How to Close HR Analytics Gaps

The All-Too-Common HR Analytics Reality

Despite its promise, most organizations still struggle to use talent analytics consistently as a strategic tool.

“Ironically, HR is often the richest organizational function when it comes to data, but the poorest in terms of consuming the outcomes of that data,” says Korn Ferry’s Ashish Sinha.

“Too many organizations haven’t been able to connect HR decisions to the business outcomes that result from them.”

5 Consequences of HR Analytics Gaps

This isn’t an abstract problem. There are opportunity costs associated with ineffectual or failed talent analytics. These consequences aren't always obvious, but over time, they can corrode operational efficacy and damage employee trust.

What happens when organizations can’t connect the dots?

1. Disconnected Hiring and Onboarding
   “The information that we gather during talent acquisition is so important to assess whether a candidate is the right fit and whether they hold the same values as the organization,” says Sinha. “But too often, candidate data is totally disconnected from employee data.”
   
2. Engagement and Retention Blind Spots
   Many senior HR leaders are all too familiar with engagement surveys or exit interviews that sit in metaphorical drawers. That’s simply because there’s no easy way to connect the information with other metrics.
   
3. Poor Succession Planning
   When there’s no unified way to assess data, succession choices are subject to bias. This can result in employees being promoted into roles they are ill-suited or unprepared to take on, or whose traits don’t match evolving organizational goals. It can also foster disengagement among employees whose accomplishments are overlooked. 
   
4. Incomplete Talent Maps
   Without effective talent analytics, organizations lack a clear view of potential skills gaps, or, at the other end of the spectrum, an effective way to identify successful traits of high performers. What’s more, high-level trends go unseen.
   
5. Siloed HR Activities
   When no one has a comprehensive and unified view of what HR activities mean, it’s tough to connect them to broader business goals and outcomes.

Why HR Analytics Is So Hard to Harness

Why are so few firms taking full advantage of what talent data can do? Here are the most common culprits.

1. Poor Data Hygiene
   Organizations’ data is often too messy to be effective. Sometimes the processes of gathering information are flawed. Data hygiene issues tend to be deeply entrenched and attempting to correct years or even decades of patchy intelligence can feel impossible.
   
2. Lack of Strategic Focus
   Too many companies try to wrangle every last bit of data into an analytics tool, which is often unfeasible and usually unnecessary. “You probably don’t need all the data about an employee,” says Sinha. “What you need is the data that is really important to make decisions.” 
   
3. Failure to Lay the Groundwork
   Too often, no one has taken on the pain of understanding how everything comes together, says Sinha. “You have to invest in foundational capabilities if you want to bring analytics to life.”

Your HR Analytics Tool Kit: What Organizations Need to Close the Gap

All good talent analytics programs start with a solid foundation of organizational capabilities that, together, greatly increase the likelihood of success.

Here are the essentials:

Capability No. 1: A Common Language

Here’s a quick test—ask some members of your team to define what a few core HR concepts mean within your organization, such as “potential,” “attrition,” “skill,” and “succession.”

In a perfect world, their responses would mirror one another.

Yet that’s rarely the case, according to Sinha. “It’s very easy for organizations to overlook adopting a shared language of talent, but it is such a fundamental capability in the successful development of talent analytics,” he says.

Why? When people carry different definitions of what different talent metrics mean, it creates disparities in the data and makes it much more difficult to calibrate information. It’s a bit like comparing apples to oranges.

“When you share a common talent language—one that is really understood throughout the organization—you’ll know everyone is looking through the same lens,” Sinha says.

Not only does this linguistic level-setting produce cleaner and more consistent data, but it also tends to yield fewer misunderstandings, fairer talent assessments, and better overall organizational alignment.

Capability No. 2: Strategic Data Hygiene

No talent analytics initiative can succeed if it’s drawing from bad or poorly organized information—which means most organizations have some data cleansing to attend to.

That can feel overwhelming. But it needn’t be as daunting as it might seem.

That’s because you can be selective in the scope of the project. “You only have to cleanse the data that matters to you,” Sinha says. “When organizations try to get all the data perfectly correct—which will never happen—they can’t move to the next level.”

Once you’ve determined what matters and what does not, technology can accelerate the process. Data cleansing has never been easier.

Capability No. 3: A Willingness to Share

HR data tends to be heavily gatekept within organizations because it often includes sensitive and personal details. But managing data responsibly does not necessarily mean hoarding it within departments, meetings, or PowerPoint slides.

By giving more stakeholders access to information (with the right governance guardrails in place, of course), organizations can remove many of the soft barriers that impede the efficacy of talent analytics and prevent it from integrating into cross-functional workflows.

“I always encourage organizations to democratize data,” Sinha says. “When you’re more open with your information, you will see the culture of the organization become more data-driven.”

There is a cumulative benefit to more open access. Over time, stakeholders will develop a sense of ownership over all information, not just their own team’s, which tends to lead to an improvement in data quality.

Capability No. 4: An Appetite for Insights, Not Just Information

An organization recently came to Korn Ferry with a problem. It had been hiring women into leadership roles at an unprecedented rate but consistently fell short of its gender equity targets. They couldn’t figure out what they were doing wrong.

An analytical scan of their data, drawn from all stages of the employment cycle, identified two previously overlooked organizational tendencies:

• Attrition—the company was losing women at a faster rate than it was recruiting them.
• Talent advancement—female employees were moving into horizontal roles far more often than they were promoted.

Taken together, they told a much more nuanced story than what could be gleaned by reviewing hiring, exit, engagement, and advancement data in isolation. “We unearthed a bigger problem and were able to start having the bigger conversations,” Sinha says. “That can only happen when you integrate data. The power comes when you start connecting.”

Such breakthroughs require cultural change away from the all-too-prevalent addiction to transactional data and towards mindsets that default to looking for strategic meaning.

“People in HR tend to cling to dashboards and love to see what is happening in real time,” Sinha says. “But talent analytics is more concerned with telling you why it matters, so your team has to be able to have conversations about insights, not just information.”

“When you make talent analytics a priority, and when you leverage technology, you can create the kinds of insights that take things into a new dimension.”
Ashish Sinha, Korn Ferry