Data Steward vs. Data Owner: 7 Key Differences & Relationship

Companies today generate massive amounts of data, such as customer data, operational data, and more, from diverse sources. This makes it challenging to store, organize, and analyze data effectively. To address these complexities, organizations rely on two important roles within their data governance framework—one is the Data Steward, and another is the Data Owner.

Data stewardship focuses on the practical coordination and implementation of data policies, whereas data governance pertains to the creation of those high-level policies. These roles are crucial for ensuring data is accurate, high-quality, and secure throughout the data lifecycle.

In this article, you will explore Data Owner vs Data Steward, what these roles involve, how they differ, and their interactions. Understanding these roles is key to managing data effectively and promoting data quality.

Who is a Data Steward?

In data management, a Data Steward plays a pivotal role in ensuring that data is handled, stored, and utilized effectively and responsibly across an organization. A Data Steward acts as a guardian or custodian of data, overseeing its lifecycle and ensuring it aligns with organizational objectives and regulatory requirements. Data stewardship responsibilities encompass various duties, including managing data integrity, quality, and security, as well as defining and communicating these responsibilities within the organization.

The primary responsibility of a Data Steward is to support data integrity, quality, and security within the organization. They bridge the gap between business users and IT professionals, advocating for best practices in data management while understanding different departments’ unique needs and priorities on the business side. Data stewardship focuses on maintaining data quality and ensuring data assets are managed effectively.

Data Steward Responsibilities

• Data Governance and Policy Enforcement: Data Stewards establish and enforce data governance policies and procedures. They collaborate with stakeholders to define data standards, guidelines, and protocols to ensure consistency and compliance across the organization.‍
• Data Quality Management: Ensuring data accuracy, completeness, and reliability is a key responsibility of Data Stewards. They implement data quality assessment processes, identify anomalies or discrepancies, and coordinate efforts to rectify issues and improve overall quality. Proficiency in data analysis is essential for Data Stewards to effectively manage and safeguard data, facilitating collaborative efforts between technical teams and business sectors.‍
• Data Access and Security Management: Data Stewards are responsible for managing access controls and permissions to safeguard sensitive or confidential data for their assigned datasets, incorporating risk management strategies to mitigate potential threats. They work closely with IT security teams to attain data access policies, monitor user activity, and mitigate security risks.‍
• Metadata Management: Metadata, which provides context and information about data assets, is managed and curated by Data Stewards. They define metadata standards, tags, and classifications to enhance data discoverability, lineage, and understanding across the organization.‍
• Data Lifecycle Management: Throughout the lifecycle of data, from creation to archival or deletion, including disposal policies, Data Stewards oversee its management and governance. They establish policies and procedures for data retention, archiving, and disposal, ensuring compliance with regulatory requirements and business needs. Archiving is a crucial stage in the data lifecycle that involves managing and storing data securely while ensuring it remains accessible and trustworthy.

Who is a Data Owner?

Data Owners are individuals or groups responsible for overseeing an organization's data, ensuring its effective management. They hold the ultimate authority and accountability for the data they manage, ensuring its integrity, confidentiality, and appropriate use throughout its lifecycle.

Data Owner Responsibilities

Data Ownership and Accountability: One of the primary roles of a Data Owner is to establish clear ownership of the data assets within their area of responsibility. This involves defining ownership rights, responsibilities, and authorities over specific datasets to ensure that individuals or teams are held accountable for the data’s accuracy, reliability, and security.

Additionally, Data Owners must establish structured procedures and rules for data governance to maintain data quality, confidentiality, and effective management practices. They must also manage data effectively, recognizing the rights and interests of individuals related to that data and addressing potential harms from data omission or exclusion.‍

Data Classification and Prioritization: Data Owners are responsible for classifying and prioritizing data assets based on their importance, sensitivity, and criticality to the organization. This involves categorizing data into different classes or levels based on factors such as confidentiality, integrity, availability, and regulatory requirements.‍

Data Usage and Access Control: The task of the Data Owner is to oversee data usage and control to ensure that data is used appropriately and securely. This includes defining access controls, permissions, and authentication mechanisms to govern who can access, modify, or delete data assets. They also decide which datasets are going to be assigned to different Data Stewards.‍

