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Open Access

Research on Data Management Capability Assessment

Tingting Guo1, Leiju Qiu2, Lianchong Zhang3( )
CUFE Business School, Central University of Finance and Economics, Beijing 100081, China
China Center for Internet Economy Research, Central University of Finance and Economics, Beijing 100081, China
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
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Abstract

In the digital economy, data are crucial for enterprise value creation, yet many organizations still lack systematic assessment tools for data management capability. This paper presents a comprehensive review and develops a multidimensional framework encompassing five core dimensions: data strategy and governance, data resources and quality, technology and platform support, data application and value realization, and security and risk management. We further analyze three complementary assessment methodologies—maturity grading, quantitative assessment, and qualitative assessment. Recognizing that existing models primarily address data for internal operations, we introduce a product-oriented data management capability assessment model. To demonstrate its applicability, we conduct a case study on 11 commercial remote sensing satellite companies, constructing a tailored three-level indicator system and employing entropy-weighted TOPSIS for evaluation. Results reveal clear enterprise stratification, with data sustainable development capability and data market capability emerging as the primary differentiators. This paper contributes a theoretically grounded and operationally feasible assessment framework, offering both academic insights and practical guidance for enterprises seeking to enhance their data management capabilities and accelerate digital transformation.

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International Journal of Crowd Science
Pages 166-177

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Cite this article:
Guo T, Qiu L, Zhang L. Research on Data Management Capability Assessment. International Journal of Crowd Science, 2026, 10(3): 166-177. https://doi.org/10.26599/IJCS.2026.9100003

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Received: 15 April 2026
Revised: 08 May 2026
Accepted: 08 May 2026
Published: 10 September 2026
© The author(s) 2026.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).