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  4. Vol 10, Issue 1, 2026

The Dilemmas of Data Quality Governance Based on Data Asset Incorporation and Technological Solutions

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DOI: 10.23977/acss.2026.100108 | Downloads: 5 | Views: 255

Author(s)

Xiaobin Zhang 1

Affiliation(s)

1 Jiangxi Juxing Digital Technology Co., Ltd, Nanchang, 330096, China

Corresponding Author

Xiaobin Zhang

ABSTRACT

With the advent of the big data era, data quality governance has become a key issue in organizational management. Data quality is directly related to decision-making accuracy and operational efficiency. This paper focuses on the core challenges faced in data quality governance during the process of recognizing data assets on the balance sheet. It systematically analyzes issues such as the lack of unified data quality evaluation standards, insufficient full lifecycle management mechanisms, and lagging technical means, and on this basis proposes corresponding technical solutions to address these challenges. Moreover, establishment of unified data quality evaluation system, intelligent quality management solutions and cross-departmental collaboration mechanisms demonstrate innovation and efficient implementation of data governance, thereby solving bottlenecks in current data quality governance. At same time, research results enhance effectiveness of data asset management and support decision-making capabilities.

KEYWORDS

Data Quality Governance; Artificial Intelligence; Blockchain Technology; Data Assets

CITE THIS PAPER

Xiaobin Zhang. The Dilemmas of Data Quality Governance Based on Data Asset Incorporation and Technological Solutions. Advances in Computer, Signals and Systems (2026). Vol. 10, No. 1, 62-68. DOI: http://dx.doi.org/10.23977/acss.2026.100108.

REFERENCES

[1] Wu, Y., Chen, Y., Wu, H., et al. (2026). Research on the construction of a data governance and sharing standard framework for educational large models. Open Education Research, 32(01), 54-66.
[2] Ling, J. (2026). Research on the data governance path in the digital transformation of manufacturing enterprises. Communication World, 33(01), 169-171.
[3] Cui, Y., & Meng, W. (2026). Path analysis of data governance driving accounting information quality improvement from the perspective of business and finance integration. International Business and Accounting, (02), 50-53.
[4] Su, Y. (2026). Research on data governance in universities under the perspective of big data. Cybersecurity and Informationization, (01), 19-20.
[5] Yi, A. (2026). Exploration of the fusion method of quality management and anomaly detection for big data governance. Information Recording Materials, 27(01), 73-75.

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