IBM defined data governance “as a discipline of quality control to add new rigor and discipline to the process of managing, using, improving and protecting organizational information”. (IBM, Data Governance) Recently, de EU splits this concept and defines firstly “data governance that entails defining, implementing and monitoring strategies, policies and shared decision-making over the management and use of data assets” and secondly “data policies are a set of broad, high level principles which form the guiding framework in which data assets can be managed.” More specifically, both concepts “aim to provide guidance, assurance and support to transform the data-driven organization by defining clear roles and responsibilities; and introducing common principles, guidance and working practices that provide the foundation for harmonized and coordinated data management across the organization.”
To this end, it is defined 3 organizational hierarchical levels, namely:
- Implementation of data policies and accountable for local decisions about data
- Whenever necessary, issues are escalated to the managerial level for resolution.
- Responsible for developing and implementing data policies at corporate level and local level.
- It monitors progress, reports to the strategic level and refers to them any issues and matters that are beyond its decision-making power or mandate.
- Defines the long-term vision, gives direction, oversees progress, takes strategic decisions, and acts as the highest point of reference for issues and matters related to data governance and data policies.
Overall, data governance enables a greater degree of transparency, auditability, and accountability of the organization's data assets. Therefore, a strategy for a data governance program should cover not only the organizational, data management priorities, but also aspects as legislation and regulatory compliance and data culture and structures within the organization.
The principles for implementing data governance strategy are listed below: