Case Study: Fortune 100 Financial Services Organization reduces data quality errors from 100,000 to 12,000 per cycle with Infogix

Automating Data Quality Reduced Errors from 100,000 to 12,000 per cycle

A Fortune 100 Financial Services Organization needed to improve data quality for retirement services and reduce the risk caused by inaccurate, slow-to-arrive data from asset managers and third-party sources. Traditional spot-check approaches were too reactive, allowing bad data to spread through forecasting, reporting, accounting, and data warehouse systems. Infogix and its Enterprise Data Analysis Platform were used to address the issue at the source.

Infogix implemented continuous, end-to-end data quality monitoring with automated business rules, real-time reconciliation, and checks on incoming and system-to-system data. The solution validated 100% of transactions and gave managers visibility into error trends and root causes. As a result, errors dropped from 100,000 to 12,000 per cycle, while the organization reduced risk, improved operational efficiency, and increased confidence in cash flow, valuation, and reporting data.