Data Stewardship vs. Data Governance: What’s the Difference?

Data stewardship and data governance are two crucial aspects of data management. While they serve different purposes, they work together to ensure the accuracy, security, and best practices for an organization's data usage. In a recent survey of data analytics professionals, 75 percent stated that improved data quality is a primary goal [1]. Among participants with established data governance programs, 83 percent reported tangible value from improved data quality [1]. Data governance establishes an organization-wide strategy to maintain data quality, usability, and security, while robust data stewardship implements these policies.

You can expect the total volume of data generated by 2025 to reach 182 zettabytes [2]. For reference, one zettabyte equals 1,000,000,000,000,000,000,000 bytes—that’s 21 zeros. This projection tracks data growth from 2010, when just two zettabytes were generated, to 2024, when 149 zettabytes were created. As data expands, organizations can benefit from data stewardship and governance to maximize their potential, with forecasts predicting 394 zettabytes by 2028.

Given the vast amounts of data generated and analyzed, discover how your organization can leverage data stewardship and data governance to maximize your data’s potential. 

What is data stewardship?

Data stewardship refers to the responsibility for implementing and adhering to the procedures and guidelines established by data governance. A data steward is responsible for implementing these practices, maintaining quality assurance, managing metadata, and categorizing sensitive data. Data stewards collaborate with various data professionals, including data architects, business intelligence specialists, and data owners. Their role involves:

• Managing data access

• Ensuring data quality throughout its life cycle

• Implementing policies defined by data governance

• Streamlining data storage, collection, and security

Data stewards play a central role in data management and can help develop business practices that support operational efficiency. 

Why is data stewardship important?

A company can collect as much data as it needs to train a machine learning algorithm or extract business insights, but the data is useless without proper data stewardship to ensure its quality, security, and accessibility to the data team and other stakeholders. 

Data stewardship is important for different reasons across various communities. The biomedical community, for example, uses sensitive patient information for research and must ensure compliance. Data stewards in this context must understand the number of stakeholders affected by the use of this data. Some of these include:

• The public: Decisions in biomedical science based on big data can impact the daily lives of everyone in a community.

• Data subjects: Those represented by or from whom the data is collected should know its purpose, usage, and reason for collection. For example, if the data comes from a mental health questionnaire, data stewards must ensure subject notification of data usage details, including who will have access and whether the data is being collected anonymously.

• Vulnerable groups and communities: Data on socioeconomic status, for example, can reveal health disparities between socioeconomic status and thus potentially create biases or stigma. Data stewards can ensure identity protection for the stakeholders and enforce compliance with privacy governance. 

Data stewardship is a broad concept that can have different meanings based on the stakeholders involved, the organization collecting the data, the data’s usage, and the disciplines involved. For instance, the data governance strategy adopted by a marketing firm will likely differ from that of a biomedical research organization, leading to distinct responsibilities for data stewards in each context. 

What is data stewardship used for?

Organizations typically implement data stewardship programs to fulfill their data governance policies. The role of data stewardship often varies depending on the stage of an organization’s data program. The responsibilities of a data steward in a company in the early stages of data governance will likely differ from those in a company where foundational data governance is complete. Some use cases for data stewardship include:

• Master data management: Creating a single source of “truth” from an organization's data to reduce redundancy and promote efficient workflows through data processing technologies like automatic integration, cleansing, and redundancy reduction. 

• Quality assurance: Ensuring that the data your organization uses meets quality standards through metrics, error detection, and data profiling strategies.

• Security and privacy: Securing data, informing stakeholders of its use, complying with data privacy regulations, and protecting data from malicious actors. 

• Access and authorization: Providing users with the data required for their roles and informing them of its use and privacy.

It’s important to note that data stewardship functions align with the policies and procedures established by the organization's data governance committee. 

What is data governance?

Data governance, a subset of data management, provides a framework for data storage, organization, quality, and access, helping organizations protect and manage their data effectively. A data governance program acts as a central authority, creating and overseeing the data management policies that data stewards implement. These policies ensure that quality data securely reaches its intended destination while complying with regulatory requirements. 

