Case Study: ADP scales enterprise data quality and governance with Anomalo
Key results
The challenge
Before adopting Anomalo, ADP managed data quality with a manual, rules-based process that involved 700 individually created checks. The approach was cumbersome and inefficient and became untenable as data volume and variety expanded, limiting the company's ability to support data science and generative AI initiatives.
The solution
ADP integrated Anomalo's automated, machine-learning-powered data quality checks with its Databricks environment and connected the results to Alation for governance visibility. Data stewards were empowered to manage data quality rules directly under a federated governance model.
“We couldn't get to where we wanted to go on our data quality journey and unlock the power of our data for our data scientists and our Gen AI initiatives with the traditional data quality rule-by-rule approach. We needed something smarter, more powerful, and more automated, that's where Databricks and Anomalo come into play.”
KHKristin HlavinkaDirector - Enterprise Data Governance, ADP
The results, in context
ADP expanded from 700 manual checks to over 16,000 daily machine-learning-powered validations within months. The Data Governance team reduced the share of time spent on data quality issues from 70% to 30%, and the Anomalo-Alation integration gave teams greater confidence in the data they access.