What is Data Governance

What is data governance?

There are 10 components under this umbrella:

• Data Quality
• Data Architecture
• Data Modelling & Design
• Data Storage & Operations
• Data Security
• Data Integration & Interoperability
• Documents & Contents
• Reference & Master Data
• Data Warehousing & Business Intelligence
• MetaData

A picture paints a thousand words:

Next, we need these key components to implement data governance.

1. People The data governance professionals, data stewards, and other key business and IT staff are the backbones of a data governance program. They establish and develop workflows to ensure that the enterprise data governance requirements are met.

2. Data strategy The data governance team plays a crucial role in the development and implementation roadmap of an organization’s enterprise data strategy. A data strategy is an executive document that provides high-level enterprise requirements for data and ensures that those requirements are met. Building an enterprise data strategy is a vital step in the organization’s data management journey.

3. Data processes Data governance programs need to establish key data processes for data management. These include data issue tracking or resolution, data quality monitoring, data sharing, data lineage tracking, impact analysis, data quality testing, and many others.

4. Data policies A data policy is a high-level set of one or more statements that state expectations and expected outcomes of data that influence and direct data habits at an enterprise level. Data governance programs establish data governance policies for data management. Policies include outbound data sharing, regulatory adherence, and many others.

5. Data standards & data rules A data standard provides a framework and an approach to ensure adherence to a data policy. An example of a data standard could be using the ISO 3166 standard for the definition of the codes for the names of countries, dependent territories, special areas of geographical interest, and their principal subdivisions. A data rule directs or constrains behavior to ensure adherence to data standards, which provides compliance wi****th data policies. An example of a data rule would be an organization that only allows country codes listed in the ISO 3166 Standard. Typically, organizations will look to establish data rules for master and reference data, data definitions and domain development, metadata management, classification, accessibility, and many others. A data governance program can leverage many data standards. Some of the more notable data standards include:
   International Organization for Standardization (ISO): 3166, 19115, 11179
   Dublin Core: A basic, domain-agnostic, most widely used metadata standard that can be easily understood and implemented.
   Also Read: Top 10 Data Governance Tools for 2021

6. Data security Data security involves protecting digital data, such as those in a database, from destructive forces and unwanted actions of authorized and unauthorized users. These unwanted user activities refer to espionage, cyber-attack, or data breach.

7. Communications Data governance communications include all written, spoken, and electronic interactions with association audiences who need to know about the data governance team’s activities. A communication plan encompasses objectives, goals, and tools for all communications and should be part of a governance program from the very beginning. The plan identifies how to present governance and stewardship challenges and successes to the various stakeholders and the rest of the organization. The communications plan highlights the right business cases and presents their results.

8. Socialization Socialization of data governance is an important activity in any governance program. The data governance socialization plan is a plan that helps integrate data governance activities into an organization’s policies, internal culture, hierarchy, and processes. The plan is unique to the organization as it is tailored to its culture and standards of behavior.

9. Metrics/KPIs Establishing business metrics and key performance indicators (KPIs) for monitoring and measuring the overall business impact of the data governance program is vital to the program’s success. The metrics and KPIs must be measurable, tracked over time, and consistently measured the same way every year.

10. Technology The data governance program needs various technologies that make the process seamless and automated. Smaller data governance programs typically use the technology stack which they already have within their enterprise. Meanwhile, larger data governance purchase software that is specific to data governance and the functions it requires. It simplifies the process of capturing the required metadata, management of the metadata, automating the data stewardship workflows, decision trees, collaboration, and many other data governance functions.