data steward types

Data stewardship roles can be segmented and categorized in multiple ways, depending on their responsibilities and required skills, as well as the organization’s structure, industry, goals and objectives and its data management needs. There is also an argument to be made that everyone is a data steward, but for the scope of this article I would like to argue there are 4 different types of data stewards for which your own organization might have different titles or variants for.

1.     Data object data steward

Description: This role manages reference data and attributes of one business data entity.

Synonym: Domain data steward

Example: Customer data steward. Reference data and attributes managed by this steward: company hierarchy, address, industry code, contact information, finance data. 

Key characteristics and considerations:

  • The role needs to reaches across functional lines and needs  to establish a cross-department team of subject matter experts
  • The most common types of stewardships, yet often most difficult to implement especially in decentralized organizations
  • Support them by following the best practices for managing reference data
  • Requires strong executive sponsorship

2.     Business data steward

Description: Manages the critical data, both reference and transactional, created or used by one business function.

Synonym: Functional data steward

Example: Sales or marketing data steward, business or data analyst

Key characteristics and considerations:

  • They are key representatives in a specific business area that is responsible for quality, use, and meaning of that data in the organization
  • One of the easiest functions to implement in a highly autonomous company
  • Effectiveness can be more easily measured by a direct business unit process metric
  • Gets challenging where the data is shared between several business units. This is where a centralized data governance organization is needed to intervene. Read more on the data governance operating models.

data steward types

3.     Process data steward

Description: Manages all data across one business process.

Synonym: N/A

Example: Lean specialist

Key characteristics and considerations:

  • Works across multiple data domains
  • Need strong cross-process governance in order to be successful
  • Often this person is part of the process improvement team
  • Interacts regularly with business unit data stewards

4.     System data steward

Description: Manages the data for one or more IT systems.

Synonym: Technical data steward

Example: Enterprise data warehouse architect, MDM practitioner

Key characteristics and considerations:

  • Best to ask how the data is created, transformed, stored, and moved in technical systems
  • Good place to start if no formal stewardship program in place


These are the four types of data stewards, though you might choose to categorize them differently or have combinations of those described above. It’s good to note that the data steward’s scope and role are subjective to the organization’s culture, data governance program, available resources and priorities.

  • What is your understanding of Data Domain Owners?

  • I acknowledge that this article was published in 2018. I have heard recently about ‘everybody is a data steward’ in a context that if a person is accountable for the data that he/she produces or defines. What is you thoughts on this subject and where do ‘they’ belong to by the types of data steward?

    • Hi Bas,
      There are indeed two different views of this. Bob Seiner for example is mentioning that “everyone is a data steward” in the sense that everyone has something to do with data (either because they produce it, define it, maintain it, use it, or benefit from data) and as a result they have a shared responsibility to take care of this data.
      While I don’t disagree with that, I think that if that culture isn’t there, then you need to have clear data stewardship roles and add in data stewardship responsibilities within the job descriptions.

      You might also be interested in this video:

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    About the author 

    George Firican

    George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

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