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Data Stewardship

Introduction

Agency data is an asset that must be defined, managed, governed and stewarded. The stewardship of agency data is fundamental to ensure quality reporting capability, compliance and auditability. With a robust data stewardship program in place and a clear understanding of value in improving data quality, the agency will be able to reduce costs through standardization, reuse of data and stakeholder satisfaction.

What is data stewardship?

Data stewardship is formal accountability for the management and oversight of agency data assets to help provide users with high-quality data that is easily accessible in a consistent manner. It is the operational aspect of an overall Data Governance program. Data stewardship helps to cultivate knowledge about data, what the quality of that data is, what the quality needs to be, and how to improve data quality to get more value out of it.

Who are data stewards and how are they identified?

Data stewards are the employees, identified by the data owner, who are responsible for the data related to a specific program area or division. A data steward is a business subject matter expert designated and accountable for assisting with analysis, quality and use of the data as well as documentation of appropriate metadata. Anyone who defines, produces or uses data as part of their job is a data steward and a key player in a successful data stewardship program.

What do data stewards do?

  • Identify issues and necessary changes to data systems.
  • Evaluate issues and identify possible solutions.
  • Escalate data issues and solutions to the Data Governance Committee, as needed.
  • Address any additional questions or issues brought by the Data Governance Committee.
  • Collaborate with data stewards and IT providers to implement approved changes.

Goals of data stewardship

  • Ensure key data elements and associated rules are identified, defined and documented.
  • Clearly document decisions and publish them to all stakeholders.
  • Create and use supporting tools such as a metadata repository and business glossary.
  • Ensure processes and procedures are written, approved and being used to:
  • Identify key business data elements.?
  • Collect, review and approve metadata.
  • Log, analyze, prioritize and remediate data and data quality issues.
  • Support projects.
  • Manage domain data.
  • Identify and analyze data quality improvement opportunities.
  • Document work done to remediate data issues.

Fact Sheet