Data Intelligence: meet the team who have made predictive maintenance a reality

Denis Grandjean: Head of Data Intelligence

Denis has been working for Akkodis for six years. He started his career in sales, marketing and management for several digital service companies. As Head of the Data Intelligence, he is responsible for the pre-sales and sales phases of the project, alongside taking part in the business workshops and monitoring the project as it evolves towards new use cases. His days, he says, are full of variety.

The project was a challenge for the technical teams, but they were able to adapt to several constraints. First, there were geographically dispersed teams: Akkodis based in Niort, western France, the business team based in Lyon, and their IT team in Italy. There were also the client's strategic choices, including migration to the AWS cloud.

“We discussed these challenges with the team in charge of the project. We were able to upgrade our skills across a number of tools and adapt our work pace and methodology,” explains Denis.

You can't run data projects without understanding the customer's business. “To make the data ‘speak’ you must take an interest in the issues affecting both the customer and their business sector,” he adds. “Every time we work on a new data project, the consultants and I learn something new. One of the advantages of working in this field is that you can apply your skills and knowledge to other projects.”

Adelin Bouchet: Data scientist

When Adelin returned to his home region five years ago, he decided to join Akkodis. He was attracted by the development of the Datakodd SDK platform hosted in a sovereign cloud and by the opportunity to collaborate with industrial clients.

Thanks to a background in statistics, he has seen first-hand the emergence of tools for processing large volumes of data and deep learning methods. Adelin thrives on the challenge of big data. “We always have new data and increasingly varied analyses to carry out in close collaboration with the customer,” he says. “The deeper we dig, the more useful information we extract for our industrial customers.”

Everyone has their own recipe for success. Adelin finds motivation in effective communication within the team and with the client “We always play fair, and there's a healthy dialogue,” he explains. “This has led to a healthy and lasting relationship.”

 Adelin can also count on the support of the Akkodis teams, for example via discussion forums, which were invaluable in helping him to find Power Bi experts and meet the customer's data visualization needs.

At Akkodis, enthusiasm goes hand in hand with lucidity. Adelin believes that, in the future, “the challenges will reside in being able to exploit the phenomenal quantities of data without being dependent on GAFAM and BATX [shorthand for tech giants], while explaining to our customers that the new deep learning tools such as ChatGPT are helpful tools and not an end in themselves.”

Hermine Montrouge: Product Owner

Before joining Akkodis, Hermine worked for a Bordeaux-based start-up. She followed a former colleague's recommendation to join the company because of its expertise, human values and the diversity of the projects on offer. Initially hired to work as a developer, Hermine quickly progressed to the position of Product Owner.

This project had many surprises in store for her. She had to deal with the customer's strategic changes, such as budget cuts. “This was a first for me, and I found it interesting to have to adapt. We proposed a new macro-planning and a new team configuration that convinced the customer, and it's still working today.”

When faced with such challenges, Hermine relies on the strength of the team and transparent communication. “Working as a team meant that we were able to revise the workload plan: smooth the activity until the end of the year, measure new risks and submit an action plan. As a result, we were able to demonstrate our in-depth subject knowledge and our ability to adapt, while maintaining the quality of work.”

Hermine says she will often ask, “What next?” Impressed by the results on this project, she is already thinking about how machine learning and artificial intelligence could benefit other projects, for example in the aeronautical industry.

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