Big Data is becoming more and more central to making smart, effective and systematic hiring decisions and, equally important, helping organizations retain top talent. With bad hires costing companies $50,000+ each, it’s essential to find processes that can inform workflows from recruitment through retirement. But, increasingly, senior-level executives and stakeholders aren’t just looking for better HR methodologies, they’re looking for more concrete modeling and predictive strategies that all more or less guarantee short- and long-term success—enter the HR data scientist.
Michael Arena, the head of General Motors’ global talent and organizational capability group—and a data scientist himself—explains this seismic shift from highly anecdotal to massively analytical HR processes: “Historically, we haven’t been able to measure definitely the things that we intuitively believe to be true. But businesses are mandating it.” Gone are the days of “trust me, this will work,” as “HR is being held accountable to deliver business results. And the language of the business is analytics
So then the big question: if you’re leveraging or plan to leverage data and data science in your HR processes, do you need a data scientist? With more and more data scientists popping up in HR departments across every vertical, we’re seeing a massive, industry-wide ripple effect. It’s just like any other sweeping trend—as an increased number of organizations bring on data scientists, more feel the pull to weave these roles into their own departments. It seems like a no-brainer—integrate a dedicated HR data scientist and overcome the costly and time-consuming hurdles plaguing your organization’s systems.
And it is an unparalleled resource—for those HR departments that can bring the pieces together. Effectively leveraging a data scientist means having the framework in place, from the Big Data, to the top-down organizational commitment, to the resource allocation and the ability to put data scientists’ methods into action to get the most bang for your personnel buck. If you can lay this groundwork, then tap a solutions-driven data scientist to connect the dots, this position can be incomparably powerful. Complex, employee-centric challenges can often be broken down into smaller, more manageable parts, with clear-cut testing and optimization opportunities. Recruiting becomes more data-driven, pragmatic and forward-looking, ensuring a more seamless interviewing and onboarding process and a higher likelihood of long-term success.
Sounds pretty good, right?
That said, there’s a key consideration before jumping in head first—and again, it’s your department’s ability to bring those critical pieces together. A data scientist who can’t practice true data science may lend some value by spotting trends or developing hypotheses, but it’s not going to move the needle much more than that. All too often, organizations layer in a data scientist, but relegate that person or, even, an entire division to simple reporting, basic analysis and trend-spotting that barely scratches the surface. Sure, it may help your organization spot sweeping issues or uncover recruitment or retention patterns, but it’s not making the most out of data-driven talent management—or your data scientist.
Your next step? Take a good, long look at your HR structure and the overarching department and decide where you fall on the data science spectrum. If you’re head-down and ready to take your workflows to the next level, upping your data science game and, even, tapping a dedicated data scientist can be the way to go—and the results can be tremendous. But if you’re just getting started with data-driven talent management, consider integrating a more manual walk-before-you-run approach. Start with the analysis, hypothesizing and trend-spotting. Consider integrating weekly or monthly reporting to open up the conversation and integrate additional departments and hiring managers into the process. See what emerges and what the initial qualitative and quantitative data seems to be saying—and track those trends over time. Chances are, you’ll spot some valuable opportunities to bridge gaps, close loops and streamline various processes that will deliver some modest but still meaningful successes. And as you gain more and more wins, you’ll have a greater likelihood of gaining institutional buy-in, support and resource allocation, and be one step closer to leveraging full-blown data science—and data scientists—successfully with your division.