Shifting Gears: How O’Reilly Auto Parts Modernized its Data Environment with Alation and Snowflake

At Snowflake Summit 2025, Dwayne Foresee, a key leader in the Data Strategy team at O’Reilly Auto Parts, took the stage to share the company’s transformative data journey. Faced with outdated infrastructure and growing business demands, O’Reilly didn’t just migrate to the cloud—it reimagined its data environment. By treating metadata not as a byproduct but as a strategic asset (and by leveraging Alation and Snowflake in tandem), the company laid a foundation of trust, efficiency, and AI readiness.

With over 6,300 stores and 93,000 team members across the U.S., O’Reilly Auto Parts is more than just a retailer of car parts, it’s a mission-critical service provider. “Your car’s not working, it affects everything: getting to work, taking the kids to school, going on vacation,” said Foresee. The company supports both individual customers and professional auto shops, and its complex supply chain and massive operational footprint generate vast volumes of data daily.

To maintain its reputation for speed and service, O’Reilly knew it needed to treat data like the critical asset it is.

Key challenges for the O’Reilly data migration

When O’Reilly’s legacy data appliance reached end-of-life in 2023, the team faced more than a migration—it faced a reckoning. Years of accumulated, unorganized, and siloed data from various business lines created what Foresee called “a data hoard.” A forklift approach to moving this into a new warehouse—Snowflake—would only amplify the problem.

“If your garage is full of junk, you don’t fix it by just building a bigger garage,” he explained.

Key challenges included:

• Lack of data ownership and stewardship.

• Absence of governance and standardization.

• Low visibility into how data was being used—or misused—across the business.

• A culture of unquestioned reliance on outdated reports and queries.

• Need to ensure cost-effective use of Snowflake’s powerful but consumption-based architecture.

These challenges set the stage for a strategic, metadata-driven migration of O’Reilly’s data foundation.

Objectives: Make data governed, trusted, and AI-ready

The team recognized that a successful data modernization journey would require a shift in mindset, not just in tooling. Their key objectives became clear:

• Establish data ownership by assigning stewards and business owners to every data asset.

• Catalog and document data assets with clear, consistent metadata.

• Build trust and clarity around business-critical KPIs and reports.

• Promote data literacy, empowering teams to ask the right questions and make confident decisions.

• Drive efficiency and cost control in Snowflake through metadata-driven insights.

• Enable AI readiness by laying a strong data foundation for the future.

Armed with this roadmap, O’Reilly made a critical strategic move.

Implementation: Alation First, then Snowflake

Before spinning up their Snowflake environment, the team deployed Alation.

“We didn’t want to move without understanding what we had,” Foresee said. “Alation was the starting point.”

This deliberate sequencing was key. Cataloging enterprise data is often recommended as the first step in a cloud migration because it offers a unified view of the data landscape. By surfacing usage patterns, quality issues, and ownership gaps before migration, organizations like O’Reilly reduce risk, shrink costs and timelines, and avoid transferring technical debt into a modern platform.

Metadata-driven migration

First, Foresee’s team connected Alation to O’Reilly’s legacy systems to ingest query logs and uncover how data was being used. This insight helped the team:

• Identify high-use, high-value data assets.

• Surface redundant, outdated, or low-value data for de-prioritization.

• Create a prioritized roadmap for Snowflake migration.

When the Snowflake environment went live in Spring 2024, Alation continued to play a central role. Each asset moved to Snowflake was:

• Owned and validated by a designated business owner.

• Documented in the data catalog with descriptions, classifications, and lineage.

• Reviewed for compliance and accuracy before being marked production-ready.

This methodology helped O’Reilly avoid chaos and instill confidence in data producers and consumers alike.

Custom workflows, powerful filters

O’Reilly used Alation’s customization features to build filterable dashboards by business unit, Scrum team, or project wave. This enabled:

• Targeted task management for data stewards.

• Simplified review and approval processes.

• Incremental onboarding of new data stewards with limited workloads.

“The beauty of Alation,” said Foresee, “is that you can save these searches and assign them to stewards, making it approachable and manageable.” This tailored approach ensured scalability without overwhelming teams.

Lineage, transparency, and AI enablement

Alation’s ingestion of query logs and automatic lineage building was a revelation: “Once we connected it, it was like—wow. Look at all this information!”

This capability has:

• Enabled transparent reporting from source systems (e.g., DB2 on IBM iSeries) through Snowflake transformations.

• Facilitated data discovery with sample previews—no queries needed.

• Supported AI-readiness by identifying trusted queries used frequently and consistently across the business.

O’Reilly also brought in dbt transformation logic, giving analysts and engineers a clear view into the ETL processes and enabling comprehensive governance across the data lifecycle.

Results

In less than a year, O’Reilly has dramatically advanced its data culture maturity:

• Data ownership is formalized, with each Snowflake object assigned a steward and owner.

• Business adoption is growing, thanks to strong data literacy efforts and intuitive Alation interfaces.

• Cost control is improving—by identifying and optimizing high-frequency queries.

• Metadata is centralized—KPI definitions, table descriptions, classifications, and lineage all live in Alation.

• AI foundations are in place—with trusted, documented, discoverable data forming the backbone for future intelligent applications.

“The real power,” said Foresee, “is that Alation is agnostic—it’s our source of truth, no matter where the data lives. We own the data, and we can integrate it wherever we need it.”

Key learnings from the O’Reilly journey

Building trust in data means going beyond tools—it requires answering the real questions data consumers ask every day. Questions about data accuracy, ownership, usage, compliance, and cost need to be easy to answer at the point of need. That level of clarity can’t happen without governance baked into the foundation.

At O’Reilly, implementing a data product mindset—with Alation at the core—made that clarity possible. Treating data as a product meant every dataset addressed critical questions and had a purpose, an owner, and clear documentation. This operating model brought accountability to the forefront and enabled visibility across systems, teams, and use cases.

Together, these practices didn’t just improve access—they drove trust, empowered decision-making, and unlocked new value from the data.

Conclusion: From auto parts to AI agents

O’Reilly Auto Parts isn’t just modernizing its data stack—it’s building a data-driven future. With Snowflake and Alation working in tandem, the company is positioned to:

• Expand use of AI agents that rely on verified, trusted data.

• Continue migrating key assets with stewardship and governance embedded.

• Democratize data use across the business with clarity, control, and confidence.

The takeaway? For organizations asking how to harness the full value of their data—especially amid a move to the cloud or a push toward AI—the journey starts not with the destination, but with the data itself.

“Catalog your data. Assign ownership. Build trust,” said Foresee. “That’s how you shift gears—and that’s how you win the race.”