Dataiku

Seeing that one of their goals is to find ways to better answer consumer and business needs, the Global Engineering (GE) team was facing the challenge of improving processes and performance of the surfacing machines to significantly improve their production by using the increasing volume of data."We wanted a data science platform that would allow us to solve our business use cases very quickly. Thanks to Dataiku and its collaborative platform, which is agile and flexible, data science has become the norm and is now used more widely within our organization and around the world," said Cédric Sileo, Data Science Leader at Global Engineering, Essilor.

U.S. Venture, a company operating in diverse industries such as automotive aftermarket, energy, and technology, faced significant challenges in managing and analyzing customer data due to its complexity. The company struggled with creating enterprise tools and processes that could eliminate silos and promote collaboration. The Data and Analytics team at U.S. Venture, established in 2018, initially focused on data warehousing and basic reporting. However, they soon realized the need for advanced analytics at scale. The team faced difficulties in maintaining models and disparate data sources, which could quickly become unmanageable without the right people and tools. Additionally, the team's data scientists and analysts were using a varied set of tools and coding mechanisms, leading to a lack of standardization and collaboration. 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.

Pricemoov, a yield management solution provider, faced a significant challenge in handling and cleaning data from old SI systems, Oracle, or MySql. The data was dirty and required a full-time developer to perform long ETL (extract-transform-load) steps in PHP for cleaning. Once cleaned, the datasets were painstakingly entered into a model, as they were custom-built pipelines. The replication and deployment process for the next customer was taking weeks. This slow and inefficient process was hindering Pricemoov's ability to provide optimal pricing suggestions and solutions to its customers in a timely manner.