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What is Data Stewardship?

By   /  November 13, 2017  /  No Comments

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.” (DAMA DMBOK).

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.”

Data Stewardship activities include:

  • Creating and managing core Metadata (DMBOK)
  • Documenting rules and standards
  • Managing Data Quality issues
  • Executing operational Data Governance activities
  • Set and manage guidelines around data (Robert S. Seiner)

Other definitions of Data Stewardship are:

  • “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
    • Privacy
  • Improve Processes
    • Improve Data Quality
    • Manage Metadata
  • Perform various types of Data Analytics with more accuracy and efficiency
  • Share, protect, define, archive, access, and synchronize data
  • Define policies and processes around data
  • Define roles and responsibilities for data
  • Improve data documentation
  • Remediate data and data-related issues and problems
  • Operationalize Data Governance (Amber Lee Dennis)
  • Eliminate or reduce miscommunication (MITRE)
  • 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).” (Experian Data Quality)

 

Photo Credit: enterlinedesign /Shutterstock.com

About the author

Michelle Knight enjoys putting her information specialist background to use by writing technical articles on enhancing Data Quality, lending to useful information. Michelle has written articles on W3C validator for SiteProNews, SEO competitive analysis for the SLA (Special Libraries Association), Search Engine alternatives to Google, for the Business Information Alert, and Introductions on the Semantic Web, HTML 5, and Agile, Seabourne INC LLC, through AboutUs.com. She has worked as a software tester, a researcher, and a librarian. She has over five years of experience, contracting as a quality assurance engineer at a variety of organizations including Intel, Cigna, and Umpqua Bank. During that time Michelle used HTML, XML, and SQL to verify software behavior through databases Michelle graduated, from Simmons College, with a Masters in Library and Information with an Outstanding Information Science Student Award from the ASIST (The American Society for Information Science and Technology) and has a Bachelor of Arts in Psychology from Smith College. Michelle has a talent for digging into data, a natural eye for detail, and an abounding curiosity about finding and using data effectively.

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