Data governance

A few roles are key to the practice of data governance. Three roles ensure that standards are created and maintained over time, aiding in data compliance, security, data quality and automation goals.

Chief data officer

Executive sponsors, such as chief data officers, signal the importance of a data governance program to the organization through its prioritization. These individuals are instrumental in the development of a cross-functional council, which usually sources members from various business units to represent the needs and concerns of different disciplines or product portfolios. This committee serves as a forum to communicate new data governance initiatives and assign responsibilities to achieve agreed upon timelines and outcomes.

Data owners

These individuals are responsible for the state of the data. They are usually designated by the type of data that they manage, such as customer or financial data, and their role seeks to maintain data accuracy and usability. Common tasks include troubleshooting data issues, approving data definitions, and providing data recommendations, particularly as it relates to any regulatory requirements.

Data stewards

These individuals are subject matter experts (SMEs) around their data domain, influencing data policies and championing data governance across the organization. Since they can communicate the importance of specific data points for business processes or decisions, they can also impact the structures of database tables to ensure that the right data is surfaced for reporting purposes. Overall, though, data stewardship helps keep stakeholders accountable for their role in maintaining data quality.

Data governance practices have increased in adoption over the years, especially with the growth in digital transformation projects. For data governance initiatives to achieve successful outcomes, they should include a number of components, such as:

Data standards

Data dictionaries, taxonomies and business glossaries should be developed to provide clarity around business and data definitions. This documentation reduces confusion in conversations, particularly ones involving metrics and reporting. It also gives stakeholders visibility into the data architecture, enabling teams to innovate on their own to automate processes for their discipline.

Data processes and organizational structure

Data governance processes provide transparency to end users around how data is processed within an organization. This can be inclusive of data refresh cadences, PII restrictions, regulatory data policies or even something as simple as data access. This type of documentation also supports organizational structure by clarifying the responsibilities of different roles as it pertains to the management and maintenance of data.

Technology

Different data governance tools, such as metadata management platforms, support the processes and standards around data. These tools can store and secure information about the data that an organization manages. This can include documentation on business definitions, data logs, data owners, database information (such as database and tables names, server locations, data types, etc.). It can also feed into self-service data analytics tools, allowing analysts to query and visualize different data sets for reporting or innovation projects.