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

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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 Governance Policies provide strategic and operational direction to the enterprise. The policies recognize that corporate data is a critical corporate resource and will be managed as such. Whilst they are at an overarching level, these policies are at the next level down from Guiding Principles and can be translated into something than can be measured and implemented. Data governance policies should be directly linked to one or more of the Guiding Principles.

Data Stewards and Data Owners will be the key personnel developing these policies; the Data Governance Steering Committee will be responsible for review and sign-off.

As with any corporate policies, the success of data governance policies is dependent on the ownership structures, processes and standards that support the implementation of the policy. These help ensure that a data governance capability is being delivered. Without these in place, an organisation may struggle to demonstrate compliance with its own policy. This is of particular relevance for clients who are subject to regulatory compliance. In this case, regulators may require policies to be in place, but those policies can be a risk to an organisation who cannot demonstrate evidence of the policy being implemented. Taking data quality as an example, successful implementation of data quality policies requires data quality issue resolution and escalation processes to be established. Data ownership and stewardship roles are also critical to implementing these processes, and ultimately to the success of the policy.

Contents

Examples

Data Governance Policies are required for Data Governance Program Management, Master Data Management and Data Quality Management:

Example for a Data Governance Program Policy

A Data Governance Program policy should address the maintenance and continuous improvement of the data governance program. This includes:

  • Maintenance of the data governance strategy and framework – including organisation structures (roles & responsibilities), policies, data standards and processes
  • Maintenance of the Data Governance Council charter
  • Maintenance of Key Data Element (KDE) Definitions - including business definitions, business rules, usage definitions and related business processes
  • Continuous improvement through the identification and initiation of projects or activities that improve and expand the existing data governance program.

Example for a Data Quality Management Policy

A clearly articulated enterprise-wide data quality management policy provides the strategic and operational framework for data quality management and data quality improvement.

The policy should address requirements for data quality standards, data quality monitoring, issue resolution and continuous improvement.

Example for a Master Data Management Policy

A Master Data Management policy guides the approach for managing the capture, classification, security, access, distribution, retention and maintenance of master data.

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