Auto Parts Retailer

About the Business

This auto parts retailer has been on the Inc. 5000 Fastest-Growing Private Companies list six times. They have a 75,000 square foot distribution center that stocks more than $14 million in inventory, as well a highly efficient distribution model that allows them to ship most orders within 24 hours. This client has committed themselves to building their company based on service, quality, and continuous improvement.

The Business Challenges

The auto parts retailer was looking to modernize their analytics posture as it related to tools, technologies, and analytical capabilities. Some of the most critical reports required time and attention for preparation, and the goal was to reduce the manual effort required and automate the creation of those reports. The auto parts retailer was seeking assistance in the reframing and/or refactoring of key reports from Periscope and Google Sheets to Power BI. In partnership with 2nd Watch, they then wanted to jointly create a roadmap to build, integrate, model, and visualize key data driving business decisions.

The Solution

For this client, 2nd Watch provided an accelerated data assessment within the span of four weeks. Because of this project’s time constraints, our data consultants primarily focused on restructuring a subset of the data and analytics platform to create a framework the client could work off of in the future. The main subset involved performance summary metrics that we implemented in a newer data transformation tool called Coalesce, which is a user-friendly tool that reduces the amount of coding involved.

Our client’s main goal was to move away from spreadsheets and into Snowflake. Their current state (pictured below) shows there was no centralized data warehouse prior to this project. All data was manually input into Google Sheets from numerous sources, with different people owning different pieces of the data. Overall, this state was not scalable as the business grew.

We then proposed a future state below and began implementation. All data was previously ingested into Snowflake into the raw layer. Our job was to transform the data into a more standard and reusable design, creating a centralized data platform that will help support their internal analysis and reporting as well. When creating the future state, we wanted to make sure the data model was flexible enough to support new data sources if they were to be introduced in the future.

The Outcome

Before implementation, multiple employees spent a majority of every Monday manually entering data. Moving all data to a centralized data warehouse will save both time and money as having time to analyze data will give employees the ability to make educated decisions and focus on other tasks they may have previously put off due to time constraints.

As businesses want to analyze more data that they are collecting and gain better insights, automation of data will help reduce human error, and day to day efficiency will accelerate growth to new levels.