Data Governance Best Practices – the Beginning
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, http://www.ibm.com/news/be/en/2005/06/3003.html)
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, http://web-services.gov/DraftDRMDataManagementStrategy-021604Correct.pdf)
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 encompasses the people, corporate processes and procedures that ensure data value, data quality improvement, development and maintenance of single shared definitions for all data, and availability of the right data at the right time to the right people in the right format.
There can be confusion between the terms data governance with data management. Data management is the management of: data, access points to that data and management of its metadata (or definitional meaning). Data management is part of the role of data governance, but the process of data governance is to exercise control over the data within a corporate alignment. Data, in this context, is any information captured within a computerized system, which can be represented in graphical, text or speech form.
Data governance runs horizontal to the entire enterprise. Data is everywhere, and access to data is generally neither monitored nor measured either within a business unit or across the enterprise. Consistent definitions of the data and how to use it are tasks that are part of the data management process and data stewardship efforts. Establishing individuals to oversee the administration of data processes and integration into the enterprise is data governance, and data management is an effort that is part of the governance operations.
Data governance is not a feature and it should not be optional. True governance is a process by which organizations control the definition, usage, access and security of the information they own and manage. Data governance must include metadata, unstructured data, registries, taxonomies and ontologies, and it contributes heavily to organizational success through repeatable and compliant practices.
There are many things that can be called “Best Practices” for Data Governance. The following list will be the basis for future articles, columns and discussions. This list is not exhaustive or complete, so please feel free to contribute additional topics to explore and discuss.
Clearly defined and communicated vision, objectives, processes, and metrics of the Data Governance Program
- Project(s) Scope
- 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
- Business and technical data stewards assigned for ALL subject areas
- Functioning Data Governance Council
- Data governance training
- Enterprise standards library
- Participation in enterprise architecture reviews
- Defined, approved and published data standards
- Managed metadata environment
- Communities of practice for governance, stewardship, information management, etc.
- Rewards for good data governance behavior
- Success metrics are defined and measured for data governance
As we embark on the journey into the realm of governance and stewardship, remember that each organization’s approach will be unique but all the approaches are based on some common concepts and all governance programs can be improved. This forum should help you learn about governance and discover ways to create, sustain and enhance your governance program.
About the Author
Anne Marie Smith is a leading consultant in Information Management and is a frequent contributor to various IS publications. Anne Marie has over 20 years experience in information management for several corporate entities and has successfully led the development of data resource management departments within corporations and consulting organizations. Anne Marie is active in the local chapter of DAMA and serves on the board of directors of DAMA International, and is an advisor to the DM Forum. She has been an instructor of Management Information Systems (MIS) with Philadelphia, PA area colleges and universities.
Anne Marie has taught topics such as: data stewardship and governance, data warehousing, business requirements gathering and analysis, metadata management and metadata strategy, information systems and data warehouse project management.
Anne Marie’s areas of consulting expertise include metadata management including data stewardship and governance, information systems planning, systems analysis and design, project management, data warehouse systems assessment and development, information systems process improvement and information resource management/data resource management.
Anne Marie holds the degrees Bachelor of Arts and a Master's of Business Administration in Management Information Systems from La Salle University; she has earned a PhD in MIS at Northcentral University. She is a certified logical data and process modeler and holds project management certification. Anne Marie can be reached at email@example.com