Data Quality Monitoring

A working prototype was ready within 10 days for the customer to rapidly assess efficacy with no disruptions to any underlying systems.

Non-intrusive detection and reporting of data quality issues [inaccuracy, duplication, inconsistency, incompleteness, incorrect classification], in high volume environment without data/technology standards. Apply inflight fixes as required

Business Problem

  • Data quality issues in a mission critical customer facing process was resulting in both regulatory and customer satisfaction issues at a Financial Services company.
  • High volumes of transactions across multiple systems, with different technology and data standards, was exacerbating the problem.
  • Data quality issues included data inaccuracy, duplication, inconsistency, incompleteness, incorrect classification.



  • The RAGE solution was designed to identity, report on data quality issues, across five stages of the end to end process.
  • The solution was introduced in a completely non-intrusive manner, intercepting data flows between transaction systems, with the eventual goal of automatically fixing known errors in-flight.
  • RAGE enabled a launch-and-learn approach, as requirements continued to evolve during and beyond the initial implementation.



  • A working prototype of was ready in 10 days for the customer to rapidly assess efficacy. No changes were made to any underlying systems.
  • Easy and rapid user managed implementation of new rules/models developed in statistical modeling tools.