilesh Arora. "Optimizing Sustainable Human Resource Management through AI-Driven Predictive Analytics and Data-Driven Decision Models." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011407.

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Reports on the topic "Data-Driven Analytics"

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Gurram, Abhishek. Linking Data Analytics for a Better Business Decision Making Process: A Data Driven Approach. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-959.

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Prasanna, Viktor, Rajgopal Kannan, Sanmukh Kuppannagari, et al. DEEP Solar: Data DrivEn Modeling and Analytics for Enhanced System Layer ImPlementation. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1603790.

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Kadeethum, Teeratorn, John Jakeman, Hongkyu Yoon, and Birendra Jha. Continuous integration data-driven platform of industrial-scale subsurface storage for real-time analytics. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2480191.

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Danni, Chen. Data-Driven Advanced Analytics for Enhanced Inventory Management and Demand Forecasting in Manufacturing and Retail Sectors. Iowa State University, 2023. http://dx.doi.org/10.31274/cc-20240624-483.

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Wetzel, Deborah, Fátima Cartaxo, Ricardo Poppi, and Mariano Lafuente. Data-driven Digital Innovation to Strengthen Integrity: TCU’s Payroll Oversight in Brazil. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004549.

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This technical note documents the Continuous Payroll Audit Initiative within Brazils Court of Accounts (Tribunal de Contas da Unio, or TCU), presents its lessons learned, and makes recommendations that could improve its results. This initiative uses digital tools such as data analytics to proactively identify and address incorrect and/or illicit payments in the federal civil service and pension payroll. As of 2021, the initiative had led to savings close to $R 2 billion (US$515 million). The study demonstrates that a small team using the right technology can be very effective in rooting out ir
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Ajjarapu, Venkataramana, Amarsagar Matavalam, Alok Kumar Bharati, et al. Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1785126.

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LaRaine Ingram, Keisha. Applied Sales Predictive Analytics for Business Development. Vilnius Business College, 2024. http://dx.doi.org/10.57005/ab.2024.1.2.

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In the dynamic business environment, leveraging predictive analytics for sales optimization and business development has become crucial for achieving sustained growth. As the e-commerce landscape continues to evolve, many e-businesses must harness the power of predictive analytics to anticipate sales trends and optimize business development strategies. This paper explores the application of sales predictive analytics, focusing on its role in forecasting sales, optimizing resource allocation, and enhancing customer relationship management. The application of predictive analytics in sales foreca
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Pasupuleti, Murali Krishna. AI-Driven Marketing Innovations: Personalization and Ethics in the Digital Era. National Education Services, 2025. https://doi.org/10.62311/nesx/rr625.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on digital marketing, focusing on strategies for delivering personalized content and ensuring ethical advertising. By leveraging AI, marketers can now analyze consumer behavior with precision, enabling targeted content, automated ad placement, and real-time adjustments that enhance user engagement and conversions. The Article examines foundational AI techniques, such as recommendation engines, predictive analytics, and natural language processing, which drive personalization at scale. Additionally, it add
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Ansari, A., S. Mohaghegh, M. Shahnam, J. F. Dietiker, and T. Li. Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1431303.

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Ansari, A., S. Mohaghegh, M. Shahnam, J. F. Dietiker, T. Li, and A. Gel. Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Part Three: Model Building at the Layer Level. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1463895.

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