Every organization needs to know and apply proven, best practices in data governance to be able to realize the value of their data and information assets
Governance defines the way we manage, monitor and measure different aspects of an organization. There are governance programs for managing information technology, governance programs for managing people and other tangible resources, and there is data governance.
Dictionaries define governance in the following ways:
n 1: the persons (or committees or departments, etc.) who make up a body for the purpose of administering something;
n 2: the act of governing; exercising authority;
There are many definitions of data governance. Some include:
- Data Governance is the process by which companies govern appropriate access to their critical data, by measuring and mitigating operational and security risks associated with access to data. (Source: IBM Security and Report: Data Governance Council)
- Data governance is the practice of making enterprise-wide decisions regarding an organization’s information holdings. (Source: Data Management Study, Federal Enterprise Architecture Program Management Office)
Therefore, data governance can be seen as building standards and requirements into the collection, identification, storage and usage of the asset called “data”. It is a long-term process and a successful, sustained governance program does not happen overnight. Data governance is a program, not a project, and all effective data governance initiatives are started, managed, and sustained with a permanent view. Just as an organization would not consider their accounting department to be temporary, any organization that wants to be lasting and successful should plan for a permanent data governance program, staffed by data governance professionals.
Data governance encompasses the people, corporate processes, and procedures that ensure that the organization can provide the right data to the right people at the right time for the right purpose in the right format with the right usage controls. Doing this requires that data governance be aligned with metadata management, data quality management, data / information security, data integration, and analytics – all at the enterprise level.
Data Governance vs. Data Management
There can be confusion between the terms data governance with data management. Data management is the collection of components that allow an organization to manage data and information as assets. Sometimes, “data management” is called “enterprise data management,” or “enterprise information management” – but the terms all mean the same thing. Data management has several parts, and data governance is one of them. Data Management and Data Governance are not synonymous – and many challenges and issues can arise when people confuse one term with the other.
Data governance runs horizontal to the entire enterprise; it is an enterprise function. Data is everywhere, and access to data should not be controlled or measured within a business unit; data should be able to be shared across the enterprise, except where security prevents that access.
The companion function to data governance is data stewardship. Data stewardship is the role that implements the policies, standards, practices, and procedures that are developed by the data governance professionals. Data stewardship works with data governance to ensure that data is managed according to proven practices and in accordance with the organization’s requirements, since most data stewards are subject matter experts and not data governance professionals.
The alignment of data governance with metadata management should not be optional. Effective data governance is a process by which organizations control the definition, usage, access and security of the information they own and manage; much of that information is contained in the metadata that explains the raw data. So, data governance must be connected to metadata, with its registries, taxonomies and ontologies, and repositories of the context that defines the content.
Best Practices for Data Governance
There are many things that can be called “Best Practices” for Data Governance. The following list is not exhaustive or complete, but it is representative of many proven applications for data governance at successful organizations.
- Clearly defined and communicated vision, objectives, processes, and metrics of the Data Governance Program
- Program Sponsorship
- Program Charter
- Program Scope
- Scope for Each Project
- Single, enterprise level data governance effort
- Well understood escalation/issue resolution process
- Well defined change management process
- Well defined, actionable roles and responsibilities for all data governance roles
- Data governance processes integrated with existing methodologies
- Use of a proven data governance methodology
- Business and technical data stewards assigned for ALL subject areas
- Functioning Data Governance Council
- Data governance training
- Enterprise standards library
- Participation in enterprise data architecture reviews
- Defined, approved and published data standards
- Managed metadata environment established and sustained
- Communities of practice for data governance, data stewardship, information management, etc.
- Rewards for good data governance behavior
- Success metrics are defined and measured for data governance
Conclusion
As each organization develops, implements, and sustains its data governance program, it will review the list of best practices and adapt it. Do not neglect best practices, since they have become “best practices” through the struggles of those who have gone before you.
