How Cox Auto drives automotive operations with Dash Enterprise

Cox Automotive is modernizing its auction operations by bridging legacy infrastructure with scalable data applications using Dash Enterprise. Their core system runs on IBM AS/400. It serves as the source of truth, but creates data challenges due to its reliance on freeform text and limited structure. Layers of newer systems, such as those supporting vehicle reconditioning for retail, offer cleaner, better-categorized data, but the landscape is fragmented, and data maturity varies across business units.

To work with these limitations, Cox historically relied on manual data pulls and Excel, including complex VBA macros. However, that approach was fragile, unscalable, and heavily dependent on individual users. Custom in-house apps required full engineering teams and long development cycles, leaving a large gap between DIY spreadsheets and full-stack solutions.

• Legacy system: IBM AS/400 with minimal structure and high data complexity
• In-house custom tools required large engineering teams and long dev cycles
• Manual processes with Excel and VBA were brittle and not shareable
• Tableau and Power BI improved visibility but lacked interactivity and flexibility

Dash Enterprise helped fill that gap. It allowed business users to build interactive data applications that connect to structured data without needing a full software team. These data apps have enabled real-time work assignment tools for auction managers, combining database queries with human judgment (such as technician skill sets) to assign work effectively. The first prototype, built using Dash, demonstrated enough value to secure executive buy-in, licenses, and professional services support.

• Dash apps allow for interactive dashboards and scenario planning
• Managers use them to assign work based on real-time data and team knowledge
• Early prototypes proved value and helped scale usage internally
• Dash enabled fast iteration without embedding business logic into the front end

The data engineering stack supporting these applications has also matured. Snowflake is now the central analytics warehouse. Prefect is used to orchestrate data pipelines—pulling from on-prem systems, APIs, and other sources—and DBT (data build tool) handles transformations. Dash apps are hydrated hourly using these pipelines, with built-in monitoring and retry logic, ensuring reliability and timely insights.

• Prefect handles orchestration with notifications and retries
• DBT transforms raw data using SQL and Python
• Dash apps stay up to date by connecting directly to Snowflake
• Logic and data transformations are decoupled from the front end

Challenges remain. Scaling the effort is constrained by team size and budget. More importantly, data quality still slows progress. But Cox sees significant opportunity in expanding their planning applications to help shop leaders better manage workloads and reduce reactive decisions. Their focus is now on making recommendations both actionable and accurate.

• Scale and funding are key barriers to faster expansion
• Data gaps and inconsistencies limit decision-making confidence
• Planning apps are a top priority for further development
• Long-term goal is smoother operations through proactive planning

Watch the video to follow along with the full story and see how Dash Enterprise is helping modernize complex operations at scale.