Enterprise Data Asset Quality: A Management-Standard Conformity-Benefit Realization Framework and Formation Mechanisms
For enterprises and data governance practitioners, this provides a systematic framework and empirical evidence linking data management, quality standards, and value realization, though the findings are domain-specific to enterprise data assets.
This study develops a three-dimensional framework for enterprise data asset quality and uses PLS-SEM, NCA, and fsQCA to show that Data Asset Management Capability has the strongest effect on Data Quality Standard Conformity, which in turn promotes Data Asset Benefit Realization Capability, forming a chain mechanism. Multiple configurational paths for achieving high data asset quality are identified.
Motivated by the limited standardization of enterprise data asset quality evaluation and the unclear relationship between assessment outcomes and value realization, this study develops a three-dimensional framework comprising Data Asset Management Capability, Data Quality Standard Conformity, and Data Asset Benefit Realization Capability, based on grounded theory and LDA topic modeling. To examine the formation mechanisms of data asset quality, this study adopts a multi-method approach combining PLS-SEM, Necessary Condition Analysis (NCA), and fuzzy-set Qualitative Comparative Analysis (fsQCA), to capture net effects, capability thresholds, and configurational paths. The results show that significant positive relationships exist among the three dimensions, with Data Asset Management Capability exerting the strongest effect on Data Quality Standard Conformity and further promoting Data Asset Benefit Realization Capability, forming a chain mechanism of management foundation-standard enhancement-value realization. In addition, all three dimensions constitute critical necessary conditions for achieving high data asset quality, and multiple equivalent configurational paths reflecting different combinations of Management, Standard, and Benefit are identified, such as governance-oriented and benefit-driven mechanisms. This study integrates structural (PLS-SEM), necessary-condition (NCA), and configurational (fsQCA) analyses within a unified framework, providing a systematic approach to understanding data asset quality formation and offering practical insights for enterprise data governance and data factor market development.