Gartner’s Insight on Data Governance Roles and Responsibilities

Updated September 29th, 2024

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A successful data governance program serves many different data stakeholders across all levels of an organization. This success requires work, though: defining the roles and responsibilities you need to fill to design, run, and continuously improve your program.
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Gartner used the insight and experience gained through assisting thousands of clients to create a set of basic data governance roles and responsibilities.

In this article we will define these roles and responsibilities, and how you can use them to define your approach to data governance. We’ll also look at why choosing the right tools to support these roles can be challenging — and how Atlan can help.

Table of contents #

  1. Data governance roles
  2. The challenges with implementing Gartner outlined data governance roles and responsibilities
  3. How Atlan supports Gartner data governance roles and responsibilities
  4. Gartner Data Governance Roles and Responsibilities: Related reads

Data governance roles #

Any data governance program contains a number of components. These include a data governance framework, policies and procedures, and data security and privacy, among others, as well as training and education.

The foundational component, though, is data governance roles and responsibilities: establishing clear lines of authority and spheres of responsibility for tasks such as defining data governance policies, resolving data quality issues, and communicating the status of governance initiatives to leadership.

In addition to publishing multiple reports on the state of data governance and the data governance tools marketplace, Gartner also offers guidance around the roles and responsibilities an organization should implement to support a successful data governance program.

Gartner outlines the following key data governance roles and responsibilities:

Role name

Role description

Data steward

Implements data and analytics governance policies; monitors assets and people; resolves data issues that require manual intervention

Lead data steward

Coordinates stewardship work by establishing processes and standards for other stewards

Governance board member

Participates in a senior and diverse group of leaders to define the assets and policies for the company’s data governance program

Governance sponsor

Serves as the face of data governance initiatives to the C-suite, communicates status of cross-organizational data governance efforts

There are different ways to implement these data governance roles and responsibilities within an organization. These may be roles that an employee fills in addition to their other responsibilities; for example, data engineers or business analysts may represent their community’s interests by serving as members of a governance board. These same roles may also be dedicated positions (e.g., lead data steward) that consume an employee’s full time and attention.

Let’s look at each of these roles in greater detail.

What is a data steward? #


The role of data steward has existed for a while. Their primary role is to act as the guardian of data quality within a defined data domain. Data stewards ensure on a day-to-day basis that the data under their purview is accurate, consistent, and reliable.

With the advent of active data governance, more data quality work is performed via automated tooling. Data stewards monitor these results and use them to identify and optimize approaches for achieving data quality targets. They also monitor data quality metrics to assess how well data producers and consumers are conforming to the organization’s policies. Data stewards serve as the primary point of contact for data quality issues that can’t be resolved through automation.

According to the Gartner’s data governance roles and responsibilities summary, data stewards require a number of skills, including:

  • Intimate knowledge of a company’s key business processes
  • Data analysis techniques and tooling
  • Project management skills
  • High conflict resolution, negotiation, and communication skills

The data steward role is generally not a technical role. However, all data stewards must have at least basic familiarity with the IT landscape — and the data landscape in particular — so they can interface with other, more technical roles.

What is a lead data steward? #


One of the challenges with data governance is establishing consistency, not just within a division, but across the entire company. A lead data steward establishes processes for the other stewards in the organization, coordinating activities and ensuring overall data quality standards are met.

Lead data stewards typically manage the work of other data stewards within one or more data domains, but quite often also serve as data steward over a specific data domain within the organization.

Besides monitoring the state of data across multiple domains, lead data stewards also work to communicate new analytics governance board policies to other stewards. They help stewards interpret data policies and mentor them in a wide range of activities. Leads also communicate all of this information to the governance council, and may even participate in governance board meetings.

What is a data governance board? #


The data governance board (or council or committee) sets data governance policies for the entire organization and consists of a senior and diverse group of business leaders. Governance board members collaborate to define the scope of a data governance program, and policies to address data quality, security and compliance, privacy, retention, and other aspects of maintaining data quality and standards.

Board members assist in the board’s overall mission, participating in regular board meetings to define, document, and communicate new policies to all data stakeholders, such as data stewards and lead data stewards. Some board members may lead subgroups focused on specific areas or new projects.

The key skills for a governance board member include data literacy, strong communication skills, and leadership abilities. Communication skills in particular are critical, since members must communicate new policies to data stewards org-wide. Strong communication and leadership abilities also help board members serve as effective champions and policy advocates within the business.

What is a data governance sponsor? #


Because a data governance program is a company-wide and ongoing effort, it can’t succeed without executive-level support.

A data governance sponsor (or champion) is the face of the data governance initiative at the company’s executive level. They advocate for the critical role of data governance, explaining its business value and how proper governance is essential to data success.

The governance sponsor is responsible for convening the data governance board and setting how it operates. They lead the data governance board meetings and sign off on the benchmarks and metrics that hold the board accountable. They also spearhead communications and messaging to other key executives across the organization.

The primary skill for governance sponsors is leadership. Most often, companies will source this role from an existing operational business role, such as Chief Operating Officer (COO) or Chief Data Officer (CDAO, to ensure that the data governance program fully aligns with the needs of the business.

The challenges with implementing Gartner outlined data governance roles and responsibilities #

Data governance at scale #


The first challenge is the sheer volume of data that a data governance program must oversee.

The amount of data companies generate year over year continues to grow exponentially. The advent of AI — particularly the explosion of Generative AI — only accelerates this trend. At the same time, organizations need access to higher-quality, more diverse data (both structured and unstructured) more than ever before.

This is why data governance can no longer be a second thought. Instead of a passive, reactive effort, companies need to actively seek cutting edge automation to ensure their data is properly cleaned, tested, tagged, and secured. Doing this at scale means utilizing emerging technologies like AI to automate tasks such as enriching metadata and authoring data policies.

Supporting business users #


It is notable that the Gartner data governance roles and responsibilities are not technical in nature. Technical roles such as data engineers, data scientists, and analytics engineers are all important partners in a data governance program. The data steward, leader, and board/sponsor roles, though, are focused on the needs of the business itself.

This is why data governance tools need to serve the needs of the full range of users, both technical and business. The problem is that many tools for data cataloging, metadata management, and data quality management, etc., were built by engineers, for engineers. They were not built to be user-friendly for non-technical users, who are forced to wade through a complex UI and an unwieldy set of options to find the information they need.

It is no surprise that these tools are hard to incorporate into day-to-day data tasks for all users, all across the company. Fortunately, there are now modern and accessible data tools that can adjust to the needs of every user — so they can quickly find and easily access the particular data they require.

How Atlan supports Gartner data governance roles and responsibilities #

Atlan supports an active, personalized approach to data governance, equipping every data team member with the tools to activate governance and turn data into value.

Drive data self-service

Empower data domain owners to create reusable, trusted data products that are easy to find and use. Leverage personas and purposes to deliver a personalized experience tailored to each role on your data team. Define no-code data request and approval workflows and process approvals with a single click.

Embed automation for effortless, AI-powered governance

Atlan AI documents 55% of your data estate with AI-enriched suggestions. Automate data classification at scale with playbooks, and tag data once to propagate classifications across your entire estate.

Connect policies to data

Move beyond documentation—use Atlan’s no-code, AI-assisted policy creation to write and connect policies to data in minutes. Manage exceptions and incidents effortlessly through a simple, self-service UI.

Adapt to edge cases

Seamlessly connect to all in-house data sources with Atlan’s out-of-the-box connectors. Govern your entire data estate, including proprietary systems, with Atlan’s open API architecture.

Learn more about how Atlan can support your entire team no matter their data governance role - contact us today for a demo.

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