Centralized Approach to Data Quality Increases Visibility into Process Bottlenecks for State Agency
A modern data vault and enhanced reporting set the stage for a large state agency to increase visibility into data quality issues. This modernization effort created a centralized data repository enabling the organization to be hyper data–driven and establish a data governance program to raise the standard of data quality to improve internal business processes and provide better customer service.
Ongoing data quality issues created bottlenecks in business processes and an overall lack of internal data trust.
Enable stronger data quality through identification and increased visibility into data quality issues to improve business processes.
Build a centralized dashboard to monitor data quality issues to enable a proactive approach to resolve data issues negatively impacting the agency.
The agency‘s complex financial calculations are dependent upon accurate data figures. Without a comprehensive program to identify issue patterns, underlying problems went unnoticed for long periods of time until they affected crucial processes. Many data quality issues were identified, but the records of these issues were divided between many systems, making it difficult to accurately scope the organization’s impact, decreasing confidence in the data for business users.
The new data vault created a centralized repository enabling our team to create a centralized data quality approach. UDig analyzed the agency‘s data fix logs and interviewed subject matter experts to identify data issues regularly encountered in their work. Working side-by-side with our client, the team conducted targeted data analysis, isolating all instances of these data quality issues into a series of discrete business rules. These rules were used as the basis for an interactive Tableau dashboard, allowing users to track data quality issues over time and target the largest data quality patterns. The visibility afforded by the dashboard enabled the agency to take a proactive approach towards the resolution of these issues and implement fixes before they affected the end–user. This proactive approach to data quality has allowed for increased visibility into process bottlenecks and improved data trust.
How We Did It
- Microsoft SQL Server