What is Data Stewardship?

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Data StewardshipData Stewardship is:

“The most common label to describe accountability and responsibility for data and processes that ensure effective control and use of data assets. Stewardship can be formalized through job titles and descriptions, or it can be a less formal function driven by people trying to help an organization get value from its data.”

According to the Data Governance Institute:

“Data Stewardship is concerned with taking care of data assets that do not belong to the stewards themselves. Data Stewards represent the concerns of others. Some may represent the needs of the entire organization. Others may be tasked with representing a smaller constituency: a business unit, department, or even a set of data themselves.”

Some Data Stewardship Activities are:

  • Creating and managing core metadata.
  • Documenting rules and standards.
  • Managing Data Quality issues.
  • Executing operational Data Governance activities.
  • Set and manage guidelines around data.

Other Definitions of Data Stewardship Include:

  • “Manage data from a variety of sources, for a variety of uses.” (Amber Lee Dennis)
  • “Formalization of Accountability over the management of Data and Data-Related Resources.” (Robert S. Seiner)
  • Subject matter “experts for their departments and act as trustees of data, rather than owners of it. These people are visible, respected, and influential.” (Gartner)
  • “Work that makes a company’s data trusted, dependable, and high quality.” (David Plotkin)
  • “Responsibility for a set of data for the well-being of the larger organization, and operating in service to, rather than in , of those around us.” (USGS)
  • “Behavior in the valuation, creation, use, storage, archiving, and deletion of information, all with the objective of ensuring data quality control and integrity.” (University of Michigan)

Businesses Use Data Stewardship to:

  • Comply with regulations.
  • Reduce Risk: Data security and privacy.
  • Improve Processes: Data Quality and manage metadata.
  • Define policies and processes around data.
  • Define roles and responsibilities for data.
  • Improve data documentation.
  • Perform various types of data analytics with more accuracy and efficiency.
  • Share, protect, define, archive, access, and synchronize data.
  • Remediate data and data-related issues and problems.
  • Operationalize Data Governance.
  • Accomplish “consistent use of Data Management as an effective resource:
    • Efficient mapping of data between systems and technology
    • Lower costs associated with migration to Service Oriented Architecture (SOA).”

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