You need to take every opportunity to tweak your data governance framework to fit your business goals. Discussions among data operatives and business users give birth to crucial areas to drive optimization.
If pursuing a new use case or bringing a steward on board makes a positive impact, you should be all for it
Now, let’s go over some best practices for optimizing
Minding the fluid and iterative
nature of data quality
Businesses try to improve data quality to streamline mergers and acquisitions; reduce IT and business costs; enhance customer satisfaction; increase operational efficiency, among others. Data quality is linked to such core business imperatives and use cases. All these aspects of business are subject to change and so is the plans and policies around data quality.
Firstly, data quality continues to improve as a DQ process is implemented. As the stewards become familiar with the data and the processes of standardization and deduplication are through, they start identifying scopes of modification in data dictionaries and business rules.
Secondly, data quality changes with the introduction of a new data source, or new record updates. These changes need to be included to the iterative cycle of data quality management and they bring about change in data quality.
The enterprise data catalog may require upgrades due to various reasons including unavailability of a compatible scanner in the old version, a new feature requirement, or a critical bug.
The upgrade requires prior planning and assignment of roles and responsibilities based on the type of the upgrade - in-place upgrade, parallel upgrade, or clone upgrade.
It is executed through meticulous practices and through tests post the upgrade.
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