Data Stewardship

For most organizations, their data has been captured using different systems and applications. For some, acquisitions have increased the number of silos in which data resides. Therefore, duplications and variations of information are inevitable.

The matching and linking feature uses algorithms that automatically identify duplications and help resolve multiple entries into a single, accurate record.

Data Stewardship is required for data implementation and data management to succeed.

Data Stewardship is a key part of any data governance program which needs the right combination of processes, technology and people in place to be effective.


What are the benefits of data stewardship?

The benefits of appointing a data steward can include:
• 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).

Transforming data from a multitude of systems into one single, accurate record is a critical part of any master data management solution. In addition, the master data matching and linking feature will ensure you.

Data Stewardship

  • Data Management- Establish and maintain data quality and integrity through the following, in accordance with the policies and procedures laid out by the data governance management.
  • Data validation and profiling (in conjunction with data quality team)
  • Business rule conformance and data model validation
  • Data exception resolution
  • Business Metadata Management – Serve as subject matter experts (SMEs) to answer metadata requests (access and interpretation) for business metadata from the data governance specialists and other requestors.
  • Data Definition Management – Develop, enhance, manage and explain the business data definitions in the data steward’s domain.
  • Data Stakeholder and Owner Identification – Identify the stakeholder(s) and owner(s) of data management and the implementation of data-oriented policies by stakeholders.
  • Data Usage and Access Management – Oversee data access and ensure usage policies are understood and approved, in conjunction with other teams (e.g. data security, information architecture, etc.)
  • Data Policies Violation Management – Resolve violation of data governance policies and help teams across the enterprise take appropriate corrective action, document and communicate decisions to ensure that policies are followed in future.
  • Data Change Management – Manage changes to data definitions, usage, access, policies, and administration in compliance with Data Governance standards and practices.