Who am I?

I am Philippe GOGE, graduated from engineering school Ensieta in 1997. I have been working for 20 years in the field of mechanical vibrations, numerical simulation, and reliability. These successive activities are key skills and pillars of modern industry, which make it possible to predict, anticipate, forecast, optimize, and make the performance of innovative components or systems more and more robust. As a senior expert in the field of mechanical vibrations, I continually developed trainings in my field of competences. I am currently a Reliability Metier Manager in worldwide automotive company “Valeo”. In 2019, I recently obtained the “Data Science Starter Program” certification delivered by Ecole Polytechnique.

Why data science?

Since my childhood in Britany, in the far west of France, I fell inside the “maths pot” a bit like Obelix. I am fond of mathematics and this passion for both science and mechanics was a key that guided my choices from numerical simulation to data science through reliability and its statistical calculations always connected to the operational physics of the product. I really started to think Machine Learning and Artificial Intelligence in 2015 after watching a conference of Yann LeCun at famous College de France. Then, I quickly installed Opensource Anaconda framework on my computer and started practical training to codes Python and R. In my daily work at worldwide-connected R&D center Valeo Thermal Systems, located in France at La Verrière (78), I use and handle more and more data in connection to automotive R&D purpose.

Data science and data analytics enable to better understand and predict the reliability and the durability of our products. Because more and more data are available today thanks to sensors measurements during tests, data acquisition of cars in the field, open database (for example, climatic data that contributes to implement meteorological data into robust warranty predictions), it gives to me the opportunity to interact with data in different ways.

What kind of data?

Basically, the world of data is so tremendous and various that we can even connect finance data to warranty claims data and then establish a connection or pipeline to climatic open database and sometimes real tests measurements. Data must be connected. The capabilities are huge and today my motivations and objectives are:

• Visualize data for better understanding,
• Collecting large amounts of data and generating insights,
• Using data-driven techniques for solving business problems,
• Working with Artificial Intelligence and Machine Learning techniques.

What’s next?

Tomorrow, management of big amount of data will be a challenging and complex breakthrough in automotive data-centric industry leading by the example of the autonomous automotive driving solution and artificial intelligence deployed more and more in production plants. Digitalization is a keyword and needs human intelligence. What seems an evidence today remains quite a bit tricky in complex and worldwide organization. To face with these stakes and challenges, it becomes essential to educate and train people to data science. Data-science certification of industrial, financial, R&D networks will be one of the keys of success.