Data Privacy and Compliance: They are responsible for implementing measures to protect sensitive information from unauthorized access, disclosure, or misuse. This includes implementing data anonymization and encryption techniques and conducting privacy impact assessments. While ensuring that data handling practices comply with applicable regulations such as GDPR, CCPA, and HIPAA.‍

Data Stewardship Collaboration: Collaboration with Data Stewards is essential for effective data governance and management. Data Owners work closely with Data Stewards to support data stewardship responsibilities, enforce data policies and standards, and resolve data-related issues and challenges. They ensure that data stewardship encompasses all aspects of managing the data lifecycle, including promoting data quality and maintaining comprehensive data documentation.

Data Stewardship Program

A data stewardship program is a structured approach to managing an organization’s data assets, ensuring that data is accurate, reliable, and secure throughout its lifecycle. The program involves identifying and assigning data stewards, establishing data governance policies, and implementing procedures for data collection, storage, and access.

A data stewardship program also includes ongoing monitoring and evaluation to ensure that data quality issues are identified and addressed promptly. By implementing a data stewardship program, organizations can promote data quality, reduce data risks, and support informed decision-making. Data stewards are responsible for ensuring that data governance policies are implemented and followed, and they work closely with business units, data managers, and other stakeholders to ensure that data is managed effectively.

Data Governance Framework

A data governance framework is a structured set of policies, procedures, and standards that outline how an organization manages its data assets. It provides a comprehensive approach to data management, ensuring that data is handled consistently and securely across the organization.

Information governance plays a critical role in data stewardship, ensuring data security, quality, and compliance with regulations.

Key components of a data governance framework include:

• Data Quality: Ensuring that data is accurate, complete, and reliable.
• Data Security: Protecting data from unauthorized access and breaches.
• Data Access: Defining who can access data and under what conditions.
• Data Documentation: Maintaining detailed records about data sources, definitions, and usage.

Within this framework, data stewards play a critical role in enforcing data governance policies and procedures. They work closely with data managers and other data-related personnel to ensure that data is managed in accordance with organizational standards and regulatory requirements.

By implementing a robust data governance framework, organizations can ensure that their data is well-managed, secure, and compliant with relevant laws and regulations. This structured approach helps in maintaining data integrity, supporting business objectives, and mitigating risks associated with data management.

Importance of Data Stewardship

Data stewardship is critical in ensuring the quality, security, and integrity of an organization’s data assets. Data stewards are responsible for promoting data quality, managing data access, and ensuring that data is handled in accordance with established policies and procedures.

Effective data stewardship supports business decision-making by providing accurate and reliable data. It drives operational efficiency by ensuring that data is accessible and usable for various business processes. Additionally, it helps maintain regulatory compliance by ensuring that data handling practices meet legal and industry standards.

Data stewardship also plays a vital role in identifying and mitigating data-related risks, such as data breaches and cyber attacks. By prioritizing data stewardship, organizations can ensure that their data is secure and used to drive business success.

In the public sector, particularly in health care, data stewardship is essential for informing patient care and driving public health outcomes. Accurate and reliable data is crucial for making informed decisions that impact public health and safety.

Data stewardship practices that prioritize transparency and ethical management of health information are necessary to balance societal needs for data with the protection of individual privacy rights, framing certain data uses as being in the public interest.

Overall, data stewardship is a cornerstone of effective data management, ensuring that data assets are accurate, reliable, and secure, and that they are used to support business and public sector objectives.

Use of Data in Organizations

Data is a critical asset for organizations, and its effective use is essential for informed decision-making, strategic planning, and operational efficiency. Data stewards play a key role in ensuring that data is used effectively, by providing guidance on data access, data documentation, and data security.

They also work with business units to identify opportunities for data-driven decision-making and to develop strategies for promoting a data-driven culture. By leveraging data assets, organizations can gain valuable insights, improve operational efficiency, and drive business growth. Effective data management is critical to ensuring that data is accurate, reliable, and secure, and data stewards are responsible for promoting data quality and managing data risks.

