In the wake of the most recent (and seemingly, continuing) economic crises, there is a lot of conjecturing as to whether increased regulations, increased reporting, and increased scrutiny would have averted some amount of the financial woes. True, the data (or perhaps, absence thereof) indicated that there were bound to be issues at some point;, but despite the benefit of 20/20 hindsight, yet another bubble was allowed to continue to grow and eventually pop. However, this is not an isolated incident, but rather a recurring pattern, manifested in tulipomania in 1600’s Holland, and occurring repeatedly until today. The question is not necessarily of increased scrutiny, but perhaps increased attention to whatever oversight is in place.

This insight informs the data governance process. Data governance is expected to ensure that the data meets the expectations of all the business purposes, in the context of data stewardship, ownership, compliance, privacy, security, data risks, compliance, data sensitivity, metadata management, and MDM. The common denominator is that each of these issues centers on ways that information management is integrated with controls for management oversight along with verification of organizational observance of information policies. In other words, each aspect of data governance relates to the specification of a set of information policies that reflect business needs and expectations, along with the processes for monitoring conformance to those information policies.

In this context, then, the goal of instituting data governance is not the creation of additional oversight. Rather, it is more about the synergy of instituting the appropriate levels of governance, providing the proper training for members of the organization to recognize the scope of their accountability, and then establishing the practices to recognize the boundaries between reasonable risk and folly.