A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions
The explosion of data volume in the digital age has completely changed the corporate and industrial environments. In-depth analysis of large datasets to support strategic decision-making and innovation is the main focus of this paper’s exploration of big data management engineering. A thorough exami...
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| Format: | Article |
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MDPI AG
2024-07-01
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| Online Access: | https://www.mdpi.com/2673-4117/5/3/68 |
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| author | Leonidas Theodorakopoulos Alexandra Theodoropoulou Yannis Stamatiou |
| author_facet | Leonidas Theodorakopoulos Alexandra Theodoropoulou Yannis Stamatiou |
| author_sort | Leonidas Theodorakopoulos |
| collection | DOAJ |
| description | The explosion of data volume in the digital age has completely changed the corporate and industrial environments. In-depth analysis of large datasets to support strategic decision-making and innovation is the main focus of this paper’s exploration of big data management engineering. A thorough examination of the basic elements and approaches necessary for efficient big data use—data collecting, storage, processing, analysis, and visualization—is given in this paper. With real-life case studies from several sectors to complement our exploration of cutting-edge methods in big data management, we present useful applications and results. This document lists the difficulties in handling big data, such as guaranteeing scalability, governance, and data quality. It also describes possible future study paths to deal with these issues and promote ongoing creativity. The results stress the need to combine cutting-edge technology with industry standards to improve decision-making based on data. Through an analysis of approaches such as machine learning, real-time data processing, and predictive analytics, this paper offers insightful information to companies hoping to use big data as a strategic advantage. Lastly, this paper presents real-life use cases in different sectors and discusses future trends such as the utilization of big data by emerging technologies. |
| format | Article |
| id | doaj-art-af171a67be0e4b5399347989261b0ef3 |
| institution | OA Journals |
| issn | 2673-4117 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj-art-af171a67be0e4b5399347989261b0ef32025-08-20T01:55:30ZengMDPI AGEng2673-41172024-07-01531266129710.3390/eng5030068A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research DirectionsLeonidas Theodorakopoulos0Alexandra Theodoropoulou1Yannis Stamatiou2Department of Management Science and Technology, University of Patras, 26334 Patras, GreeceDepartment of Management Science and Technology, University of Patras, 26334 Patras, GreeceDepartment of Business Administration, University of Patras, 26504 Patras, GreeceThe explosion of data volume in the digital age has completely changed the corporate and industrial environments. In-depth analysis of large datasets to support strategic decision-making and innovation is the main focus of this paper’s exploration of big data management engineering. A thorough examination of the basic elements and approaches necessary for efficient big data use—data collecting, storage, processing, analysis, and visualization—is given in this paper. With real-life case studies from several sectors to complement our exploration of cutting-edge methods in big data management, we present useful applications and results. This document lists the difficulties in handling big data, such as guaranteeing scalability, governance, and data quality. It also describes possible future study paths to deal with these issues and promote ongoing creativity. The results stress the need to combine cutting-edge technology with industry standards to improve decision-making based on data. Through an analysis of approaches such as machine learning, real-time data processing, and predictive analytics, this paper offers insightful information to companies hoping to use big data as a strategic advantage. Lastly, this paper presents real-life use cases in different sectors and discusses future trends such as the utilization of big data by emerging technologies.https://www.mdpi.com/2673-4117/5/3/68big data analyticsbig data toolsdecision-makingdata lifecycle managementpredictive analytics |
| spellingShingle | Leonidas Theodorakopoulos Alexandra Theodoropoulou Yannis Stamatiou A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions Eng big data analytics big data tools decision-making data lifecycle management predictive analytics |
| title | A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions |
| title_full | A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions |
| title_fullStr | A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions |
| title_full_unstemmed | A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions |
| title_short | A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions |
| title_sort | state of the art review in big data management engineering real life case studies challenges and future research directions |
| topic | big data analytics big data tools decision-making data lifecycle management predictive analytics |
| url | https://www.mdpi.com/2673-4117/5/3/68 |
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