- Define goals and understand benefits
- Analyze current state and delta analysis
- Derive a roadmap
- Convince stakeholders and budget project
- Develop and plan the data governance program
- Implement the data governance program
- Monitor and control
Data governance vs. data management
Data governance is just one part of the overall discipline of data management, though an important one. Where data governance is about the roles, responsibilities, and processes to ensure accountability for and ownership of data assets, DAMA defines data management as an overarching term that describes the processes used to plan, specify, enable, create, acquire, maintain, use, archive, retrieve, control, and purge data.
While data management has become a common term for the discipline, it’s sometimes referred to as data resource management or enterprise information management (EIM). Gartner describes EIM as an integrative discipline to structure, describe, and govern information assets across organizational and technical boundaries to improve efficiency, promote transparency, and enable business insight.
Data governance and gen AI
Older models of data governance may need to adjust in the age of gen AI to account for the automated data pipelines required. Likewise, compliance may become a moving target as regulatory environments evolve. These issues require an end-to-end strategy for data management and data governance that covers every step of the data journey: ingesting, storing, and querying data to analyzing, visualizing, and running AI and ML models.
