From Legacy SAP BO to Snowflake & Power BI in Record Time with Data Products:How a Leading Automotive Manufacturer Used One Data to Modernize Quality Reporting on Production-Line Data

For years, the company’s quality teams relied on SAP BO for critical manufacturing metrics, including their key quality reports. But with support ending, they faced a major challenge: migrating years of embedded business logic, maintaining report continuity for hundreds of users, and transforming siloed information into accessible data products.

Business users did not know the source of their report data. It was also unclear which reports in SAP BO were still needed, creating uncertainty across the organization. Making matters worse, the teams providing the data to the apps did not know what depended on their data, creating significant risks for any system changes. There were no shared understanding and no trust in the data.

These circumstances, while also keeping daily operations running, made a migration highly challenging.

One Data provided a structured, accelerated approach to migration: combining built-in automation with continuous alignment between business and technical teams to move production-line data from SAP BO into Snowflake. Rather than relying on manual development, One Data’s software platform automated key steps of the process from requirements capture all the way through to code generation.

The Use Case Builder was the centerpiece of this automated approach. Business users worked directly within the One Data platform to scope and define the requirements. From there, the platform took over: it automatically translated business requirements into development-ready specifications and generated the basis code, which was then pushed to GitLab and Snowflake via dbt, without manual coding effort. By combining automation with close alignment between business requirements and technical execution, the traditional back-and-forth was eliminated.

Crucially, the team used One Data to establish shared standards and templates that everyone, business users and developers alike, could work with consistently. Because these standards were embedded in the platform’s automated workflows, every new deliverable was generated with the same structure, quality rules, and governance documentation from day one. This combination of standardization and automation was one of the biggest accelerators of the entire migration.

The team adopted an agile, product-oriented approach, with One Data experts working side-by-side daily with the automotive company’s team. Instead of a big-bang migration, they prioritized business-critical reports. The most important quality report became the proof point, successfully rebuilt in One Data and enhanced with additional production-line data sources that had previously been locked in silos.

In just three months, a lean team of three FTEs used One Data to deliver a comprehensive set of production-ready data products in Snowflake, each paired with a Power BI template report that visualizes the data product directly. This template-based approach ensures that every data product comes with a ready-made reporting layer, giving business users immediate, consistent access to their insights. This pace was only possible because of the platform’s automated code generation and standardized workflows. The automotive manufacturer did not only migrate, but they modernized, too.

Business users gained direct control over their reporting through the standardized templates and structures established during the project and now have a much clearer understanding of what data their reports require. Development cycles shortened dramatically, and data quality improved measurably. Empowered by this new-found trust in their data, business users themselves are starting to build lower-priority reports independently, easily accessible through the data catalogue.

The migration delivered a transparent, scalable architecture ready for the future, thanks to the One Data platform and its integrated development process. Their existing data catalogue (Collate) is now much more complete because the information from the intake process is transferred directly to it. Legacy dependencies became traceable, alignment between teams improved significantly, and users gained confidence in the new platform.