Tom Redman
The Data Doc, President
Data Quality Solutions
The Business Case for Attacking Data Quality Pro-Actively
By now everyone knows that AI succeeds or fails on the quality of answers/inferences/predictions it returns. (Poor) data quality also gets in the way of day-in, day-out work and good decision-making. The usual response is to try to find the errors and clean them up. It is time-consuming, expensive, and frustrating. And plenty of errors leak through. No wonder people don’t trust data.
Fortunately there is a better way: Pro-actively finding and eliminating the root causes of all those errors! Saves time and money. Builds trust. And people like the work!
In this workshop we’ll craft the business case for data quality by comparing “clean-up” versus “eliminating root causes.” The goal is to help participants grow increasingly intolerant of bad data and start addressing the issues pro-actively.
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