The concept of data governance has come about because traditional methods of ensuring data quality are not living up to the expectations. Some of these traditional methods are Enterprise Resource Planning, Business Intelligence and Analytics, Supply Chain Management and Master data management. Typically this is dealt with by creating a data governing council or board but without well defined policies and procedures this creates a data governance gap. So the gap occurs when predefined policies, procedures and practices are not established prior to the creation of the council.

Avoiding this situation means achieving operational data governance and as earlier stages of data governance mature, we see a transition to effective data governance that focuses on business processes and how they met the objectives of the corporation.

This ensures that the organization can make compliance with data policies pervasive but performance metrics related to the achievement of the business objectives.

Data Governance Gap

This mostly happens when the managers create a formal data governance structure before there is a clear definition of roles and responsibilities which will ensure that the steps will be operationalized. This typically results in fears and confusion as opposed to the desired effects. Some of the roles that we are talking about are “data governance director”, “data owner”, “data governance board member” or “data steward”. It is also characterized by lack of established methods for monitoring accountability for each role’s performance.

The data governance gap becomes a problem because the data errors are looked at from an application or data centric perspective rather than from a Business Process perspective. This is because from the time gap between the formation of the data governance council and the formation of the policies for the processes and this results in very frustrating moments. Over a period of time this frustration grows and the lack of interest starts growing in the system.

Operational Data Governance

To ensure that the right type of controls are put into place when the data metrics do not fall under acceptable range is one part of story. This means that whenever violations with respect to data occur they are flagged to the correct people but that alone is not OK. There needs to a mechanism to ensure that these issues are sorted properly and using the right methods and the solutions.

In other words operational data governance operates within context of business success criteria and it means putting in place the policies and procedures for the cycle of defining, implementing and observing data controls related to potential information issues.

This implies that a life cycle is imposed on the definition and implementation of data policies and this cycle encompasses the policy definition, implementation, compliance enforcement and maintainance.