T/GDICA 005-2026 Active Group standards

T/GDICA 005-2026 Data quality assessment indicator system and methodology specification

Publish Date: 2026-03-16 Implement Date: 2026-03-17 For services related to genuine standard inquiry, procurement, translation, and other related services in China, please Contact Us

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Basic Information

Standard Code: T/GDICA 005-2026

Standard Type: Group standards

Standard Status: Active

is_force_gb: no

CCS Name: -

ICS Name: The application of information technology in transportation and trade

Publish Date: 2026-03-16

Implement Date: 2026-03-17

Scope

Data, as a new type of production factor, has become the core engine driving digital transformation and promoting the high-quality development of the digital economy. Guangdong Province is currently in a critical period of comprehensively advancing the construction of a digital Guangdong and building a modern digital economy system. Improving data quality and ensuring the reliability, availability, and effectiveness of data during its collection, governance, sharing, and application are of crucial importance for unlocking the value of data elements, enhancing the efficiency of government governance, and promoting the intelligent upgrading of industries. Currently, data quality assessment still faces problems such as inconsistent dimensions, incomplete indicators, incomparable methods, and difficult application of results in practice. There is an urgent need to establish a set of scientific, systematic, and actionable standards and norms to provide clear guidance for the full life cycle management of data quality. This standard aims to respond to the above needs by constructing an assessment indicator system covering three dimensions: explanatory documents, data quality, and model application, and clarifying the complete methodology from objective assessment to comprehensive evaluation, providing a unified "benchmark" for measuring the quality of various data resources. The implementation of this standard will help: - Unify quality awareness: Establish a common quality language and evaluation benchmark for data providers, users, and managers. - Standardize assessment processes: Guide organizations to systematically carry out data quality detection, analysis, and evaluation work. - Guide quality improvement: Provide direction for the continuous optimization and precise governance of data resources through graded evaluation results. - Promote data circulation: Enhance data trust and promote the compliant and efficient circulation and value release of high-quality dat