ADP’s Path to Enterprise-wide Data Quality and Data Governance

The Challenge

Scaling Data Quality in a Data-Driven World

Before adopting Anomalo, ADP struggled with a manual, rules-based data quality management process that was unable to keep pace with their data production. The process was not only cumbersome and inefficient, involving 700 individually created checks, but also became untenable as data volume and variety grew.

• ADP used a manual, rules-based approach for data quality, creating checks one by one.
• This process was inefficient and involved data stewards submitting rules and a centralized team programming them, followed by an iterative review.
• As data volume and variety increased, this manual method was no longer sustainable, prompting the need for a more scalable and intelligent solution.

The Solution

Automating Data Quality with Anomalo

Recognizing the limitations of their traditional data quality methods, ADP sought a new approach that could keep pace with their rapidly evolving data landscape. This led their team to partner with Anomalo to automate the detection and resolution of data issues.

By integrating Anomalo’s automated data quality checks with their Databricks environment, ADP was able to scale their data quality efforts exponentially. Within months, they had expanded from 700 manual checks to over 16,000 daily machine learning-powered validations.