Bringing added value to data in all its forms is precisely the mission of Régis Ravalec, Group Data Officer at Engie. The setting for all the answers in this interview is industry, and more specifically his feedback on the energy industry, in both BtoB and BtoC.
My belief is that it would be wrong to reduce Data Science and artificial intelligence to just a technological contribution. It is actually more of a new way of bringing value to business, and doing it comprehensively. In my opinion, this is where we see the obstacle to a massive and rapid adoption of artificial intelligence in industry. This transformation is identical to the process of robotization. AI theoretically enhances technicians in their operations, but technicians still require convincing and training, they need to thoroughly review their know-how, etc. It also involves a new way of apprehending one’s profession, which must be taught and above all, must be supported by management teams that have already been won over.
I see a huge number of projects having value that has been identified theoretically, but that fail the test of industrialization due to a lack of upstream involvement of structures that need to thoroughly review their operational processes.
Industry adopting the cloud and big data is clearly fundamental. The IIOT (Industrial Internet Of Things), and more generally the acceleration of connected remote systems, are now essential in the landscape, to different degrees depending on the sector. Embedded analytics deployment is going to be our next challenge.
The recovery plan launched by the government, based on energy transition, should accentuate this phenomenon which is made possible solely thanks to continuous remote monitoring in order to manage renewable resources.
Data will be one of the facilitators of energy transformation and Engie’s Executive Committee has already understood this. However, corporate data still needs to be democratized, as it must remain a common good that can be easily shared. Data analytics experts are sorely lacking in quality data at scale. Working on data quality upstream implies a cost that the company will have to bear: in my opinion, this is the most likely new awareness in the next few years.
Data is central and inseparable from strong support by the Executive Committee, that has to vote on budgets accordingly, until the Business Units have reached sufficient maturity. We consider data to be an asset, and a common good of the company. However, beyond the words, taking concrete action remains difficult, even though things are moving forward. The health and economic crisis has shown how fundamental data is in the decision-making process. The same data is often involved, but it is analyzed on a continuous basis. As least the crisis will have given us that! Nevertheless, general indecisiveness does not support the prediction offered by machine learning, and refocuses data analysis needs on factual aspects (Business Intelligence and data visualization).
We measured our Group on a scale of 5, and we now expect to achieve an average of 3.5, one point above the result two years ago. It goes without saying that the evaluation is heterogeneous, depending on the business, in particular between BtoC and BtoB. However, the gap is becoming smaller!
Thought Leadership
