The Components of Data Governance

Editor’s Note: This post was originally published in May 2023. We’ve updated the content to reflect the latest information and best practices so you can stay up to date with the most relevant insights on the topic.

Data governance isn’t new. But it is certainly getting its time in the spotlight. As chief data officers (CDOs) explore ways to drive greater value from enterprise data, that spotlight is only going to become brighter. 

Today, data governance is changing. With more organizations embarking on a journey to deliver trustworthy data for analytical and operational use cases, data leaders are refocusing their governance and master data strategies. The emphasis is now on the needs and usage patterns of data consumers, rather than solely on the oversight and management of data sources. 

But first things first. What is data governance?

Data Governance, Defined

Data governance is a data management practice that establishes a foundation of policies, processes, roles, and standards—defining who should access and use which data, when, under what circumstances, and using what methods. It builds trust and drives consistency in the data, helping organizations to avoid “data brawls” from erupting when two departments present conflicting insights or results. 

Data governance also fosters accountability by making sure everyone across the organization understands the role they play in ensuring enterprise data remains accurate, secure, and compliant. 

The Roles of a Data Governance Team

Most data governance teams include the following critical roles. However, depending on the size of the team, individuals may cover one or more of these responsibilities. 

• The chief data officer (CDO), who often acts as the executive sponsor of data governance initiatives, setting the vision, strategy, and policies for enterprise-wide data management. The CDO also ensures alignment between governance initiatives and business objectives.
• A data admin, who is responsible for leading data governance initiatives. This individual has a good understanding of both the technical and the business sides of the organization.
• A data steward, who is very knowledgeable about the business and serves as the bridge to translate business needs into requirements that IT can implement. Data stewards also curate the data and help to ensure its quality, consistency, and reliability. 
• A data custodian, who is an expert in managing the storage and security of data. Data custodians also manage data usage, serving in the role of data engineer. 
• A data user, which is exactly as it sounds: any individual in the organization who uses data. This individual could be the CMO, a sales executive, a finance director, or another stakeholder within the organization who uses data to make decisions. 

In addition to these traditional roles, many organizations are adding data owners and data product managers to the team as well.

• A business data owner is a line-of-business leader within a specific domain such as sales, finance, HR, or marketing. Business data owners determine how data within their domain is defined, accessed, and used. They also manage requests for access so that the data within their domain remains in compliance with information access policies set by the business. 
• A data product manager designs, builds, and manages the cross-functional development of a data platform, or a suite of specific data tools, in order to serve multiple internal and/or external data consumers. 
• Finally, many organizations assign a data architect to work on the data governance team. A data architect designs and implements the data architecture, including data models, data integration, and data warehousing. 

Components of Good Data Governance

Solid data governance is built on a framework of people, processes, and technology that ensures data is accurate, secure, compliant, and valuable to the business. There are a number of components to consider:

First, good data governance includes a comprehensive data catalog that makes it easy for users to find important information, improving discoverability and understanding of data assets.

Second, it actively maintains data quality and integrity through stewardship, monitoring, and remediation processes.

Next, good data governance considers consumption, not just sources. Providing users with the right tools helps ensure they use the organization's data appropriately and within established access policies—and avoid getting in trouble by using the wrong data. 

Finally, it is supported by the right technology. And that includes using solutions that are not just AI-enhanced, but ones that are AI-native. These technologies should be part of a broader master data strategy that aligns governance goals with the creation, maintenance, and use of accurate, trusted master data across the enterprise.

The Role of AI in Data Governance

When organizations use AI for data governance, they enhance speed, expand scale, and deliver more intelligent insights, allowing them to handle data faster—with greater precision and more security. Using AI, they can:

• Automate routine and complex tasks 
• Improve data quality
• Maintain regulatory compliance 
• Strengthen data stewardship 

Using solutions such as AI-native master data management (MDM) extends the value of AI in data governance even further by delivering the data quality and transparency needed to turn insights into a strategic advantage. AI-native MDM strengthens data governance through the delivery of accurate, trustworthy master data that makes it easy to conform with organization’s data policies, maintain sufficient data quality, reduce ambiguity, and support better traceability.

Start with the Right Data

Tamr’s AI-native MDM solution supports data governance by delivering clean, consistent, explainable data. Tamr simplifies data governance by enabling organizations to create a strong foundation for policy, compliance, and effective stewardship that allows them to deliver the right data into the right hands, every time.