Public and Utilities
Randstad is the second-largest staffing company in the world, active in 38 countries and with a top-three position in almost half of these markets. Founded in 1960 and headquartered in Diemen, the Netherlands, Randstad employs around 35,000 people at over 4800 offices worldwide. In 2020, they helped nearly two million candidates find a job with more than 236,000 clients, trained close to 350,000 people, and generated a revenue of €20.7 billion.
From People Driven to Data Driven
Dutch multinational staffing company Randstad had a problem. Actually, it had a few of them. It needed to evolve from subjective to data-based decision making. Its multiple data science teams lacked a unified way of working, so the lead time to produce AI solutions was too long. On top of that, the company was having trouble finding and hiring the right people to do its data engineering. It also wasn't setting up the correct platform architecture. Rather than a "quick-fix," the company needed to develop a first-class data science and engineering practice that would prepare them for the future.
First-Class Data Science Starts with People
Xebia first conducted an AI Maturity Scan to assess Randstad’s data and capabilities and lay a foundation for Xebia’s consulting work. A lead data scientist and a lead data engineer from Xebia were then tasked with guiding Randstad in their journey to unify and professionalize their data science organization. “Xebia’s consultants supported us by introducing standardized ways-of-working, setting up chapters of data scientists, supporting hiring, and improving the knowledge of our data teams,” said Falco Vermeer, data and analytics manager at Randstad.
Building In-house Capabilities to Enable Further Improvement
Thanks to the help of Xebia’s expert data consultants, Randstad's data scientists now form one team that works more effectively and efficiently. This has helped drastically reduce the lead time to production for AI solutions. Furthermore, this data science team has expanded and is now improving its capabilities through clearly defined growth paths and targeted training plans. Xebia also helped the company hire an internal lead data scientist with the expertise required to properly implement their AWS platform, hire the right candidates, and continue the professionalization of their data science department without external support. Vermeer noted, “The connection between data science and data engineering has improved and there is now a data science community within our company.”