AIGOV Five‑Star Model for Data Asset Management: Framework, Capabilities, and Enterprise Practices

Introduction

In the era of the Internet of Things, data is generated at unprecedented scale and is recognized as a core asset for governments and enterprises; China’s 2015 "Action Plan for Promoting Big Data Development" emphasizes data as a strategic driver.

Research Status of Data Asset Management

International – Since the late 1980s the concept of data warehousing emerged, followed by data governance in the early 2000s. Key milestones include H. Watson’s work on data‑warehouse governance, the DGI data‑governance framework (2004), DAMA’s DMBOK (2006, updated 2008) with ten functional areas, IBM’s data‑governance process (2010), EDM’s data‑management capability model (2014) and CMMI’s six‑dimensional maturity model (2014).

Domestic – Research and standardisation lagged behind; the China Banking and Insurance Regulatory Commission issued a data‑governance guideline in May 2018, and a national data‑management maturity standard was released simultaneously. NewJ Network introduced the “AIGOV Five‑Star Model” in 2015, refined it through industry exchanges, and formally presented it at the 2018 China Data Asset Management Summit.

Comparison of Existing Models

Many models are either overly simplistic (e.g., the 2003 DGI model) or excessively complex (e.g., DAMA 2006, DMM), making implementation difficult. The AIGOV Five‑Star Model offers a balanced, holistic view focused on the entire data‑asset lifecycle.

AIGOV Five‑Star Model Details

The model defines five management domains and thirteen capability items:

Data Architecture Strategy – organizational structure and policies (1 capability).

Data Integration & Sharing – data acquisition, integration, and shared‑service centre (3 capabilities).

Data Governance – data‑model management, metadata, standards, and quality (4 capabilities).

Data Operations – lifecycle, security, and master‑data management (3 capabilities).

Data Value‑Added Application – analytics, mining, and open services (2 capabilities).

Each domain is described with its purpose, key activities, and expected outcomes.

Value and Significance

The model provides a clear framework for enterprises to assess their current state, design tailored data‑asset strategies, and execute systematic improvements, thereby enhancing data quality, reducing risk, and delivering business value.

Enterprise Practice Cases

A large manufacturing company faced fragmented data resources, data black‑boxing, poor visibility, insufficient security controls, low data quality, and capacity issues. By adopting the AIGOV model, the company established a cross‑functional data‑asset team, re‑engineered data dictionaries and metadata, and deployed a visual data‑asset platform that unified metadata, lifecycle, and security management, resulting in improved data accessibility, governance, and operational efficiency.

Conclusion

The AIGOV Five‑Star Model introduces a new methodology for data‑asset management that is expected to shape the big‑data industry, cultivate talent, and drive further development of data‑management standards and best practices.

References

[1] CMMI Institute. Data Management Maturity Model v1.0, 2014. [2] EDM Council. DCAM version 1.0, 2015. [3] Aiken et al., "Measuring Data Management Practice Maturity", Computer, 2007. [4] Li & Bin, "Data Management Capability Maturity Model", Big Data Research, 2017.