Agents
CASE STUDIES
ADP Achieves Enterprise Data Quality & Governance with Anomalo
Key Results
16,000+
Daily, automated machine learning-powered checks
30%
(down from 70%) of time spent by Data Governance team on data quality issues
About ADP
A comprehensive global provider of cloud-based human capital management (HCM) solutions that unite HR, payroll, talent, time, tax, and benefits administration and a leader in business outsourcing services, analytics, and compliance expertise.
Tech Stack
Data Warehouse: 
Data Catalog: 
Size
10,000+ Employees
Industry
Human Resources Software
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.
The Results
Efficiency, Trust, and Empowered Data Users
ADP’s data quality transformation has yielded significant benefits across the organization:
- Scalable Monitoring – Automated data quality checks enabled ADP to proactively identify and resolve issues at scale
- Increased Efficiency – Reducing the time spent on data quality issues from 70% to 30%
- Federated Governance – Data stewards are empowered to manage data quality rules directly
- Trust in Data – Visibility provided by Anomalo’s integration with Alation provides confidence in data they’re accessing
Unlock the full case study to see how ADP scaled from 700 manual checks to 16,000+ ML-powered validations
Why was a smarter, more automated data quality solution needed for ADP's Gen AI initiatives?
A smarter, more automated data quality solution was needed for ADP's Gen AI initiatives because traditional rule-by-rule approaches were insufficient to unlock the power of data for data scientists and Gen AI. This insight comes from Kristin Hlavinka, Director - Enterprise Data Governance, who emphasizes the necessity of powerful, automated tools like Databricks and Anomalo to overcome these limitations.
Frequently Asked Questions
- 1 How did ADP achieve enterprise-wide data quality and governance?
-
ADP achieved enterprise-wide data quality and governance by implementing a comprehensive strategy. This involved integrating leading data platforms like Anomalo, Databricks, and Alation to ensure data reliability, accuracy, and proper oversight across their extensive enterprise data.
- 2 What specific challenges did ADP address with their data management solution?
-
The case study explores how ADP tackled common enterprise data challenges, focusing on improving data trust, ensuring data accuracy, and establishing robust governance policies. Their solution aimed to streamline data operations and enhance decision-making.
- 3 What role did Anomalo, Databricks, and Alation play in ADP's data strategy?
-
Anomalo provided autonomous data quality monitoring, Databricks served as the scalable data processing and analytics platform, and Alation facilitated data cataloging and governance. Together, these tools formed a cohesive system for ADP's data management success.
- 4 What best practices for enterprise data management can be learned from ADP's case study?
-
ADP's case study highlights best practices such as adopting an integrated data platform approach, prioritizing continuous data quality monitoring, and establishing clear data governance frameworks. These practices are crucial for achieving successful enterprise data management.