It becomes difficult to maintain and manage high quality data without policies, strategies and dedicated efforts across business functions. Implementing a successful data governance program has several technical challenges. First, companies need to reliably integrate data sources and implement data quality measurement and improvement features. Second, companies need to establish, monitor and improve key quality and performance metrics that support data governance principles. Finally, collaboration is required so teams of people from different departments can work together on - and be accountable for -data quality issues.

All of these disciplines are required; otherwise there is a risk of low quality data that can impede business performance.

How Vision Helps

Database Patrol – A Data Quality Control Process

The Vision data mart is a collection of information from multiple structure and unstructured sources. It also permits adjustments and enrichments in an auditable environment. This increases the risk and issues of data integrity.

Vision has been strengthened with an internal policing program that scans all the key risk areas for referential integrity and redundancies in data. The process is automated and examines more than 80 key referential integrity points for issues and errors.

This process ensures data quality and assists with the data cleansing process.


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