Scaling Data Quality: A Major U.S. Retailer’s Story

The Data Challenge Behind a Beloved Retail Brand

This well-known U.S. retailer operates many stores in the United States and generates billions in annual sales, supported by tens of thousands of employees. Every day, the company processes data from billions of items scanned at checkout. Layer on the rapid growth of digital marketplaces such as Instacart, DoorDash, and Uber Eats and the data picture becomes even more complex.

Each of these partners sends data back to the retail chain. But not all data is created equal. Maturity varies widely, and discrepancies, such as missing records, incorrect substitutions, or inconsistent fields, introduce downstream impacts on operations, customer experience, and financial accuracy.

Before modernizing on Databricks and Anomalo, these checks often meant manual, time-intensive monitoring, including staff reviewing numbers at 2:00 a.m. to ensure accuracy before reports went upstream.

Building Data Quality Into the Foundation

As part of its migration to Databricks, this major retail brand saw an opportunity: build data quality into pipelines from the start, rather than bolt it on later.

But developing and maintaining thousands of rules in-house wasn’t scalable—especially given the evolving nature of marketplace partnerships and internal systems.

“If we create all these rules, now we have to maintain all these rules… and when anything changes, you have to adapt. We didn’t want to spend our time on that.”
– Vice President, Data and AI Architecture, Major U.S. Retail Brand

That’s where Anomalo’s automated, machine-learning–driven data quality platform became a natural fit.

Automation + AI = Trustworthy Data at Scale

The U.S. retail chain uses a combination of Anomalo’s:

• Automated anomaly detection powered by unsupervised ML
• Rule-based checks for known, deterministic constraints
• No-code/low-code controls that business users can increasingly own

This mix ensures coverage for both the known and unknown issues in their data.

One example the retailer shared stood out: Anomalo surfaced unexpected duplicate keys in data from the retailer’s mobile app. It was a subtle issue that would have been difficult to find without automated detection.

By detecting issues early, the retail chain prevents downstream impact on reporting, ensures accuracy in financial reconciliation, and frees teams to focus on high-value work instead of data firefighting.

Empowering the Business to Own Data Quality

A key part of the retailer brand’s long-term vision is democratizing data quality. With Anomalo’s approachable interface, teams across the business—not only engineers—can participate in monitoring and validating their data.

“Data quality is everyone’s problem… We want savvy business users to help us all share the data quality burden.”
– Vice President, Data and AI Architecture, Major U.S. Retail Brand

This cultural shift, combined with automated tooling, is helping the retail chain scale data quality without overwhelming the data engineering team.

A Strong Partnership with Databricks

Anomalo is built to complement Databricks deeply, and Databricks has invested strategically in Anomalo to help customers modernize their data architectures with reliability at the core.

This retail chain’s evolving platform now includes:

• Databricks as a unified, scalable data and AI foundation
• Anomalo providing automated, intelligent data quality monitoring
• A growing culture of shared responsibility for data trust

This combination enables this major retail brand to accelerate analytics and AI initiatives with confidence.

Learn More

The journey of this major U.S. retail chain is a powerful example of what’s possible when organizations pair a modern data platform with intelligent, automated data quality.

Want to see how Anomalo can help your organization? Sign up for a free demo today.