Edmunds

Customer Case Study

Edmunds is a leading car information and shopping network serving nearly 20 million visitors each month.

Industry

Automotive Retail

Vertical Use Case

Improving online inventory management

Technical Use Case

Data ingest, ETL
Machine learning

Customer Testimonial

The Challenges

Massive Volumes of Disparate Data: 100’s of TBs of data from various sources made it difficult to accurately maintain vehicle inventory online — resulting in missing or inaccurate details which impacted the customer experience.
Infrastructure Complexity: Maintaining infrastructure to support data processing and machine learning requirements at scale required significant DevOps effort to support.
Poor Collaboration Due to Team Siloes: Data scientists were working in disjointed and unproductive ways.
Model Deployment Manual and Inconsistent: Lack of automation and consistent processes to ensure efficient model management.

The Solution

With Databricks, Edmunds is now able to simplify access to their disparate data sources and ensure their inventory of vehicle listings on their website is accurate and up to date, improving overall customer satisfaction.

Automated Infrastructure: Fully managed, serverless cloud infrastructure for speed, cost control and elasticity.
Interactive Workspace: Make collaboration easy and seamless across teams and multiple programming languages to accelerate data science productivity.
Streamlined ML Lifecycle: MLflow serves as the core framework and tool to make model deployment to production very easy and seamless.
Optimized Data Lake: Delta Lake provides a centralized system that ensures reliability, transactionality, and performance of their data.

The Results

With the implementation of Databricks, Edmunds.com was able to democratize data access across their organization, allowing its data engineering, data science, and business analyst teams to work collaboratively on the data at scale. They were also able to ensure data reliability and performance of ETL pipelines and streamline the ML model lifecycle. Edmunds.com also achieved the following quantitative results:

Improved operational efficiencies from data access to the productionization of ML models has resulted in millions of dollars in savings.
Accelerated ad hoc data exploration and analysis by six-fold allowing them to answer data integrity questions faster.
Improved reporting speed by reducing processing time by 60 percent, or an average of 3-5 hours per week for the engineering team.
Improved vehicle data quality metrics across their website by 35 percent.

Within Edmunds, Databricks democratizes data, data engineering, and machine learning, and allows us to instill data-driven principles within the organization.

Greg Rokita
Executive Director
Edmunds

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