Data stewardship is a critical practice within data governance that focuses on managing and overseeing an organization’s data and metadata assets to ensure they are accessible, reliable, secure, and used effectively throughout their lifecycle. A Data Steward’s role is to ensure organizational data and metadata meet quality, accuracy, format and value criteria, ensuring data is properly defined and understood, i.e., standardized, across an enterprise.
A Data Steward is a person responsible for working with the data and metadata to meet the requirements of the data governance program . Data steward responsibilities include overseeing the data lifecycle and ensuring compliance with industry regulations.
The Data Steward, who is often not just one person, but a collection of people, aligns the IT systems with the business’ requirements. An individual data steward can be responsible for multiple domains. The data steward has the challenge of guaranteeing that one of the corporation’s most critical assets–its data–is used to its fullest capacity.
What is Data Stewardship?
Definition of Data Stewardship
Data stewardship is the process of managing and overseeing an organization’s data assets to ensure their quality, integrity, and security. It involves the implementation of data governance policies and procedures to ensure compliance with regulatory requirements and industry standards. Data stewardship is a critical component of data governance, as it ensures that data is accurate, reliable, and trustworthy. By managing data assets effectively, data stewards help organizations maintain high standards of data quality and integrity, which are essential for making data-driven decisions.
Benefits of Data Stewardship
The benefits of data stewardship include improved data quality, increased data security as well as enhanced data governance. Data stewardship helps to ensure that data is accurate, complete, and consistent, which is critical for informed decision-making. It also helps to reduce the risk of data-related errors and inaccuracies, which can have significant consequences for an organization. By implementing robust data governance policies and procedures, data stewards help to protect sensitive data and ensure compliance with regulatory requirements. The benefits of data stewardship extend beyond data quality and security, contributing to the overall efficiency and effectiveness of the organization’s data governance program.
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Data Stewardship Roles
There are 9 different types of data stewards including:
Executive Sponsor Data Governance Program Manager
Data Governance Specialist
Business Steward
Lead / Domain / Subject-Area Steward
Chief Steward
Technical Steward
Data Custodian
Data Owner
Effective stewardship is vital for managing an organization’s data assets, ensuring the accuracy, integrity, and accessibility of data, which enhances decision-making and compliance across the organization.
Traditionally the greatest obstacles a data stewardship initiative experiences involve organizational change management. Data governance programs cut across departments and lines of business changing the status quo of an organization. It is an industry best practice to have a strong group of executive sponsors to help ensure that the organization adheres to the data policies, standards and rules.
Executive sponsors do not need to attend every data stewardship meeting, nor do they need to participate in detailed tasks like defining data definitions. They are responsible for communicating the value of data and its impact on the organization’s behavior and performance. Most importantly, executive sponsors ensure that the data governance program is funded. Key qualities in an executive sponsor include:
Someone willing to be an executive sponsor
Someone with an understanding of the value of data as a corporate asset
Excellent communications skills
A person with executive ranking
Someone with high credibility
Someone knowledgeable about information problems within the company
A person willing to challenge the company status quo and promote change for improved information management
Data Governance Program Manager
The Data Governance Program Manager (DGPM) is responsible for the day-to-day organization and management of the data governance council and the entire data governance program. Typically, the DGPM will be a senior level person as opposed to executive level.
The DGPM must have a sound knowledge of both the technical and the business sides of the organization, but most importantly, have an expert level of knowledge on data governance , its best practices and have experience implementing successful, enterprise data governance programs.
They must be politically savvy to coordinate and guide the data governance council, lead the data governance specialists and be able to lead the data governance council to consensus across disparate groups within the organization. Also, the DGPM needs strong leadership and communication skills to maintain the data governance program’s focus and organizational contacts.
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Data Governance Specialist
The data governance specialist is a data governance professional and has experience building enterprise data governance programs. They report to the DGPM and a large data governance program may need multiple specialists to meet their requirements.
