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| Main Authors: | , |
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| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2024
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| Online Access: | https://doi.org/10.5281/zenodo.14982979 |
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Table of Contents:
- <p>In the era of big data, the volume, variety, and velocity of data generation has significant challenges for<br>effective governance. Traditional approaches often are dependent on the direct governance of original<br>datasets, which can lead to inefficiencies, data privacy concerns, and scalability issues. This paper<br>presents the potential of metadata in addressing these challenges, positioning it as crucial asset for<br>enhancing data governance strategies. By integrating metadata descriptive, structural and administrative<br>data organizations can streamline processes such as data access, security, compliance, and lifecycle<br>management without directly interacting with the raw data. This metadata-driven approach ensures<br>better scalability, preserves data privacy, and enhances overall data management efficiency. The study<br>investigates use case related to credit card fraud detection using various machine and deep learning<br>algorithms, highlighting how metadata governance frameworks can offer a more sustainable solution for<br>managing the complexities of big data. </p>