Data affects all industries and businesses. But data projects are too often focused on technologies and not enough on the use cases.
• we identify the use cases by defining with both the IT and the business teams a common model generating ROI
• we support your teams while they are upgrading their skills in data science and technical solution architecture
• we implement an organization based on a Data Lab led by a community of ad-hoc practices
• we realize and industrialize your data projects at the company scale
With our innovative approach, we can prove the value of a business use case in only 10 weeks: 2 days to identify the use case, 2 weeks to frame it and 2 months to implement it.
GO WORK Together: do you have a data project and don’t know how to drive revenue from your data? Contact us!
With our innovative approach, we can prove the value of a business use case in only 10 weeks
Development of a data repository from scratch
- Implemented infrastructures around data to trading and quant teams
- Mutualized and standardized data sources
- Created Machine learning algorithms for data processing
- Continuously integrated and processed industrialization
- Automated delivery chain
Proven ROI: time saving, reliable data, the R&D team can now focus on value-added tasks
Technological environment : Docker, Python, Gitlab, Jenkins, AWS, C++
Implementation of business use cases on the following topics:
- Data Governance: analysed the quality of the data and detected anomalies in a cash flow database
- Natural Language Processing: experimented with a non-supervised text summarizer and created a document search engine
- Machine learning: analysed text with machine learning algorithms
- Other R&D topics : Reinforcement learning on analysis of bitcoin trading strategy from reception of market price information
In order to gain in competitiveness, ENGIE wanted to improve its offers to provide a better service for its key B2B clients. They particularly wanted to offer them new services linked with their contracts, especially forecasts of their electricity and gas consumption.
- Managed and coordinated the Lab teams to design the applications
- Developed statistic data visualisation applications
- Coordinated the data-scientist teams
- Implemented infrastructures
- Developed HCI (a lot of POC) to make Data Engineers’ API usable