Data governance specialist play a key role in many activities, including:
Develops data policies, standards, and rules that the enterprise will follow
Directly work with data stewardship domain groups to ensure that they are adhering to the industry business practices and data governance procedures that have been implemented
Build key data governance documents and artifacts like, Data Governance Socialization Plan, Data Governance Communications Plan, Domain / Subject-Area Model, data stewardship workflows , meeting notes, etc.
Business Steward
Key individuals from the business will be asked to serve as business stewards. The business steward is responsible for defining the procedures, policies, data meanings, data definitions and requirements of the domains assigned to them. Business stewards must have a strong knowledge of the business requirements and policies / processes of the organization and work on a day-to-day basis with an organization’s data.
Domain / Lead / Subject-Area Steward
From the business stewards within a domain (or subject-area), there should be a Domain (Lead / Subject-Area) Steward. The Domain Steward is the central point of responsibility for their domain (e.g. Customer, Product, Location, Supplier, etc.). Lead Stewards are usually “first among equals” on their domain’s stewardship team.
Domain Stewards serve on the Data Stewardship Coordination Group as representatives of their domain. In additional they attend all of the Data Governance Council meetings to ensure that the requirements from the meetings are fully understood since the domain steward’s teams will be responsible for fulfilling them.
Chief Steward
From the pool of domain stewards, a Chief Steward is elected. The Chief Steward is the leader of the domain stewards on the Data Stewardship Coordination Group. Chief Stewards are usually “first among equals” of the domain stewards on the coordination group.
Technical Steward
Technical stewards typically come from the IT side of the organization or an IT-like group within a business unit. Technical stewards assist business data stewards under the guidance of the Data Governance Program Manager with appropriate tools (repository, Metadata Management product, etc.) and the profiling of data in the applications. They do the technical system’s work needed to support the data governance program. Technical stewards do not need to possess the domain expertise of the business steward or domain steward.
Data Custodian
The data custodian ensures that the data and metadata in each application meet the standards and guidelines developed and implemented by the business stewards. They enter and maintain the correct data values in an application based on the requirements provided by the data stewards. They are typically under the guidance of the technical steward.
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Data Owner and Data Ownership
The actual owner of the data can be a tricky question to answer and can differ based on your organization’s definition of ownership. Typically, there are two types of data ownership : legal and internal.
Legal Data Ownership Regulations and law can differ based on industry; however, many industry statutes require that a C-Level executive or the company be the owner of the data or specific types of data.
Internal Data Ownership Realistically, a C-Level executive will not be working on a day-to-day basis with the data of a company or large government agency. Therefore, an internal data owner will be defined. This will typically be a person (and data steward) who works with the data on a day-to-day basis.
Experts recommend that the main person, who works with the data element at an application level is the data owner. This person should follow the data standards, rules and policies of the data governance program; data owners should work together to define these rules and policies. This implies that there are many data owners (one for each application).
A data steward and a data owner are not the same thing; however, they could be the same person. These comments are only for data ownership. There are many kinds of “owners”, Application Owner, Report Owner, Technology Owner, etc.
The responsibilities of a data owner vary based on the industry that the organization resides in. Examples of data owner responsibilities might be:
Compliance
Data Management oversight, including risk management, data security, data quality, etc.
Data Governance oversight, including data access policies, metadata oversight, etc.
Data Governance and Data Management
Data governance and data management are two distinct but complementary disciplines that are essential for effective data stewardship. Data governance focuses on establishing policies, procedures, and standards for managing data assets, ensuring compliance with regulatory requirements, and aligning data management practices with business objectives. It provides the framework for data stewardship, defining the roles and responsibilities of data stewards and setting the standards for data quality, security, and usage.
On the other hand, data management involves the practical implementation of these policies and procedures, encompassing activities such as data collection, storage, processing, and analysis. Data management ensures that data is available, accurate, and accessible when needed, supporting the organization’s operational and analytical needs.
