How to Build Effective Data Standards for Enterprise Governance

1. Overview of Data Standardization

At the 2024 National Data Work Conference, the Director of the National Data Administration announced plans to establish a national data standardization system, create a technical committee, and accelerate the development of national and industry data standards.

Data work is positioned as an innovative driver that will boost research capacity, accelerate the construction of data discipline systems and talent teams, and promote digital‑economy industry‑university‑research collaborations.

2. What Is a Data Standard?

A data standard is a set of normative constraints defined by an organization to ensure consistency and accuracy when data is used or exchanged internally and externally.

Data standard management combines policies, processes, and technical tools to apply unified definitions, classifications, record formats, transformations, and coding, thereby achieving data integrity, validity, consistency, openness, and sharing.

3. Types of Data Standards

Data standard management targets three major categories:

Model data : abstracted descriptions of basic business information.

Master and reference data : core business entities (customers, products, etc.) and fixed reference values.

Indicator data : statistical data derived from one or more basic data items.

Each category can be built using data elements as the basic unit of the standard.

Examples include a bank's model data standard schema, a telecom operator's DWD model data element definitions, and a bank's indicator data standard framework for the "Provision Coverage Ratio" metric.

4. Implementation Process and Methods

Data standardization involves not only creating and publishing standards but also ensuring they are effectively applied. A scientific workflow and appropriate technical tools are essential for successful implementation.

The implementation is divided into four stages:

Data inventory and assessment : conduct surveys, collect requirements, inventory existing data, and set baselines and goals.

Establish assurance mechanisms : build organizational structures, define responsibilities, create policies, train talent, and promote a data‑driven culture.

Data standardization execution : develop technical platforms, apply standards to key domains, and ensure each target is realized.

Data value realization : leverage standardized data to drive governance, improve data quality, and unlock business value.

5. How to Apply Data Standards

Three key questions must be answered before standards can be applied:

Which data need which standards?

Which systems should adopt which standards?

Who is responsible and when should the standards be enforced?

Without clear answers, standardization efforts remain at the spreadsheet level and fail to permeate system designs.

6. Case Study: Bank Data Standard Framework

A domestic bank built a comprehensive data standard framework covering over 600 basic data standards across eight themes (public information, products, contracts, assets, events, channels, participants, finance) and more than 200 code standards. The implementation improved regulatory reporting quality, reduced IT data‑quality tickets, and increased data retrieval efficiency.

Source: Data Academy