Enterprise Data Platforms are evolving from centralized storage facilities to all-purpose foundations for connecting business semantics, data products and intelligent applications. The old "post-event cleanup" fails to address cross-source heterogeneity, data-quality drift and accountability gaps. This paper proposes a governance mechanism for lakeware integrated platforms that includes standard objects, quality indicators, rule enforcement, lineage identification and work order closure. Based on literature from the past three years and two publicly available enterprise surveys, the study establishes indicators such as completeness, accuracy and consistency, as well as risk priority models, designs hierarchical standards, data contracts and tiered service agreements. Based on the above results, the main way to enhance the quality of enterprises is not to add standalone verification rules, but to strengthen standards, responsibilities, evidence and improvement actions throughout the operating chain of the platform. This solution will help the group company improve the quality of data, reduce fault handling costs, and enhance the reliability of data for analysis and artificial intelligence.

Yun Lou. Research on Enterprise-level Data Quality Measurement and Standards for Closed-Loop Governance of Lakewarehouse Integrated Data Platform. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 3: 86-94. https://doi.org/10.38007/IJBMET.2026.070308.

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