Driving ESG and ML Innovation via Data Solutions for Iconic Vehicle Brand

The Challenge

The company was heavily reliant on gigantic volumes of data that lacked consistency, which harmed the entire decision-making process. This also deprived the ML team of quality data for training ML models, and the company’s strife for innovation was facing serious obstacles. Furthermore, the client could not even compute the amount of CO₂ emissions its Supply Chain logistics was producing.

The Solution

Scalefocus introduced user acceptance tests that helped the company identify useful data. The data infrastructure was uploaded, processed, sorted, and filtered to create different iterations of the data product and significantly improve its quality before returning the reformatted data to the business so that its experts could visualize and use it for analysis.   

By implementing a CI-CD process we helped our customer to achieve complete automation and simultaneous delivery in the Dev, QA and Prod environments for testing, new tables, or procedure modifications. Leveraging Snowflake and Power BI, we introduced reports and dashboards that manage financial data and add sorting and search criteria to facilitate the client in identifying trends and anomalies, hence making informed decisions.

Developing a data product that computes CO₂ emissions based on the ISO standard for greenhouse gases was also essential. Scalefocus utilized Snowflake to deliver a solution that leverages supply chain data to provide insights into the environmental impact of all inbound and outbound processes. By analyzing shipment routes, vehicle types, and transportation modes to compute CO2 emissions, it enables logistics optimization for sustainability and efficiency.

To address the lack of quality data to train ML models, Scalefocus utilized the Databricks platform and Data Lake as a central repository for sizeable files. The team made sure the ML inference models worked by putting all disparate pieces of code into order.

The Results

Scalefocus’ database maintenance approach streamlined the customer’s operations to save developers’ time. Automation enables testing in one environment, and when parameterized, the changes are automatically updated in Prod, QA, and Dev. 

The Power BI reporting solutions gave stakeholders a unified view of the complete production cycle. They can now easily find and sort data regarding pricing, expenses, or delivery times per date, vehicle model, or specific car parts without relying on manually extracted and processed sales data.

The CO₂ emission data product also proved a genuine game-changer in the company’s mission to make its logistics as CO₂-neutral as possible. The solution facilitates monitoring the transportation methods and optimizing delivery routes. As a result, the company boosted its sustainability efforts and significantly reduced negative ESG impact.

Finally, the massive volumes of data that Scalefocus stored in the Databricks and Data Lake repository helped the ML team’s data scientists train inference models more efficiently and achieve the company’s innovation goals.