Today’s data can answer tomorrow’s questions.
Data exploitation is a central aspect in the evolution of the company.
In a rapidly changing world, it is no longer possible to simply observe and then act.
Artificial intelligence techniques allow us to be one step ahead, to stay competitive, to predict future phenomena, to see and identify trends and more generally to better understand the business and make the best decisions.
Today, artificial intelligence is no longer a domain of experts.
Your IA concerns
- Once the data has been collected and formatted, how can we explain and understand what we observe?
- How to evaluate the functioning and the links of the data? e.g. “Why did such and such a customer make such and such a purchase? Why did I have such and such a rate of frequentation of my establishment on such and such a day?” in order to allow decision making.
- I have information on the use of my equipment by my customers but I can’t find an explanation for the cancellations? how can I anticipate them?
- I would like to anticipate my customers’ buying behavior. What to offer to the right person at the right time?
- How can I automatically process or label large volumes of text?
- How to easily exploit unstructured data from documents, images…?
Our data science offer
Support you in the analysis of your data
Artificial intelligence works very differently from human intelligence. It is not intended to replace it, but to complement it, by providing elements that are simply not accessible to a human. It can also enrich your own business knowledge and allow your experts to know their field even better. We then enter a virtuous circle where human and artificial intelligence complement each other.
Our data team accompanies you on these key points
- Identify which factors impact which other factors, to go beyond what the experts already know.
- Find out which factors can be acted upon so that small changes lead to big improvements?
- Give your experts access to new and previously unknown information to allow them to enrich their knowledge.
Implementing predictive models
- the price at which a product will sell;
- the proportion of non-conforming parts in a current production run;
- the reaction of your customers to a future change in a product;
- or more broadly, the risk of an event occurring.
- Identify what in your data will predict the key indicator to anticipate and improve your decision making
- test several models and select the most efficient one(s)
- Maintain these models by organizing the re-learning phases
- transparently display what the model does so that you remain in control of your decisions