Dresner Advisory's 4th annual study says traditional, narrow stewardship of a clean customer database is giving way to broader oversight spanning data, analytics, and machine learning.
Enterprise data governance used to mean keeping a clean customer database. The work was narrow, technical, and largely invisible outside the IT department. A new survey from Dresner Advisory Services argues that scope is no longer enough: governance, the firm says, will need to cover analytics outputs and machine learning models as well as the raw data underneath them.
Dresner Advisory, an independent analyst firm based in Nashua, N.H., published the 4th edition of its AI, Data, and Analytics Governance Market Study on June 15, according to a company release distributed via PR Newswire. The annual study tracks end-user deployment, sentiment, and priorities around governance, and the 2026 edition covers governance practices and policies, business use cases, and how organizations support the people and technologies doing the work.
The release frames governance as the oversight and operating framework for information-related decision rights, principles, policies, processes, people, and technologies. In practice, surveyed organizations are most often applying that framework to executive dashboards and key performance indicator reporting, financial planning and analysis, and operational performance monitoring, the release says.
The argument for treating governance as a board-level concern, not just an IT checklist, comes from Dresner analyst Saul Judah. Traditional governance, scoped to isolated domains such as master data, is inadequate as data environments grow more complex, Judah argues, and effective governance will need to be broader and integrated across data, analytics, and AI, per the release.
That framing is consistent with the way enterprise software vendors have been pitching "data fabric" and "AI governance" platforms for the past two years. The release does not disclose sample size, respondent composition, year-over-year deltas, or how "Wisdom of Crowds," the trademarked research framework Dresner uses, weights responses. Buyers looking to compare governance maturity against peers will need the full study, not just the announcement.
The 4th edition positions the shift as a longitudinal read rather than a one-off poll, which makes next year's data point the one to watch: whether organizations that already treat analytics and machine learning as governed assets actually see fewer failed deployments than those that still treat governance as a database problem.