Enhancing DataOps practices through innovative collaborative models: A systematic review
The rapidly evolving field of Data Operations (DataOps) is essential for enhancing data management within large-scale enterprises. However, persistent challenges, such as inefficiencies in data integration, delivery, and governance, limit its potential impact. These obstacles hamper the seamless imp...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | International Journal of Information Management Data Insights |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096825000035 |
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| author | Aymen Fannouch Jihane Gharib Youssef Gahi |
| author_facet | Aymen Fannouch Jihane Gharib Youssef Gahi |
| author_sort | Aymen Fannouch |
| collection | DOAJ |
| description | The rapidly evolving field of Data Operations (DataOps) is essential for enhancing data management within large-scale enterprises. However, persistent challenges, such as inefficiencies in data integration, delivery, and governance, limit its potential impact. These obstacles hamper the seamless implementation of DataOps strategies, slowing down operational processes and affecting organizational performance in data-driven environments. To address these issues, this research employs a systematic literature review, analyzing contributions from 2004 to 2024, to identify relevant solutions and innovations. The study highlights the value of frameworks, methodologies, and advanced technologies—such as automation, cloud platforms, and continuous delivery pipelines—that have reshaped the DataOps landscape. These contributions guide enterprises toward best practices in data strategy and foster improved collaboration across business and IT teams. Building on this analysis, our research also proposes a personal framework designed to offer a comprehensive approach to DataOps strategy. This framework integrates key insights from existing research and provides practical recommendations and best practices to streamline workflows, enhance data governance, and align IT operations with business goals. The enhanced DataOps practices derived from our framework demonstrate significant potential to boost operational efficiency, accelerate decision-making processes, and unlock new growth opportunities. Furthermore, the implementation of such practices sets the foundation for future innovations in data management and offers a path forward for organizations seeking sustainable, long-term value. |
| format | Article |
| id | doaj-art-dbdff9945f444ea8b4d1236a87810418 |
| institution | Kabale University |
| issn | 2667-0968 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj-art-dbdff9945f444ea8b4d1236a878104182025-02-04T04:10:40ZengElsevierInternational Journal of Information Management Data Insights2667-09682025-06-0151100321Enhancing DataOps practices through innovative collaborative models: A systematic reviewAymen Fannouch0Jihane Gharib1Youssef Gahi2Corresponding author.; Laboratory of Engineering Science, Ibn Tofail University, Kenitra, MoroccoLaboratory of Engineering Science, Ibn Tofail University, Kenitra, MoroccoLaboratory of Engineering Science, Ibn Tofail University, Kenitra, MoroccoThe rapidly evolving field of Data Operations (DataOps) is essential for enhancing data management within large-scale enterprises. However, persistent challenges, such as inefficiencies in data integration, delivery, and governance, limit its potential impact. These obstacles hamper the seamless implementation of DataOps strategies, slowing down operational processes and affecting organizational performance in data-driven environments. To address these issues, this research employs a systematic literature review, analyzing contributions from 2004 to 2024, to identify relevant solutions and innovations. The study highlights the value of frameworks, methodologies, and advanced technologies—such as automation, cloud platforms, and continuous delivery pipelines—that have reshaped the DataOps landscape. These contributions guide enterprises toward best practices in data strategy and foster improved collaboration across business and IT teams. Building on this analysis, our research also proposes a personal framework designed to offer a comprehensive approach to DataOps strategy. This framework integrates key insights from existing research and provides practical recommendations and best practices to streamline workflows, enhance data governance, and align IT operations with business goals. The enhanced DataOps practices derived from our framework demonstrate significant potential to boost operational efficiency, accelerate decision-making processes, and unlock new growth opportunities. Furthermore, the implementation of such practices sets the foundation for future innovations in data management and offers a path forward for organizations seeking sustainable, long-term value.http://www.sciencedirect.com/science/article/pii/S2667096825000035DataOpsAgile methodologiesDevOpsData IntegrationData GovernanceData Quality |
| spellingShingle | Aymen Fannouch Jihane Gharib Youssef Gahi Enhancing DataOps practices through innovative collaborative models: A systematic review International Journal of Information Management Data Insights DataOps Agile methodologies DevOps Data Integration Data Governance Data Quality |
| title | Enhancing DataOps practices through innovative collaborative models: A systematic review |
| title_full | Enhancing DataOps practices through innovative collaborative models: A systematic review |
| title_fullStr | Enhancing DataOps practices through innovative collaborative models: A systematic review |
| title_full_unstemmed | Enhancing DataOps practices through innovative collaborative models: A systematic review |
| title_short | Enhancing DataOps practices through innovative collaborative models: A systematic review |
| title_sort | enhancing dataops practices through innovative collaborative models a systematic review |
| topic | DataOps Agile methodologies DevOps Data Integration Data Governance Data Quality |
| url | http://www.sciencedirect.com/science/article/pii/S2667096825000035 |
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