Perspectives

The Case for an HR Data Scientist


3 February, 2020

Organizations have massive amounts of data about their talent. But when it comes to actionable outcomes, they have little to show for it.

“HR functions have tons of data but no real expertise in how to manage and analyze it.”

Imagine a head of sales or marketing going to the CFO with a new capital expenditure project that will cost millions of dollars. Undoubtedly, the request would be met with a barrage of questions, among them what the money is being used for, how it will be divided by region, and of course what the expected return on investment is. Now imagine the response to those queries being “I don’t know.”

That would never happen, because the head of sales or marketing would never make such a request without working up a full forensic data analysis of the investment. But HR functions frequently lack the same kind of analytical sophistication when it comes to their initiatives, even though talent costs can range from 15% to 40% of an organization’s overall capital expenditures.

“HR hasn’t been speaking the same language as other business units,” says Jeremy Welland, global head of data and analytics at PayPal.

These days, however, an overwhelming number of critical business decisions revolve around finding top talent, developing a healthy corporate culture, and focusing on various people-related issues across the talent life cycle. HR leaders must decide and execute on talent acquisition, leadership development, human resources technology platforms, compensation, performance management, culture, diversity and inclusion, learning and development, succession planning, and employee relations, among a host of other requirements.

Reverse engineering HR

What makes for successful employees and an engaging employee experience is a complex stew of cultural, environmental, performance, and social factors. Here’s how HR leaders can use data to understand the links between those factors:

Keep it clean.

Put platforms in place which ensure that the data you have is true and trusted.

Build it right.

Design systems underpinned by AI and automation to collect and analyze data at scale.

Think bigger.

Create strategies that link data insights to business results.

Speak the same language.

Leverage data to show the payoff of HR investments in a way the CEO and board can understand.

Dan Kaplan, a senior client partner with Korn Ferry’s CHRO practice, says HR leaders must start leveraging data and analytics to show the payoff of talent investments in ways the CEO and board can understand. “They need to back up their work,” so to speak.

Kaplan also says HR leaders need to think bigger—in terms of both initiatives and scale. On the initiatives side, HR leaders need to show how they are helping achieve strategic business goals across the organization. On the scale side, processes need to be designed with AI and automation in mind so that the data they collect can deliver on those goals in real time.

This is where an HR data scientist comes into play. Having a data leader in place can speed up the time it takes to build systems, and that person can help decipher links between data points and their business value, which might not be seen or understood by traditional HR leaders. For instance, the data may shed light on career pathway patterns, or on a set of specific reasons that employees are voluntarily leaving the organization. But if there is no data set to analyze a particular thesis, who will know? “Success means thinking big enough to link analytics to outcomes leaders care about achieving,” says Welland.

“HR hasn't been speaking the same language as other business units.”

For many HR leaders, AI is used primarily in talent acquisition, as this is the area where companies see significant, measurable, and immediate results. AI can reduce time to hire, increase productivity for recruiters, and deliver an enhanced candidate experience that is seamless, simple, and intuitive.

But for all its value, AI also has significant drawbacks for HR leaders. It can be biased, for one. It also can’t account for intangible traits such as communication and collaboration skills that are hallmarks of high-potential talent. “AI-based recruiting tools are not bespoke,” says Vyas. “They can’t tell HR leaders what will make someone successful in their company.”

“We want data at the front of HR, not in the background.”

By contrast, Vyas says a dedicated HR data scientist can add diversity of thought to talent recruiting and retention. They can reverse engineer what makes an organization’s best employees successful by putting a sharper filter on how skills, experiences, personality traits, organizational culture, and other factors combine—and how that leads to success or failure.

Put another way, an HR data scientist can help HR leaders better identify what factors lead particular sets of talent to sustained high performance, which is particularly valuable for roles where success is more subjective and the determination could vary depending on who is doing the evaluating. Rather than hiring for a strict set of capabilities, an HR data scientist can also help identify where skill gaps exist within an organization and thereby open up recruiting or internal development to a broader set of talent to fill them.

That’s why, though he’s only been in the role for less than a year, Brock says he has big plans is to develop a “forward-deployed data team” for Coinbase’s HR operation. “We want data at the front of HR, not in the background.”

For more information, please contact Deepali Vyas at deepali.vyas@kornferry.com or Dan Kaplan at dan.kaplan@kornferry.com.