Last week I participated in a roundtable during a conference in Paris organized by the French branch of DAMA – the data management international organization. During the question/answer part of the conference, it became clear most of the audience was confusing data management with data governance. This is a challenge my colleague at Forrester Michele Goetz identified early in the DG tooling space. Because Data Quality and Master Data Management embed governance features, they are viewed by many as data governance tooling. But the reality is that they remain data management tooling – their goal is to improve the data quality by executing rules. This tooling confusion is only a consequence of how much the word governance is misled and misunderstood – and that leads to struggling data governance efforts.
So what is “Governance”? Governance is the collaboration, organization and metrics facilitating a decision path between at least 2 conflicting objectives.. Governance is finding the acceptable balance between the interests of two parties. For example IT governance is needed because you would like to support all possible business projects but you also have limited budget or skills resources availability. Governance is needed when the objectives are different for different stakeholders, and the outcome of governance is they do not get the same priority. If everyone has the same objective then this is management.