Emerging Technologies and Data Management

Emerging technologies, such as artificial intelligence, blockchain, and the Internet of Things (IoT), are transforming the way organizations manage and use data. Data stewards must be aware of these emerging technologies and their potential impact on data management, ensuring that data governance policies and procedures are adapted to address new data risks and opportunities. By leveraging emerging technologies, organizations can improve data quality, reduce data risks, and support informed decision-making.

Data stewards must also consider the potential impact of emerging technologies on data security, data access, and data documentation, and work with stakeholders to develop strategies for managing these risks.

Data Steward vs Data Owner: Key Differences

The main difference between a Data Owner and a Data Steward is that a Data Owner is accountable for the overall management and governance of a dataset, while data stewardship encompasses ensuring the accuracy, quality, and proper usage of the data on a day-to-day basis.

While both Data Stewards and Data Owners are integral to data governance, they have distinct responsibilities that differ significantly in their authority and focus. The process of data governance involves designing meaningful participation mechanisms and engaging beneficiaries throughout various stages of data management, such as collection, analysis, and decision-making. Here are a few key differences between them:

    
Untitled spreadsheet - Sheet1 (4).csv
    

        

            
Aspect
            
Data Steward
            
Data Owner 
        
    
    

        

            
Responsibility
            
Ensures data complies with regulations, policies, and standards. Implements data governance practices.
            
Determines data governance policies, including access controls, data retention, and privacy policies.
            

            
        
        

            
Role Focus
            
Focuses on day-to-day data management tasks and operational issues.
            
Focuses on strategic decision-making regarding data and its usage. 
        
        

            
Collaboration
            
Collaborates operationally with data analysts, scientists, IT, and business teams to ensure data quality and governance.
            
Collaborates strategically with senior management, legal, and external partners to align data strategies with business goals and ensure compliance.
            

            
        
        

            
Accountability
            
Ensures adherence to data policies and procedures and resolves data-related issues.
            
Holds ultimate accountability for data quality, security, and regulatory compliance.
            

            
        
        

            
Decision-Making
            
May participate in data-related decisions but typically not the final authority.
            
Makes decisions regarding data strategy, usage, and investments, often in consultation with stakeholders.
            

            
        
        

            
Authority
            
Implements data-related decisions based on policies and guidelines set by the Data Owner.
            
Holds authority over data-related decisions and policies. 
        
        

            
Expertise
            
Requires a strong understanding of data management practices, governance principles, and relevant technologies.
            
Deep knowledge of business processes, compliance regulations, and data management practices is required.
        
    

Data Owner vs Data Steward: Relationship

The relationship between data Stewards and Owners is similar to a partnership where each plays a distinct yet complementary role in managing and maintaining data assets. The Data Owner is responsible for defining the strategic direction and priorities for the data.

And the data Steward focuses on implementing policies, procedures, and practices to ensure the data’s integrity, quality, as well as security within specific business units. This collaboration helps create better outcomes by incorporating diverse perspectives and fostering a more equitable system.

Data Owners oversee data acquisition, storage, and usage within an organization, making the final decision about data classification, access permissions, and compliance with regulations. They establish data governance policies and guidelines, define data ownership, and ensure alignment with organizational objectives and regulatory requirements.

On the other hand, Data Stewards are tasked with executing the data governance policies set by Data Owners. They work with data, performing data profiling, implementing cleansing practices, and metadata management.

Data Stewards collaborate with Data Owners to understand requirements and formulate data management plans. Once the plans are established by Data Owners, Stewards execute them, fostering a continuous feedback loop between operational execution and strategic oversight.

While Data Owners provide the vision and direction for data management, Data Stewards are responsible for implementing as well as operationalizing those directives. They work together to ensure data assets are effectively managed, protected, and utilized to support business goals along with the decision-making processes.

This collaborative relationship is essential for maintaining data quality, integrity, and compliance while maximizing the value of data assets for the organization.

