Enterprise Data Trust Score: A Unified AI Framework for Governance, Lineage, Quality, and Compliance
DOI:
https://doi.org/10.29284/6tsm3010Keywords:
Trust Score, Lineage, Compliance, Blockchain, Governance, Artificial Intelligence.Abstract
Organizations rely on data to make decisions but many organizations are unable to say how much they should rely on their own data. Quality issues with the data, lack of lineage clarity, poor governance, and regulatory compliance lapses diminish data asset confidence within the enterprise. In this paper, the authors suggest a single artificial intelligence system (Enterprise Data Trust Score) as an integration of governance, lineage, quality and compliance into a single measurable score. The model considers trust as a composite measure constructed out of four weighted aspects. Every part is rated through fixed measures and an artificial intelligence layer updates the measurements depending on the sensitivity and use of the data by the business. The paper has given the framework architecture together with a scoring model which depicts a structure of the framework and the scoring pipeline. The findings indicate that consolidated score provides data stewards with a single figure to measure and enhance. The paper ends by discussing the limitations and future work implications.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