Data stewardship bridges the gap between data governance and data management, ensuring that data governance policies are effectively implemented and that data management practices adhere to established standards. By working closely with both technical teams and business users, data stewards help to maintain high standards of data quality and integrity, supporting data-driven decision-making and enhancing the overall effectiveness of the organization’s data governance program.
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Emerging Trends in Data Stewardship
As data stewardship continues to evolve, data stewards are increasingly involved in adapting to new technologies, regulations, and collaborative practices. By implementing data privacy and governance frameworks that protect sensitive data across an organization, data stewards now play a crucial role in ensuring compliance with evolving data regulations, like GDPR. Collaboration between departments and business users has become essential in managing data assets, with organizations moving towards decentralized models where multiple data stewards oversee distinct data domains. These decentralized models empower data users and data professionals to manage data quality and governance policies more effectively.
Data stewards are also becoming integral to internal DataOps teams, contributing to continuous data lifecycle management and enhanced data workflows. This shift aligns with the industry trend of embedding data stewardship practices into daily operations to leverage data-driven decision-making. As data stewardship focuses more on team collaboration, data professionals are encouraged to maintain data integrity, enforce data governance policies, and ensure high-quality data across all stages of the data lifecycle.
Common Data Steward Activities
Data stewards work within a defined domain. Within that the context data stewards will:
Define / describe business data elements
Define data domain values
Establish and validate data quality rules
Identify and help resolve data quality issues
Translate regulatory rules into data polices, rules, and standards
Help develop data domain business rules (algorithms, calculations, processing requirements, etc.)
Define data security requirements
Promote the use of accepted data definitions, common reference data and sound data usage practices
Create and maintain business metadata definitions, domain values, business rules, etc.
Represent domain for the enterprise, site or project
Provide accountability for domain data, ensuring timely and consistent collection of data
Guide and advise others on meaning and use of data
Determine retention period of data
Provide data understanding and usage expertise to projects (development and enhancement)
Selecting a Data Steward
Selecting the right data steward is a very important job. Typically, data stewards come from an organization’s Subject Matter Experts (SME) and data analysts from the business community. Just because a person is expert on the data of an organization, does not mean that they have even a remedial level of data stewardship knowledge. Therefore, new data stewards require significant training and mentoring to be successful.
Key Responsibilities of Data Stewards
Data stewards play a pivotal role in effective data stewardship, working across various business functions to uphold the organization’s data governance framework. Their responsibilities encompass a range of activities essential for maintaining data quality, data standardization, and governance over the entire data lifecycle. Key responsibilities include:
Liaison Role : Data stewards act as intermediaries between technical teams and business executives, ensuring that data governance roles align with business objectives and support the organization’s data governance framework.Data Audits : Regular audits are conducted to ensure data quality, data accuracy, and integrity across data assets, utilizing data quality metrics and policies and procedures.Lifecycle Management : Monitoring data from creation to deletion, data stewards contribute to effective data lifecycle management and enforce standards for data handling and data-related security.Collaboration with Data Owners : Data stewards work closely with data owners and data analysts to establish data lineage, oversee data cataloging, and maintain high data quality across the organization’s data assets.Skill Development : To support data literacy and meet the evolving needs of data governance frameworks, data stewards continuously update their knowledge in data catalog management, data dashboards, and emerging best practices.
Technical Data Steward Skills
Data stewards need sound technical skills for their job. These skills include:
Basic understanding of data modeling (conceptual, logical and physical)
Basic understanding of DBMS used in organization
Basic understanding of data and information concepts
Facilitation skills
Tool usage as necessary
Technical writing and presentation skills
Non-Technical Data Steward Skills
The data steward, the non-technical skills are the most important to have. The need to have:
Solid understanding of the business
Excellent communications skills (written and oral)
Objectivity
Creativity
Diplomacy
Ability and willingness to work as part of a team
Ability to function independently
Well-respected knowledge of the domain
Well-respected knowledge of the overall organization
Conclusion
Knowing the industry best practices for building data stewardship roles can help any organization in developing its data stewardship program according to accepted and proven practices. Following these practices can help to ensure the success of a data governance program.
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