DATA POLICY LIFE CYCLE AND MANAGEMENT

Data Policy Life Cycle and Management This suggests that operational data governance imposes a life cycle for the definition and implementation of data policies, Fig shows broad overview of this life cycle.

By necessity, operationalizing compliance for data policies involves four stages:

1. Policy definition and approval:

• Business analysts must always be alerted to the introduction of business policies that imply a need for data governance. \
• Business policies can be imposed from outside the organization, such as regulatory compliance, industry standards, or extra-enterprise systemic interoperability requirements. 
• Business policies may also be imposed internally, based on generally accepted operating principles, compensation/benefit programs, supplier management practices, contractor management, among other examples.

2.Policy implementation

The data and system analysts will then review the business processes to assess where the

corresponding data rules must be asserted. 

These locations must be documented, as the

developers develop services for inspection that can be directly embedded within the business

processes.

 At the same time, the data stewards facilitate the documentation of a data quality

service level agreement that captures the data rules, acceptability thresholds, and the business data producers and consumers who are parties to the agreement in preparation for

enforcement.

3.Enforce compliance

When data issues are identified, they are logged with an incident reporting and tracking system, and the data steward is instructed to analyze the root causes and develop a remediation plan. 

• The data compliance metrics are collected and can be communicated through a data compliance portal or dashboard. 
• If issues are not resolved in accordance with the data policy, they areescalated as described in the service level agreement. 
• The data stewards’ ability to ensure compliance is continuously measured and can be reported to the members of the data governance board.

4.Maintenance – Business policies are not immune to change, and accordingly, if there are changes then they must be reviewed to determine if there is a need to modify the associated data policies. If so, the draft changes must be submitted for review and approval to the data governance board, and any agreed-to changes have to be communicated and deployed accordingly.

Conclusion Technology - Support for Data Policy Management

When reviewing the processes described in this paper, it becomes clear that the success of implementing a data governance program is critically dependent on the organizational structure of the data governance board, the policies and processes for operationalizing the decisions of that data governance board, and the techniques and methods supporting both.

This is facilitated through tools that support the full life cycle associated with defining, approving,

communicating, and fully integrating data policy compliance throughout the application infrastructure.

These tools should support data policy management through:

• Data policy definition;

• Metadata management;

• System impact analysis;

• A centralized repository for sharing information about data policies;

• Interoperability with existing tools;

• Documentation of role definitions and associated procedures;

• Documentation of the terms of Data Quality SLAs;

• Guidance for operational roles based on defined policies;

• Services for measurement and monitoring of compliance to data quality rules;

• Preset reports reflecting compliance with data policies; and

• Performance management in the context of expectations defined in data quality SLAs.