Data Stewardship: Definition, Benefits & Key Roles Explained (2025)

Updated December 04th, 2024

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Data stewardship refers to the process of managing and overseeing an organization’s data assets to ensure data quality, privacy, and compliance.

Data stewardship ensures the proper management and governance of data assets within organizations. It assigns clear accountability, enforces compliance, and enhances data quality through established practices.
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By implementing stewardship, businesses can secure data privacy, meet regulatory standards, and promote transparency.

Effective data stewardship fosters collaboration, optimizes the data lifecycle, and supports ethical data usage.

Data stewardship is a vital (if not misunderstood) discipline that promotes effective data management in the enterprise organization. Here’s everything you need to know about what data stewardship is, why it’s important, and how it relates to data governance.

Table of contents #

Data stewardship definition
Why is data stewardship important?
What are the benefits of data stewardship?
Data stewardship and data governance
Who are data stewards and how do they enforce data governance?
How are data stewards chosen?
What’s next for data governance and data stewardship?
How organizations making the most out of their data using Atlan
How Atlan helps in data stewardship and governance
FAQs about Data Stewardship
Data stewardship: Related reads

Data stewardship definition #

Data stewardship is a collection of data management functions that ensure an organization’s business users have access to trustworthy, high-quality data. The DAMA Dictionary of Data Management states, “Data Stewardship is the most common label to describe accountability and responsibility for data and processes that ensure effective control and use of data assets.”

Put another way by the University of Michigan in their data stewardship policy, “effective data stewardship is the process for maximizing the value of data as an institutional resource.”

Data stewardship touches every phase of the data life cycle:

Data creation
Data processing
Data storage
Data usage
Data archiving
Data destruction

By combining business acumen with technical skills, data stewards make sure all departments have access to reliable data in their daily work. As such, they oversee specific activities such as data collection, data cataloging, and data inventorying that enable users to find and extract value from data sets.

At a higher level, data stewards contribute to building a modern data culture inside an organization. For example, they may help identify strategic use cases for data and educate the C-suite on new ways to leverage data in decision-making.

Why is data stewardship important? #

Data stewardship is important because it facilitates the accuracy, usability, and accessibility of an organization’s data assets. When data reliability is high, employees are more likely to consistently use the data at their disposal to enhance their daily activities.

Without data stewards to implement and enforce data governance, businesses are left constantly fighting fires when data dashboards and products are inaccurate.

What are the benefits of data stewardship? #

The benefits of data stewardship include:

Improved data quality and reliability
More effective implementation of data governance
Stronger data documentation and awareness about data management best practices
Faster and more valuable analytics programs
Reduced risks around data-related security and privacy requirements
Enhanced ability to meet compliance requirements and regulations

Consider this example of the power of effective data stewardship: The Earth Science Information Partners (ESIP) wanted to foster greater data collaboration between scientists. To that end, they identified data stewards to create citation guidelines and uniform metrics that could be used to find information across many different types of data repositories. This visibility of shared data and chance of collaborating on it has a significant impact on the progress of issues like climate change between engineering geologists, geochemists, oceanographers, etc.

A survey by the United Nations Economic and Social Council 2024 indicated that 80% of national statistical offices view increased data sharing and reuse as a key success metric for effective data stewardship. However, challenges remain in balancing this with privacy and security concerns.

From a corporate organization point of view, data stewards act as bridges. As Laura Maden said in Disrupting Data Governance, “Data stewards were meant to help solidify the squishy… They speak the language of IT and translate that back to the business. The role requires the patience of a kindergarten teacher and the ability to successfully negotiate a hostage situation.”

Data stewardship and data governance #

Data stewardship and data governance and closely interlinked, however, these terms are not synonymous:

Data governance is a set of policies, processes, and standards to collect, manage, and store data.
Data stewardship is the implementation of those policies, processes, and standards.

In other words, data governance is the strategy that determines how data management decisions should be made, and data stewardship is the tactical implementation based on that strategy. To help connect the dots, here are a few terms related to data governance and their application to data stewardship:

Data architecture refers to the models, policies, rules, and standards that govern which data is collected and how it is stored, arranged, integrated, and used in data systems. Data architecture is designed collaboratively by Chief Data Officers, data stewards, data admins, etc. to guide how data flows across the organization.
Data curation is the end-to-end process of preparing and managing data (identifying, cleaning, and transforming it) so that it is ready for business use — a crucial skill of the data steward.
Data glossaries are collections of terms and definitions that help data users understand data assets’ key characteristics. Data stewards often oversee the creation and maintenance of an organization’s data glossary.
Data governance frameworks build upon data architecture to encompass all the people, processes, technologies, and workflows needed to support governance and guide how data stewards make decisions.
Metadata management entails collecting, categorizing, integrating, and maintaining high-quality metadata. This is another vital discipline for data stewards to master in order to effectively manage vast amounts of data.