Data governance frameworks typically establish specific goals in areas such as data quality, compliance, or data-driven decision-making. They also identify roles and specify responsibilities for each. Roles within a data governance framework typically include:

• Executives: Data executives, including directors or other authorized individuals, create data governance programs to align with an organization’s business initiatives and goals. 

• Stewards: Data stewards implement and manage the daily processes of the governance program. They monitor and report issues related to compliance, security, and access. 

• Owners: Data owners manage regulatory compliance, uphold data quality standards, and develop data access policies. Data ownership can also refer to individuals represented by the data. 

• Engineers: Data engineers select and integrate the tools, technology, and data sources to ensure security, quality, and access. 

Why is data governance important?

Data governance principles are important because data is a valuable asset that must be usable and protected. Effective data governance programs establish controls that ensure data quality, enable insightful analytics, and enhance compliance with regulations that reduce risk. These programs provide the structure necessary for data analytics. Consider the data governance program as the architecture of a building, with the data stewards as the workers constructing it, and the analysts as the occupants performing their tasks within the structure.

What is data governance used for?

A key aspect of a data governance program is to ensure accountability and assign responsibility for data and its dissemination processes. Governance programs also ensure data quality and help create an overall data management strategy. Uses of data governance include:

• Creating policies and protocols that control data access and monitor usage to prevent data breaches or cybersecurity attacks

• Ensuring compliance with data usage laws, such as the Health Insurance Portability and Accountability Act (HIPAA), to avoid fines and maintain trust with data subjects

• Establishing clear data access guidelines to enable departments to work more efficiently and streamline workflows

Data governance also supports artificial intelligence (AI) efforts by ensuring the data used in AI and machine learning initiatives is adequately vetted. A data governance program can help prevent the liabilities, risks, and security breaches that come with increased AI usage. In a 2025 survey of 632 chief data officers (CDOs), 41 percent identified “enhancing data governance policies and standards” as essential for effectively using AI [3].

Data stewardship vs. data governance examples

The data governance framework established within your organization guides data stewardship. The Data Governance Institute offers a sample framework [4] that can help you structure your own framework. Before creating your data governance framework, consider:

1. Why it exists

• Mission and value: How the program delivers value, risk reduction, and cost

• Beneficiaries: Those who benefit from data quality, better policies, and better decision-making.

2. What it governs

• Controls: Mechanisms to monitor data and mitigate risk

• Accountabilities: Roles responsible for compliance 

• Decision rights: Who has the authority to include components in the program

• Policies and rules: Policy operationalization and enforcement

• Data products: Data sets that the program monitors for quality and standardization

3. Who is involved

• Participants: Includes decision-makers, data stewards, and custodians who implement and manage the program.

4. How it will function and add value

• Organizational program: Department-specific workflows for meeting governance requirements

• Processes, tools, and communication: Documented methods that ensure compliance

By following a structured approach like this, you can ensure that your data governance program effectively addresses all the challenges of data management. 

Is data stewardship part of data governance?

Yes, data stewards operate within the governance framework as part of the “who.” Rather than comparing data stewardship versus data governance, it may be more useful to explore how they complement each other. Each component of the governance framework addresses key questions, while data stewards ensure compliance with relevant policies and regulations. They monitor the daily application of the data governance program, tracking usage, identifying challenges, and addressing any issues their department encounters in using and sharing data. 

For example, if a department wants to use customer data to identify marketing opportunities, a data steward ensures that the requested data adheres to the data use policy established by the governance framework. If the data complies with the policy, the steward can grant permission to the department. However, if it does not, but the team believes it could benefit the company, they can advocate for a change in the governance policy to allow this use. In this scenario, the data steward upholds the mission, oversees controls, makes decisions, and may even advocate for program changes. 

Learn more about data stewardship and governance on Coursera

Data stewardship and data governance are essential for managing an organization's data. To gain in-demand skills in data analysis, science, and management, consider the IBM Data Warehouse Engineer Professional Certificate or the Google Data Analytics Professional Certificate on Coursera.