A data steward is a role within an organization responsible for utilizing an organization's data governance processes to ensure fitness of data elements - both the content and metadata. Data stewards have a specialist role that incorporates processes, policies, guidelines and responsibilities for administering organizations' entire data in compliance with policy and/or regulatory obligations. A data steward may share some responsibilities with a data custodian.
The overall objective of a data steward is data quality, in regard to the key/critical data elements existing within a specific enterprise operating structure, of the elements in their respective domains. This includes capturing/documenting (meta)information for their elements (such as: definitions, related rules/governance, physical manifestation, related data models, etc. With most of these properties being specific to an attribute/concept relationship), identifying owners/custodians/various responsibilities, relations insight pertaining to attribute quality, aiding with project requirement data facilitation and documentation of capture rules.
Data stewards begin the stewarding process with the identification of the elements which they will steward, with the ultimate result being standards, controls and data entry. The steward works closely with business glossary standards analysts (for standards), with data architect/modelers (for standards), with DQ analysts (for controls) and with operations team members (good-quality data going in per business rules) while entering data.
Data stewardship roles are common when organizations attempt to exchange data precisely and consistently between computer systems and to reuse data-related resources. Master data management often[quantify] makes references to the need for data stewardship for its implementation to succeed. Data stewardship must have precise purpose, fit for purpose or fitness.
Data Steward Responsibilities
A data steward ensures that each assigned data element:
- Has clear and unambiguous data element definition
- Does not conflict with other data elements in the metadata registry (removes duplicates, overlap etc.)
- Has clear enumerated value definitions if it is of type Code
- Is still being used (remove unused data elements)
- Is being used consistently in various computer systems
- Is being used, fit for purpose = Data Fitness
- Has adequate documentation on appropriate usage and notes
- Documents the origin and sources of authority on each metadata element
- Is protected against unauthorised access or change
Responsibilities of data stewards vary between different organisations and institutions. For example, at Delft University of Technology, data stewards are perceived as the first contact point for any questions related to research data. They also have subject-specific background allowing them to easily connect with researchers and to contextualise data management problems to take into account disciplinary practices.
Types of data stewards
Depending on the set of data stewardship responsibilities assigned to an individual, there are 4 types of data stewards typically found within an organization:
- Data object data steward - responsible for managing reference data and attributes of one business data entity
- Business data steward - responsible for managing critical data, both reference and transactional, created or used by one business function
- Process data steward - responsible for managing data across one business process
- System data steward - responsible for managing data for at least one IT system
Benefits of data stewardship
Systematic data stewardship can foster fitness through:
- Consistent use of data management resources
- Easy mapping of data between computer systems and exchange documents
- Lower costs associated with migration to (for example) Service Oriented Architecture (SOA)
Assignment of each data element to a person sometimes seems like an unimportant process. But many groups[which?] have found that users have greater trust and usage rates in systems where they can contact a person with questions on each data element.
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Delft University of Technology (TU Delft) offers an example of data stewardship implementation at a research institution. In 2017 the Data Stewardship Project was initiated at TU Delft to address research data management needs in a disciplinary manner across the whole campus. Dedicated data stewards with subject-specific background were appointed at every TU Delft faculty to support researchers with data management questions and to act as a linking point with the other institutional support services. The project is coordinated centrally by TU Delft Library, and it has its own website, blog and a YouTube channel.
Data Stewardship Applications
A new market for data governance applications is emerging, one in which both technical and business staff — stewards — manage policies. These new applications, like previous generations, deliver a strong business glossary capability, but they don't stop there. Vendors are introducing additional features addressing the roles of business in addition to technical stewards' concerns.
Information stewardship applications are business solutions used by business users acting in the role of information steward (interpreting and enforcing information governance policy, for example). These developing solutions represent, for the most part, an amalgam of a number of disparate, previously IT-centric tools already on the market, but are organized and presented in such a way that information stewards (a business role) can support the work of information policy enforcement as part of their normal, business-centric, day-to-day work in a range of use cases.
The initial push for the formation of this new category of packaged software came from operational use cases — that is, use of business data in and between transactional and operational business applications. This is where most of the master data management (MDM) efforts are undertaken in organizations. However, there is also now a faster-growing interest in the new data lake arena for more analytical use cases.
- Metadata registry
- Data curation
- Data element
- Data element definition
- Representation term
- ISO/IEC 11179
- Universal Meta Data Models, by David Marco and Michael Jennings, Wiley, 2004, page 93-94 ISBN 0-471-08177-9
- Metadata Solution by Adrinne Tannenbaum, Addison Wesley, 2002, page 412
- Building and Managing the Meta Data Repository, by David Marco, Wiley, 2000, pages 61–62
- The Data Warehouse Lifecycle Toolkit, by Ralph Kimball et. el., Wiley, 1998, also briefly mentions the role of data steward in the context of data warehouse project management on page 70.
- Developing Geospatial Intelligence Stewardship for Multinational Operations, by Jeff Thomas, US Army Command General Staff College, 2010, www.dtic.mil/dtic/tr/fulltext/u2/a524227.pdf.
- NewMedia Centre (2018-05-16), 1 Data Stewardship at the TU Delft V2, retrieved 2018-06-12
- "Understanding the different types of a data steward - LightsOnData". LightsOnData. 2018-06-13. Retrieved 2018-06-20.
- Teperek, Marta; Cruz, Maria J.; Verbakel, Ellen; Böhmer, Jasmin K.; Dunning, Alastair (2018-01-22). "Data Stewardship – addressing disciplinary data management needs". Open Science Framework. doi:10.17605/OSF.IO/MJK9T.
- "Data Stewardship". TU Delft. Retrieved 2018-06-12.
- "Data Stewardship". Open Working. 2018-02-13. Retrieved 2018-06-12.
- "Data Stewardship TU Delft". YouTube. Retrieved 2018-06-12.
- "The Forrester Wave™: Data Governance Stewardship Applications, Q1 2016". www.forrester.com. Retrieved 2016-12-20.
- De Simoni, Guido (15 April 2016). "Market Guide for Information Stewardship Applications". www.gartner.com. Gartner.