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.

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. 

Europcar Mobility Group was facing specific challenges regarding car rentals at airports and the ability to accurately predict fluctuations in demand based on changes in the market; for example, the International Air Transport Association — IATA — predicts that routes to, from, and within Asia-Pacific will see an extra 2.35 billion annual passengers by 2037. In order to start tackling the problem, Europcar wanted to build an application using data from different sources (both public and private), including fleet traffic and passenger volume, reservation and billing data, data on the opening of new airline routes, and more. This presented another challenge, as the data comes from different places and is in different formats, but is also massive in volume.

PriceMoov’s challenge was that data originating from old SI systems, Oracle, or MySql 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 painfully entered into a model, as they were custom-built pipelines. And once finished, the replication and deployment process for the next customer was taking weeks.

In 2017, LINK Mobility decided that they wanted to scale up their data efforts both when it came to handling internal requests as well as externally with customers. LINK Mobility produces a lot of data and saw an opportunity to expand its offerings to provide more data-driven insight to customers surrounding the delivery and performance of their messages and services. They were looking to expand to customer dashboards as well as send additional offers based on that data.LINK Mobility needed to find a tool that would allow them to scale up data requests coming from inside the company as well as be flexible enough to provide data insights to customers without having to use two different tools or platforms to cover their various needs.

As an online marketplace, Trainline has always been convinced that data adds value for marketing teams. That’s why early on, they created a technical team within the marketing department tasked with creating aggregated, centralized dashboards focused on Trainline marketing acquisition efforts. This ambitious endeavor called for data science skills and a tool strong enough to blend and support multiple data formats and sources to track acquisition according to certain parameters.Starting from scratch, how could the technical team ensure they ended up with a tool that allowed them to improve and upgrade their own skills while also satisfying the marketing department’s requests quickly and efficiently?

The client services department at Orange has a data science team that, until two years ago, was performing mostly ad-hoc analysis for the business and had limited ability to work on more complex machine learning-based projects. In order to scale out the team and expand their scope, they had to overcome several challenges:Tooling: Only those who knew the tool and its proprietary language could work with data, which limited the use of data to statisticians or data scientists. Even then, the data was difficult to access, making projects difficult to get off the ground.Hiring: The data team at Orange was struggling to hire talented data scientists fresh out of university and with lots of ambition as well as creative ideas (traits they were seeking to enliven their data science practice). Unfortunately, this was largely a function of the tooling challenge, as young data scientists were largely looking for jobs where they could work with open-source tools (such as Python or R). 

Five years in, data warehouse costs were spiraling out of control, and performance was suffering as the amount of data grew. The company needed to find a solution that would allow anyone across the organization to work with large amounts of data while also ensuring optimized resource allocation.In 2019, Heetch chose Dataiku as their single platform for building data pipelines and processing raw data, paired with Looker for the seamless visualization and exploration of those flows.

The primary point of contact between OVH and its users is through its website, where customers can place an order and receive technical advice or support. But the business analysts responsible for disseminating data and insights to inform on the commercialization and optimization of the website were facing issues.The main problem was that the dashboard didn’t combine different data sources for a complete view, so it necessitated ad-hoc analysis, for which the analysts had little time. Additionally, ETL (extract, transform, load) for the dashboard presented concerns for the data architects around data and insights quality, as there was a lack of transparency around exactly what data was being transformed and how.