- Application Infrastructure & Middleware - Data Exchange & Integration
- Application Infrastructure & Middleware - Database Management & Storage
- Finance & Insurance
- Business Operation
- Data Science Services
The Data and Analytics team at U.S. Venture began in 2018. At the time, the team was doing data warehousing and basic reporting, but soon realized they needed the right people and tools to do advanced analytics at scale — maintaining models and disparate data sources were going to become unmanageable quickly without them. That was their initial pain point: They had people that could have built solutions from scratch for DataOps (but no automation surrounding data collection, prep, and model connectivity) — but it wasn’t the time to reinvent the wheel.
Further, the team’s data scientists and analysts were using a varied set of tools and coding mechanisms — one data scientist used R, one used Python, some analysts were using SQL while others used Python, and so on. Resultantly, the individual team members built their own components that lived in different places and were created via their own tools, saved on personal computers, with no visibility for other team members about where projects were and how they were created or functioned. This prevented them from supporting each other and collaborating on projects.
U.S. Venture operates in many different industries (automotive aftermarket, energy, technology, and more). This diversity leads to complexity in managing and analyzing customer data and difficulty in creating enterprise tools and processes that remove silos and foster collaboration.
- Recognized as an innovative leader in the energy and transportation industries, U.S. Venture operates in market segments such as industrial lubricants, tires, and parts, alternative fuel applications, transportation strategy optimization, and fueling infrastructure, distribution, and equipment
- U.S. Venture’s family of brands includes U.S. Oil, U.S. Gain, U.S. AutoForce, U.S. Lubricants, U.S. Petroleum Equipment, IGEN, Breakthrough, and Tire’s Warehouse
- Founded in Appleton, Wis. in 1951
- 2,500+ employees
Upon implementing Dataiku, the team has been able to:
- Take several processes that were coded differently, consolidate them, and make a single recipe for them in Dataiku so they are reusable by everyone
- Combine governance and collaboration, for now, they have documentation and project visibility (from data source to modeling/output) in one place
- Automate processes that analysts and data scientists were managing in manual steps, saving time
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