Using Data to Understand the Great Resignation with Visier VP of People Analytics Ian Cook
Welcome back to a new season of Redefining HR. I am excited to sit down with Visier’s vice president of People Analytics, Ian Cook. In this episode, we talk about Ian’s career in people analytics, his recent article in the Harvard Business Review titled “Who Is Driving The Great Resignation?” and the massive dataset that has uncovered the drivers of the Great Resignation.
Ian started his career in organizational development and effectiveness — essentially “how you put a bunch of people together to really make a business thrive.” From there, Ian discovered what he calls the “CFO challenge,” where there are great people programs in place but no funding. That challenge pushed him to continue learning the “why” behind it, leading to the analytics side of understanding human behavior.
Years later, Ian launched a business and started collecting data on insights into HR, turnover rates and more, which was not common for analytics at the time. Fast forward to now, where his experience in analytics helps employers and HR professionals understand what is driving the Great Resignation and how employers can use this information to help with their retention.
Whether you call it the Great Resignation, the Great Realignment or the Great Reshuffle, everyone has a euphemism for what we are currently experiencing in the world of work. I was excited to dig deeper into Ian’s analysis of this phenomenon.
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Finding the Data
In Ian's HBR article, he relied on a dataset of over 9 million employee records and over 4,000 companies to help his analysis.
Ian explained that Visier collects data from core human resource systems in a common data model for aggregated use while protecting individuals from their data being singled out. For customers, in other words, "give us your data; we'll tell you how you match up against it."
Visier's data science team built security on top of this process to instill a sense of anonymity for individuals. Through this analysis, Ian explains that it was not based on if people will leave or might leave, but rather "it's the event that's in the HRS that says so-and-so left."
The Expected and Unexpected
Having been through the 2007-08 recession, Ian believed that CEOs and the public incorrectly assumed that lockdowns would mean “there’s no jobs,” but that wasn't true. Instead, like in past recessions, there was internal movement. "Lots of people, when the Great Resignation thing came, said, 'Oh, it's all of the young generations who've paused,'”
But what surprised Ian was that it wasn’t so much the younger generation who was leaving but "30- to 35-, 45-year-olds. It was the people who'd been with the business for a really long time."
What also surprised Ian was how the pandemic's effect on women had varied. Women were moving jobs at a higher rate than men, but it wasn't necessarily because they were leaving the workforce. "They were staying in work and accessing flexibility, accessing the choice they had to improve the way that work worked for them," Ian shares.
Ian believes, contrary to many opinions, that this movement was less about women leaving the workforce to handle their home life. It was “more of a positive story of the flexibility that is now being provided by some employers of, ‘You know what, I can do this, and I can manage my other responsibilities, so I’m going to make a change.’"
How Data Can Help Employee Retention
Most people have been asking this question during the Great Resignation: "What is driving it?" However, Ian believes “there is no single thing that is driving the Great Resignation."
Visier’s data looks at the Great Resignation from the employer perspective and helps them understand how to prevent losing their employees. What can cause an employee to leave is different for every company. He explains, "We advise against the 'read the press, decide that's your reality and follow the pres. We actually think that that's a really, really bad way to do strategy."
Ian advises that companies use the data to understand where it matters to them, which can add to successful retention during this time. "What we've seen with customers that are retaining folks is they use the data," he says. "They identify the population that is most impacted, and they double down on that specific population."
People in This Episode
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