Case Study: Global information provider cuts data-quality processing from 22 days to 7 hours with Acceldata
Key results
The challenge
A global leader in commercial data and analytics, managing data on over 600 million businesses across 250 markets, ran data-quality checks that were siloed across applications, creating inconsistencies and delayed resolution. Legacy tools required developers for rule updates, taking over a month per change, and the system struggled to scale to 500 billion-plus rows, raising processing costs and leaving customers to find data issues before internal teams did.
The solution
The provider deployed Acceldata's data observability platform for AI-powered anomaly monitoring with recommended fixes, scalable processing of 500 billion-plus rows, and self-service rule creation that let business analysts write and deploy custom rules in SQL, Python, or Spark. A reusable rules library allowed logical rules to be applied consistently across 30,000-plus sources from 220 countries, with auto-actions correlating insights across infrastructure, processing, and data layers.
The results, in context
The provider reduced data-quality processing time from 22 days to 7 hours and accelerated rule deployment 30x, cutting simple-rule deployment from over a month to under a day. It also cut data-quality issue identification time from 12 days to under 24 hours and applied rules consistently across more than 30,000 sources spanning 220 countries.