Examples to Understand Differences Between Data Owner And Data Steward

Here are some real-world examples to understand the roles in a better way:

Retail

In a retail chain scenario, let’s explore the roles through the lenses of a Data Steward and a Data Owner:

• Data Steward (Digital Marketing Team): A digital marketing team member is responsible for validating and cleaning customer data collected from sweepstake entries. They ensure data quality by systematically formatting, cleaning, and enriching the dataset in line with data governance policies. Understanding how data is created, managed, and used is crucial for maintaining its reliability and fostering trust among stakeholders. They also ensure that the data cannot be used to identify individuals without proper privacy protections.‍
• Data Owner (Head of Sales): The Head of Sales oversees sales targets and is deeply involved in the success of marketing campaigns. As the Data Owner, they have the authority and resources to enhance data quality and security.
For instance, they may invest in technology to automate data capture or enforce authentication measures to protect data access. They may also utilize resources meant to help with creating a website’s privacy policy that meets the strict notification requirements outlined by privacy laws like the GDPR and the CCPA.

In this scenario, the Data Steward focuses on maintaining data quality, while the Data Owner oversees strategic decisions related to data management. This illustrates data stewardship’s distinct yet complementary roles in organizational data governance.

Manufacturing

In a contract manufacturing company, the Production Manager assumes the role of Data Owner, overseeing all production-related data. Under this arrangement, various individuals are appointed as Data Stewards, each responsible for specific datasets:

• Production Shift Supervisors act as Data Stewards for material usage, cycle time, and part output data.
• Maintenance Engineers serve as Data Stewards for machine performance, availability, breakdown, and time-to-repair data.
• Production Planners take on the responsibility of Data Stewards for the utilization and efficiency of data.
• The Quality Lead is designated as the Data Steward for analyzing and rejecting defective data.

Effective design and participation in data-driven systems are crucial for these roles to function optimally.

These Data Stewards play a crucial role in ensuring the data’s quality, security, and availability within their respective domains. This structure may not be universally applicable to all manufacturing companies. However, it highlights the importance of tailored Data Governance roles based on business goals and internal processes. Additionally, coordination with the organization’s IT department, represented by the Data Custodian, is vital for managing data capture and storage infrastructure.

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Utilizing Airbyte for Effective Data Management

Incorporating new technologies into data stewardship efforts is essential for navigating the challenges associated with data governance. Data stewardship and ownership are critical components of any data management strategy. They ensure that data is accurate, secure, and compliant with regulations. While they simplify data management, data integration tools like Airbyte provide solutions to put these practices into action.

Many third-party service providers enhance website functionalities, emphasizing the significant role these services play in the overall user experience and data stewardship.

Airbyte is a data integration platform that allows you to seamlessly connect to various sources. This functionality is crucial for data management and governance. It enables the consolidation of data from disparate sources into a central location, facilitating better data quality control, access management, and overall data governance.

Here’s how it can help:

• Pre-Built Connectors: Airbyte offers over 600 pre-built connectors, ensuring comprehensive data coverage. This reduces development time and costs while enabling easy connection to various data sources and destinations, thereby enhancing overall data stewardship and ownership.
• Change Data Capture (CDC): CDC functionality tracks incremental changes in datasets, enabling monitoring of data updates. This ensures that your organization has the most accurate data for analysis.
• Connector Development Kit (CDK): The CDK empowers you to develop custom connectors to integrate with diverse data sources and destinations. By allowing the creation of tailored connectors, CDK enhances comprehensive coverage of all data sources.
• PyAirbyte: Airbyte’s Python client library facilitates programmatic interaction with the Airbyte connectors, enabling customized data integration processes. Through PyAirbyte, you can perform customized measures using Python programming to control data and complex transformations to streamline workflows.

Bridge Strategy and Execution with the Right Data Roles

Effective data governance depends on clearly defined responsibilities—and Data Stewards and Data Owners are two sides of the same coin. Owners define the policies, priorities, and risk frameworks. Stewards make those policies real by maintaining data quality, access controls, and integrity on the ground. When aligned, these roles ensure data is not only compliant and secure but also usable and valuable across the organization.

Airbyte strengthens this relationship by making it easier to centralize, validate, and control access to data from hundreds of sources. Whether you’re a steward ensuring quality at every step or an owner making strategic calls, Airbyte’s platform helps ensure your data operations are consistent, auditable, and built to scale.