Data governance tends to falter when policies exist, but no one owns the decisions behind them.
Large enterprises, faced with increasing regulation and heightened customer and investor scrutiny, have raised the stakes for enterprise decision-making. Implementing a data governance framework establishes a governance operating model, but few enterprises maintain a team that actually administers the program. Policy implementation is often left to individual departments or handed to IT teams with strong technical skills but limited business and regulatory knowledge, leaving gaps in compliance.
As AI moves from pilot projects into production, companies steadily recognize that data quality and availability are binding constraints, as poorly governed data produces poorly governed AI systems. Shadow AI further complicates governance by shifting decisions -- if they are made at all -- to employees without relevant experience, authority or training.
A framework alone cannot adapt to these dynamics. It's a human problem. The question for senior leadership isn't whether to govern but who governs, how authority is structured and what business value the function delivers.
The purpose of a data governance team isn't merely policy or regulatory compliance. The greater goal is enhancing the organization's capacity to act with confidence using data-informed decisions. Teams that accomplish this operate with both authority and responsibility, guided by an executive sponsor who treats the role as a standing commitment.
Executive sponsorship vs. data governance leadership
Executive sponsorship is often treated as a single milestone: secure it. But that's only the first step. Trying to keep that sponsor engaged is harder and more important.
Data leaders trying to secure sponsorship must determine whether the sponsor will commit time and influence on advancing the governance initiative. The executive sponsor must understand the job is more involved than initially expected. The division of labor between an executive sponsor and a data governance leader is as follows.
Functions and duties in the data governance model
The structure of a governance team depends on the vulnerabilities and risks it manages, not on a generic template. However, a compact set of roles recurs in mature programs.
Measuring the value of governance
Governance rarely moves the needle on company performance directly, so measurement must be indirect but rigorous. Avoiding fines is not meaningful KPI; it is a baseline requirement. Useful metrics fall into three families.
Other measures -- often called "vanity metrics" -- track activity rather than value. Examples include the number of policies authored, council meetings held or training hours delivered.
Program effectiveness also depends on clear workflows. Three elements distinguish programs that work from those that struggle:
- Explicit dispute resolution paths. When business units disagree about data ownership or definitions, someone must be named the decision-maker. Everyone should know who that is in advance.
- Documented decision rights. Record who makes decisions, who is consulted and who is informed. A simple RACI matrix should prevent much of the political friction that governance programs generate. Ensure the RACI matrix is always up to date.
- Regular cadence. Not every organization will have the same meeting rhythm, which should match its business pace. Monthly stewardship forums, quarterly team reviews and annual strategy resets work for most enterprises. But some organizations change more quickly and need a different cadence. Mergers and acquisitions also temporarily change cadence. In any instance, maintain transparent communication regarding decisions.
Donald Farmer is a data strategist with 30+ years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.
