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Internal Design & Deployment of Advanced Analytics Solutions at AramisAuto
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Automotive
- Education
Applicable Functions
- Product Research & Development
- Sales & Marketing
Use Cases
- Real-Time Location System (RTLS)
- Virtual Training
Services
- System Integration
- Training
The Challenge
AramisAuto, a leader in France’s new and second-hand automotive sales industry, was keen on developing its own competitive advantage with data-driven projects. The company decided to internalize the design, development, and deployment of their own data-driven solutions and products. This decision was driven by the need to develop analytics projects internally using newly hired expertise such as business intelligence engineers and data scientists. Due to data sensitivity issues, outsourcing data analysis teams was not a viable option. These new team members needed to quickly get up-to-speed in terms of creating highly-scalable predictive models and applying that knowledge to a wide array of business case scenarios, including real-time deployment of data products.
About The Customer
AramisAuto is a leader in France’s new and second-hand automotive sales industry. The company was created in 2001 and has 350 employees. It generated a turnover of over 356M€. With 1 car sold every 5 minutes, coupled with over 15 years of experience in the industry, AramisAuto is in the unique position of enjoying sector dominance. The company has a strong interest in developing its own competitive advantage and has secured the position of leader of the automotive sales industry by innovating consumer solutions.
The Solution
AramisAuto addressed these challenges by adopting Dataiku’s Data Science Studio (DSS). DSS’s whitebox approach, collaborative teamcentric functionality, and ease-of-use provided the company with the tools they needed to empower their data team to quickly prototype, test, iterate, and deploy innovative data-driven solutions. The ability to share projects between multiple users enabled team members to effectively work together – no matter individual level of expertise – while a whitebox environment facilitated data transparency between relevant stakeholders. Finally, with DSS, they quickly deployed a real-time API. The implementation and active support of DSS has enabled AamisAuto to fully leverage the capability and power of predictive analytics.
Operational Impact
Quantitative Benefit
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