New framework links data management, standards, and benefit realization for asset quality

A team of researchers led by Yixuan Zhu has published a preprint on arXiv proposing a systematic framework for evaluating and understanding enterprise data asset quality. The work addresses two key gaps: the lack of standardized evaluation methods and the unclear connection between assessment outcomes and business value. The framework comprises three dimensions: Data Asset Management Capability, Data Quality Standard Conformity, and Data Asset Benefit Realization Capability. The authors derived these dimensions using grounded theory and LDA topic modeling, then validated them with a multi-method approach combining PLS-SEM (for net effects), Necessary Condition Analysis (NCA, for thresholds), and fuzzy-set Qualitative Comparative Analysis (fsQCA, for configurational paths).

The results reveal a clear chain mechanism: management capability strongly influences standard conformity, which in turn drives benefit realization. All three dimensions are necessary conditions for high data asset quality—meaning none can be neglected. Crucially, the study identifies multiple equivalent pathways to success, such as governance-oriented (prioritizing management and standards) and benefit-driven (focusing on value realization first). This configurational insight is actionable: enterprises can choose strategies that fit their current maturity. The 48-page preprint includes 6 figures and 33 tables, offering a rigorous blueprint for data governance teams and market stakeholders.