How to tackle the $15 million dollar bad data problem with end-to-end test automation

Digital transformation demands speed, but bad data is a dead weight that can hinder even the best-laid plans. Gartner estimates the average U.S. company loses $15 million a year because of bad data. Finding and fixing the root causes of bad data at the outset can save you significant time and resources and ensure sound business decision-making.

In this encore presentation of a recent Tricentis Roadshow presentation, you’ll hear Tricentis’ resident data quality expert Curtis O’Dell discuss:

• How to graduate from “stare & compare” data quality measures to an intelligent, automated approach
• The steps you can take to reduce risks by identifying the root causes of bad data faster
• Real-world examples that highlight how an automated approach can significantly reduce the timeline and cost of a data migration, as well as improve the trustworthiness of your data