Individuals involved in data governance roles will be responsible for data security, data access control, data rights management, and data resilience. A mixture of a line of business operations and IT specialists is needed to fulfill these roles. The value of data, risk to the project success if no further governance steps are taken, and potential tools for improving data quality are considered.

We previously introduced how ETL / ELT tools and machine learning tools can be used to transform data and improve its quality in Chapter 6, Defining the Data and Analytics Architecture. An agreed-upon definition as to what data means might be accomplished by populating data catalogs and using enterprise metadata management tools or through master data management (MDM) solutions that might be defined as part of the project.

Various individuals take part in data governance projects. Data consumers and analysts in the lines of business use data to make decisions and are usually best suited to judge its quality. Because they understand what the data means, some IT organizations push data stewardship, the management of content and metadata, to the consumers and analysts (and they maintain the data catalogs). IT organizations more often take on some data stewardship roles in building ETL / ELT scripts to cleanse data arriving from data producers, defining data management system structure and driving MDM projects. In organizations where data governance is a best practice, everyone who touches data on an ongoing basis becomes a data owner accountable for its quality.