Who are data stewards and how do they enforce data governance? #

A data steward is a subject matter expert who is responsible for defining and maintaining the integrity of a specific type of data or data domain. They help the organization build data glossaries, create and maintain data quality rules, and determine who has access to data.

Here’s how data stewards slot into other data governance roles and responsibilities. The most common data governance roles are:

Data admin: Responsible for operationalizing the data governance program by processing and transforming data into best-fit data models
Data custodian: Handles the movement, security, storage, and use of data (e.g., the technical aspect of setting up permission controls, versioning master data, configuring system backups, etc.)
Data steward: Enables data collaboration and democratization by serving as the bridge between the business users and the IT department; oversees the standardization of data definitions and optimization of data-related workflows and communications
Data user: Anyone who extracts value from data: marketers, researchers, executives, business managers — in some organizations, virtually every employee may be a data user

The above definitions are not hard and fast rules. For many businesses, there may be significant overlap or consolidation of these roles. The number of data stewards and exact purview of each steward is highly dependent on the needs of the organization.

For example, the world-renowned SAS Institute notes that data stewardship may be broken down by:

Subject area (e.g., customer or product)
Function (e.g., finance or sales)
Business process (e.g., procurement or enrollment)
Systems (e.g., billing or inventorying)
Project (e.g., launching or refining the program)

How are data stewards chosen? #

So, how do you choose data stewards for the above data domains? Robert Seiner, founder of KIK Consulting and The Data Administration Newsletter, has a simple model for choosing data stewards. This model notes that data stewards may be appointed in one of three different ways:

Employees may be assigned to be data stewards and own certain data domains (aka told they have no choice but to be held accountable for the integrity of the data).
Data steward roles may be identified and designed with the idea that the employees who are the best fit will naturally fill the role (e.g., a senior data team member with both engineering and analytics skills).
Data stewards may be recognized based on the ad hoc work that employees are already doing to govern data.

The first two methods of “assigning” and “identifying” are very much part of the restrictive, top-down data governance models that are (hopefully) on their way out.

The third “recognizing” method is more aligned with the future of bottom-up, collaborative data governance.

What’s next for data governance and data stewardship? #

It’s no secret that data governance has a reputation for being restrictive and bureaucratic. The “dreaded G word” is often called boring, as is the technical-sounding discipline of “data stewardship.”

But what if governance and stewardship earned a reputation for aiding innovation instead of stifling it? They are in need of something more collaborative and community-led than the data governance programs of old.

Thankfully, there is a new vision of data governance and stewardship emerging, one that takes a decentralized, bottom-up approach where all data professionals are able to contribute instead of only a chosen few. Instead of completing a data project and then making changes based on data governance requirements, the future of data governance will be baked directly into the daily workflows of data professionals.

In practice, this means adopting a data platform that allows for data access flexibility, permission controls, etc. depending on evolving business needs — and empowering more data professionals to contribute to data stewardship.

Learn more about the modern data catalog platform and how it simplifies data stewardship.

How organizations making the most out of their data using Atlan #

The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:

Automatic cataloging of the entire technology, data, and AI ecosystem
Enabling the data ecosystem AI and automation first
Prioritizing data democratization and self-service

These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.

Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.

How Atlan helps in data stewardship and governance #

Atlan is a third-generation data catalog and metadata management tool built on the premise of embedded collaboration that is key in today’s modern workplace, borrowing principles from GitHub, Figma, Slack, Notion, Superhuman, and other modern tools that are commonplace today.

Atlan helps data stewards make access control & governance a breeze with automated PII classification, granular access control & usage tracking.

FAQs about Data Stewardship #

1. What is data stewardship in the context of governance? #

Data stewardship refers to the process of managing and overseeing an organization’s data assets to ensure data quality, privacy, and compliance. Within data governance, it plays a critical role in defining data standards, managing access, and ensuring proper data usage across the organization.

2. How do organizations implement effective data stewardship? #

Organizations implement data stewardship by establishing clear roles and responsibilities for data stewards, deploying data management tools, and embedding data stewardship practices into daily workflows. They also create data governance frameworks to ensure accountability and consistency in data practices.

3. Why is data stewardship important for compliance? #

Data stewardship ensures adherence to regulatory requirements by enforcing data privacy, security, and auditability. It helps organizations maintain compliance with frameworks such as GDPR, HIPAA, and other industry standards, reducing the risk of legal and financial penalties.

4. How does data stewardship improve data quality? #

By defining data standards, monitoring data usage, and resolving data discrepancies, data stewardship improves data accuracy, completeness, and consistency. This leads to better decision-making and operational efficiency.

5. Who is responsible for data stewardship in a company? #

Data stewards are typically responsible for managing data within their domains. However, the responsibility extends to all employees interacting with data, under the guidance of a centralized data governance team.