Data Steward vs. Data Owner: Key Differences & Relationship

March 28, 2024
15 min read

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. These roles are crucial for ensuring data is accurate, high-quality, and secure. 

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.

Data Steward Overview

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.

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.

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.
  • 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. 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, Data Stewards oversee its management and governance. They establish policies and procedures for data retention, archival, and disposal, ensuring compliance with regulatory requirements and business needs.

Data Owner Overview

Data Owners are individuals or groups responsible for overseeing specific organizational datasets. 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. 
  • 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 governance initiatives, enforce data policies and standards, and resolve data-related issues and challenges.

Data Steward vs Data Owner: Key Differences

While both Data Stewards and Data Owners are integral to data governance, they differ significantly in their responsibilities, authority, and focus. Here are a few key differences between them:

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.

Data Owners oversee data acquisition, storage, and usage within an organization, making decisions 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.

Real World Example to Understand Data Owner vs 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.
  • 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.

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.

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.

Utilizing Airbyte for Effective Data Management

Airbyte

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.  

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 350 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.

Conclusion

Data Stewards and Data Owners are vital for managing data in companies. While Data Stewards focus on keeping data accurate and secure, Data Owners make big decisions about how data is used. By working together, they ensure data is reliable, valuable, and safe. 

Limitless data movement with free Alpha and Beta connectors
Introducing: our Free Connector Program
The data movement infrastructure for the modern data teams.
Try a 14-day free trial