The beauty industry was one of the first commercial sectors to harness the power of large scale data gathering and analytics in order to better pinpoint, shape and respond to customer demand. Companies like Alibaba’s Tmall Innovation Group have access to data gathered from more than 4bn customers to draw from when developing new lines of products that drive innovation within their customer bases.
"Data and artificial intelligence allow us to move faster to create cosmetic products that meet the infinite diversity of beauty needs and desires of consumers around the world,” said Philippe Benivay, IS Experimental Data Intelligence at L'Oréal.
As a world leader in the beauty and cosmetics sector, L'Oréal is constantly to innovate at the breakneck pace of the industry. Today, the company announced that it has selected cloud data integration and data integrity company, Talend, to power its Research & Innovation (R&I) department's data lake in a private IaaS environment on Microsoft Azure.
With Talend, L'Oréal's R&I teams can ingest its variety of scientific, IoT, and marketing data, to power cutting-edge analysis and new product innovation.
"In the globalized beauty industry, L'Oréal must innovate ever faster to meet the desires and needs of customers looking for new products and services that respect their bodies and the environment. Our vision is to deliver services to our businesses that they have not yet considered," said Benivay. “Talend, in our private cloud environment on Microsoft Azure, helps us transform R&I into a value-added service that can leverage all research data to deliver the best in cosmetic innovation in terms of quality, efficiency, and safety."
The deployment will allow L'Oréal to connect all types of databases, structured laboratory data, and very heterogeneous and sometimes raw data sources, such as robotic measurement or images data. Talend's solution allows L'Oréal to incorporate intelligent algorithms directly into data integration flows in the form of APIs.
The data lake enables the R&I to consolidate and prepare data to facilitate researchers' analyses and help them base their conclusions on reliable, high-quality data.