Open Framework, Information Management Strategy & Collaborative Governance | Data & Social Methodology - MIKE2.0 Methodology
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Assign Data Stewards Deliverable Template

From MIKE2.0 Methodology

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This article is a stub. It is currently undergoing major changes as it is in the very early stages of development and is only a placeholder. Please help improve MIKE2.0 by adding to this article.
This deliverable template is used to describe a sample of the MIKE2.0 Methodology (typically at a task level). More templates are now being added to MIKE2.0 as this has been a frequently requested aspect of the methodology. Contributors are strongly encouraged to assist in this effort.
Deliverable templates are illustrative as opposed to fully representative. Please help add examples to this template that are representative of the proposed output.

Data Stewards act as the conduit between IT and the business and accept accountability for data definition, data management process definition, and information quality levels for specific data subject areas. Data stewardship involves taking responsibility for data elements for their end-to-end usage across the enterprise.

Examples

Listed below is an example for Data steward roles:

Example for Data steward role responsibilities in an IT envionment

The Extended team roles and responsibilities for stewards are as below:
1. Business Process Stewards – Co-manage the business processes and responsible for:

  • Proposing the business data rules, definitions, processes, policies, procedures and standards.
  • Assessing business and/or system data change impacts to the business processes they steward.

2. Corporate Data Stewards – manage the business data processes and responsible for:

  • Maintaining the business data rules, definitions, processes, policies, procedures and standards.
  • Assessing business and/or system data change impacts to all (such as other applications/reports) related to the information they steward.

3. System Stewards – Co-manage the system processes and responsible for:

  • Analyzing and proposing systematic solutions for fixing and sustaining data quality issues.
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