The goal of data governance is to enable organizations to manage data as an asset. Data governance provides governance principles, systems, processes, overall framework, and management indicators, supervises data asset management, and guides activities at all levels in the data management process. To achieve the overall goal, a data governance program must include the following aspects.
1. Sustainable development
Governance processes must be attractive. It is not a project as an end point, but an ongoing process. It needs to be made the responsibility of the entire organization. Data governance must change the way data is applied and managed, but it does not mean that the organization needs to make huge updates and disruptions.
Data governance is management change that goes beyond one-off data governance components to implement a sustainable path.
2. Embedded
Data governance is not an add-on management process. Data governance activities need to integrate software development methods, data analysis applications, master data management and risk management.
3. Measurable
Data governance done well has a positive financial impact, but demonstrating this impact requires understanding the starting process and planning measurable improvements.
The basic principles of data governance are as follows:
1. Leadership and strategy
Successful data governance starts with vision and committed leadership. Data strategy guides data management activities and is driven by enterprise business strategy.
2. Business driven
Data governance is a business management program, so IT decisions related to data must be managed just as data-related business activities are managed.
3. Shared responsibilities
In all knowledge areas of data management, business data management specialists and data management professionals share responsibilities.
4. Multi-level
Data governance activities occur at the enterprise level and at the local level, but often at levels in between.
5. Based on the framework
Because governance activities require coordination across organizational functions, an operational framework must be established for data governance projects to define respective responsibilities and work content.
6. Principle-oriented
Guiding principles are the basis for data governance activities, especially data governance strategies. Often, organizations develop systems without formal principles; they are simply trying to solve a specific problem. Sometimes principles can be derived from specific strategies by reverse engineering. However, it is best to work on the articulation of core principles and best practices as part of the strategy.
Data governance is not a one-time activity. Data governance is an ongoing program that ensures an organization remains focused on deriving value from data and mitigating risks associated with data. Responsibility for data governance can be assumed by a virtual organization or an entity with specific responsibilities. Efficient execution can only be achieved by understanding the rules and activities of data governance, which requires the establishment of a well-functioning operational framework.