For the past several years, numerous articles and predictions have promised that this is going to be the breakthrough year for People Analytics (a.k.a., HR Analytics, Talent Analytics), with the assurance of rapid growth, competitive advantage, and value falling from the sky.

Has it happened? For many organizations, it’s never lived up to the hype.

Keep in mind, by analytics, we’re talking about moving beyond descriptive reporting of HR data, into generating rich insight that helps organizations thrive.

Some of the oft-referenced leaders in this space are household names overflowing with cash, and they’ve built talent analytics teams with dozens of people.

But even if your firm isn’t resource rich, several lesser-known companies have also enjoyed people analytics success with more modest investments.

Yet many organizations are still struggling to unlock the value of their HR data. Let’s explore why this occurs, and what we can do about it.

Why don’t some companies prioritise people analytics?

Here are some common reasons:

Lack of scale

This one’s easy. It typically takes a population of a few thousand employees to warrant building a talent analytics practice.

That’s not to say smaller organizations shouldn’t explore their HR data.

Calculating turnover, conducting engagement surveys, forecasting hiring rates, etc. are valuable activities that all but the smallest organizations can and should perform.

But advanced analytics that generate in-depth insight on a small population typically yield insufficient benefits to justify the investment.

Our data is a mess

So is everyone else’s. It’s a near-universal obstacle, and it’s tough to overcome.

Common causes include multiple systems, data disparity from mergers, lack of validation controls, processes that overlook data quality, lack of data governance to instill data standards and definitions, and large systems projects that either overrun their budget and cut corners on data quality or that exclude it to compete on costs up front.

Companies unwilling to make a data cleanup investment usually find their people analytics highly inefficient, inaccurate, and incapable.

Proving value is hard

Perhaps the trickiest reason people analytics isn’t more adopted is because of how difficult it is both to calculate and to convince internal investors that it will generate bottom line value.

A CFO once said to me, “point to the line on our income statement where this program benefits us.” 

But income statements aren’t organized by people concepts like quality, service, and engagement, so a people analytics investment can be a proposition that may not stack up well against more traditional projects like building a new warehouse or adding a new product line.

And strong finance teams likely want an NPV, rate of return, and a payback period. While the costs are easy to pinpoint, the benefits are challenging as they’re often located across multiple areas with uncertain returns.

Even if the benefits are quantified, the assumptions and attributions underlying those benefits are often easily picked apart.

A classic cost savings metric is reducing turnover: “if we can save just 1% on annual turnover, it’ll return us X.”

Although calculating an accurate benefit of reducing turnover is straightforward, determining what percentage it will reduce due to anything HR does is anything but.

Or what if engagement rises from the prior year? Was it because of people analytics, or because of good operational practices? These kinds of benefits are nearly impossible to isolate.

Finally, payback periods (the time it takes to recoup an investment) are longer than some companies wish to see.

It’s reasonable to expect investing in a people analytics capability to pay back within 2 – 4 years, but sometimes finance teams won’t allow any new investments that don’t improve next year’s annual run rate.

The minimalist approach

This happens when HR wants analytics, but invests limited resources to see if it’ll payoff before making a larger commitment.

Assigning only one or two people to this function is unlikely to yield benefits.

It takes a core team of a few people to build out the infrastructure necessary to operate with any kind of reasonable efficiency that will generate value.

And it’s far less compelling to continue investing after a track record of minimal success. Although it doesn’t have to be “go big or go home,” it takes at least “go medium or go home.”

Lack of skills

A senior HR business partner once told me, “we got into HR because we hate math!” 

While it’s certainly untrue HR practitioners possess zero analytical skill, it’s not a common competency. 

Don’t be surprised if you take strong HR talent who are also “good with Excel,” task them with building a people analytics practice, and find they struggle.

The skills needed are much greater.

Furthermore, it can be tough to recruit strong analytical people, who often perceive it as less appealing than analytical roles elsewhere in the company (they don’t know what they’re missing).

So how do we fix it?

Here are some tips that can help.

For the infrastructure component, instead of relying on a central Human Capital Management system to manage all the data, it’s a leading practice to build a central data repository (a.k.a., database, data warehouse, data lake) to fetch, consolidate, clean, report and analyze all HR data.

If built in-house, it usually takes a dedicated, 2 – 3 year effort before it’s fully complete, but the payoff is huge.

The typical, successful approach is to clean up some core data, then run some important analyses on it (try to achieve this within 6 months).

Then clean up some more high-priority data and run additional studies. Repeat this until the people and processes that generate people data are all but bulletproof.  Alternatively, vendors can do this for you, but they’re often expensive.

To get the right people, bring in analysts and analytical leaders from elsewhere in the company (and externally, if needed).

They can rely on resources within HR to gain domain knowledge. Sell them on the promise of getting to build their own team to do some great things for the organization.

Some of the strongest people analytics practices become a highly sought-after place for analysts from throughout the company to rotate through within their careers.

A more radical approach is to place the entire people analytics team outside of HR, such as in operations.

While that might sound heretic to some, and though there are hurdles to overcome (privacy concerns, among others), putting your people analytics practice outside of HR does have some advantages including gaining synergies with existing analytical talent, capitalizing on already-established credibility, and connecting analytical studies to business outcomes, which is one of the most important aspects of a successful people analytics practice.

This approach can be particularly helpful if your organization suffers from poor HR leadership, or if it’s primary strategy is “protecting the enterprise from its people” (always unfortunate, sometimes necessary).

Finally, there are a few tactics to help communicate value.

First, engage operations. You want them to be “pulling” for this capability by dangling in front of them the benefits that will help the business.

They may also benefit from adding people analytics to their existing efforts; for example, enterprise-level predictive modeling efforts often improve from adding people data to their models. Next, engage finance.

If they need to see a financial model, enlist their help to build it, so they buy-in to the assumptions.

Third, show the value from other large organizations.

Do your homework and ask them what kind of investment (people and dollars) they made as well as what benefits they receive from it.

Gather this data from several organizations and it will bolster your case. And while communicating these findings, don’t get caught in the “we’re different” trap; when it comes to people analytics, leading practices and benefits are more similar than not across all organizations.

Finally, use examples of what you can do with people analytics.

Don’t be afraid to admit you can’t necessarily quantify the benefits, while switching the conversation to gain agreement that the capabilities it enables are valuable.

Get your financial backers to recognize they’re investing in discretionary infrastructure that will improve decision making, and treat it like a similar investment.

Analogous examples include financial analysts or wireless networking:  neither provide direct benefits; that is, the organization can make decisions with less financial analyses, and employees could periodically plug laptops into the wall to check their email, but surely neither of those would be good decisions just to save money.

And remember to be up front about the investment and timeframe business leaders can expect to see results.

Good luck of your journey to extract the value from people analytics!