Data & AI / Customer Story
Why
Lack of a unified way-of-working among data science teams, resulting in a longer lead-time to production for AI solutions. Difficulty hiring the right profiles for data engineering and setting up the correct platform architecture.
What
AI Maturity Scan: Assessing Randstad’s data and capabilities, followed by strong support by one of our lead data scientists and a lead data engineer. GoDataDriven consultants introduced standardized ways-of-working, set up chapters, supported hiring, and improved knowledge of data teams.
How
Randstad's data scientists now form one team that works more effectively and efficiently drastically reducing the lead time to production for AI solutions. With the help of GoDataDriven's lead data engineer, this data engineering team has expanded and improved its way-of-working.
Dutch multinational human resource consulting firm Randstad had a problem. Actually, it had a few of them. Its multiple data science teams lacked a unified way of working, so the lead-time on producing AI solutions was too long. On top of that, the company was having trouble finding the right people to hire to do its data engineering. It also wasn't setting up the correct platform architecture. Randstad needed a more than a "quick-fix" solution it wanted to develop a First Class Data Science and Engineer practice.
Bringing the Tech to Randstad's Touch
Improving the company's data science practice was one of the goals Vermeer set out to achieve when he joined Randstad as a data and analytics manager in late 2016. "Most people think of HR as a 'people process,' so when I joined, not much was informed or driven by data," he explains. He convinced the company to implement a "data driven" approach so that decisions were based on facts & data instead of gut feeling.
"I spent about 20% to 30% of my time educating business users at Randstad and was often met with surprise. As an HR company, not many people are aware of the possibilities of data, and most employers don't know about the possibilities data offers," he explains.
Despite his efforts to educate the company on the benefits of data analysis, he encountered lots of challenges. He explains, "People get used to making decisions based on feelings, so changing that mindset took some time, naturally."
Looking For a Standard Way-of-Working
After running several successful data science pilots and use case, Vermeer and his team ran into two issues:
1. Industrializing models didn't go smoothly
2. The data scientists lacked a common way-of-working
"The tools weren't the problem; the challenge was in using them in a reproducible way," he says. "The issue was further complicated by the different ways-of-working and different visions among the data scientists." So Vermeer set out to find external help to solve these issues.
"We spoke with several parties and not only did Xebia's data label GoDataDriven stand out in expertise, but it also showed an ability to make a real connection with us. That human side was exactly what we were looking for," he says.
AI Maturity Scan as a Starting Point
GoDataDriven executed its AI MATURITY SCAN at Randstad to create a baseline of the organization's AI capabilities that:
- Created a clear picture of the organization's current capabilities
- Set a baseline against which it could mark historical and future progress
- Laid a solid foundation for the consulting work of GoDataDriven
"For me, the outcome of the scan confirmed what I already experienced in everyday practice. But it was also valuable to have hard evidence to show the rest of the organization what we were up against," Vermeer explains. "It also gave Steven Nooijen, the GoDataDriven consultant who would eventually work with us, a clear picture of where we were at before he started the assignment."
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