Case Study 12.2.2015

Improving Data Quality to Eliminate $4M in Discrepancies

Trexin helped a top five healthcare Payer find and eliminate data quality issues from a heavily customized software platform that were resulting in over $4M in cash reconciliation discrepancies.

Business Driver

Our Client, one of the nation’s top 5 healthcare Payers, was experiencing a large number of data quality and reconciliation issues around Retail Membership, Claims, and Member Eligibility. These issues were cropping up as a result of the implementation of a new software package that had required extensive customization to meet their needs. The vendor that performed the customization did not provide our Client’s staff the knowledge or resources needed to effectively support the tool, so the Executive Director of Retail Operations asked Trexin to do the following:Infographic

  1. Itemize the issues and conduct root cause analysis.
  2. Debug and correct data quality problems at the source.
  3. Provide documentation and methodologies to enable our Client’s own staff to resolve future data quality issues themselves.


After establishing a thorough understanding of the business rules and requirements related to the cash reconciliation process, our team of data quality experts began to analyze and compare the data using SQL, DB2, Teradata, and other contextually relevant technologies. This analysis made it possible for the team to quickly identify the root causes of the data quality issues.

We then worked closely with our Client’s business team to design solutions and coordinate implementation. We provided the tools, analysis, and reporting that supported the successful management of the project, and we thoroughly documented the entire methodology.


After following Trexin’s lead to find and eliminate data quality issues, our Client calculated that nearly $4M in cash reconciliation discrepancies had been corrected. Our Client now has a proven methodology for discovering the root causes of any new data quality issues they may experience and the tools needed to eliminate them.

Tagged in: Analytics, Healthcare & Life Sciences, Optimized Operations
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