1. What Does the “Datafication of HR” Mean to You? More Important, What Should It Mean to HR Leaders Today?

Stacey Harris: HR Loves to Look Outside, But It Ought to Dig Deeper Inside

Simply put, the datafication of HR is investing in analytics that will help improve your organization’s policies, practices, and processes, and in turn help HR improve how it functions. But what does that really mean? And what data should HR look at to make those improvements?

HR needs to review both internal and external metrics, but often, one of these gets overlooked.

HR is historically prone to searching outside the organization for data before it examines what’s happening inside. In other words, HR is quick to put huge amounts of hours into comparing its organization against norms for its industry or its competitors. But does that data really apply to your organization?

Here’s a very basic example: The standard average for the number of HR professionals needed in an organization is one for every 150 employees. But does that take into consideration your organization’s approach to how HR is organized and how it functions?

Here’s another example of external data that may not really tell you anything useful in and of itself: The common wisdom today is that to get a measurable impact from your talent management system you need to spend at least $10, and up to $52, per employee on your talent management technology.

Those are great benchmarks. But if they’re separated from internal metrics, HR is only seeing half the picture it needs to see when it considers the quantification of HR.

Steve Boese: HR at the Crossroads — We Have the What and Why of Big Data, but Not the How

HR is at a bit of a crossroads in terms of understanding and working with data. Five or six years ago, everyone was telling HR that to be more effective it had to understand business better and be closer to the operations side of the organization — and we sort of bought into that.

A couple of years later, HR really started asking how it’s going to do all of that, which morphed into the need for other kinds of data. Today, enterprise organizations have become data repositories, from metrics on the supply chain to metrics about call-center performance to individual employee behavior.

With all of this data about performance, compensation, and promotion — all of this HR stuff — the new question for HR became, “What do we do with it? How do we understand it, make sense of it, and apply it to make better sense of talent management?”

The bottom line: HR faces two overarching challenges around using data to better be able to be an effective part of the quantified organization:

The concept of data-driven decision-making isn’t new. But until recently, it’s been all about business data. Now organizations can dig deeper into people data. But that’s a more challenging proposition than amassing more quantifiable metrics like the kind finance or operations can create.

Robin Schooling: HR’s Relationship with Data — No Longer Looking Only in the Rearview Mirror

HR is changing its long-held belief that HR data merely equates to information or records. Datafication of HR means we stop merely storing information in data warehouses and begin using it.

HR has traditionally relied on historical point-in-time data as the primary source for planning.

With the ability to link HR data to organizational data, HR is able to not only store information, but use it proactively: to improve operational management, better align goals, be agile, and measure it in real time. Meanwhile, if HR sees that it’s measuring incorrectly, it can detect this in real time and ditch the measurement tools it’s using and adjust accordingly.

Datafication of HR means we can do that across the organization as we move toward evidence-based management — relying less on “gut feel.” But to use data to make leading decisions (as opposed to lagging ones), we need to fully incorporate external data, as well as data from all parts of the business. It’s no longer just about what has been considered HR data (generally static and historical information: time-to-fill, number of employees trained, turnover, reporting of monthly/quarterly/annual numbers, etc.).

This is a shift in mindset for many HR leaders. But it doesn’t eliminate the need for HR practitioners to have solid skills in HRM; experience and judgment will always be critical competencies.

Paul Hebert: Does More Data Mean HR Will Help Employees Do Better Work or Make Better Decisions?

Maybe we’re talking about the wrong thing here. Maybe the trend shouldn’t be about more data for HR. Maybe the discussion should be more about how organizations can help HR help employees make better decisions.

Maybe when it comes to the quantification of organizations and the build-up of HR data, the question should be: Does the data HR is chasing accurately reflect human behavior and motivation?

Especially for the 99.7 percent of U.S. businesses that have fewer than 500 workers, the whole kerfuffle around big data and quantification is moot. It’s overkill, and most likely for most businesses, chasing big data is the tail of data wagging the HR dog.

So, for most businesses in America today, datafication in HR should start by asking two key questions:

We are in the business today of automating and creating technology that makes HR’s job very efficient. But human beings are the least efficient beings in the world. Shouldn’t HR be looking at how to become more effective, rather than more efficient?

In fact, all of the discussion about needing to push technology in HR and piling up more data is the reason we have horrible engagement scores: We try to make employee interactions and our dealings with employees very efficient transactions. So, datafication becomes about removing the variability in HR, which is removing the humanity from HR.

It’s time to stop and ask this question: Is all of the datafication in HR actually the problem and not part of the solution?

Lance Haun: The Evolving State of Data’s Effect on Talent Management

HR’s understanding of data as it relates to the business side of the organization is truly in a state of transition. It’s evolving. Which means everyone’s definition of what it means is also in transition.

Many organizations have just begun the multiyear process of liberating their workforce data from the clenches of closed enterprise systems. Others have already won that victory and are trying to figure out what decisions their newfound data can support. And the few at the front of the curve are applying that data to solve complex issues — truly applying big data and the quantified organization.

For HR leaders, the datafication of HR represents three things:

The datafication of HR will certainly impact larger companies in greater numbers. But don’t overlook its benefits for the non-enterprise organization.

A 250-person company may not seem to need these sorts of analytics and information, but the consequences of poor hiring practices, poor learning management, or improper workforce planning could be even more drastic.

Very soon, smaller and midsize companies will have to fully embrace the datafication of HR. Sure, they may not need to correlate their data with currency rate fluctuations in China or an increase in demand in Europe, but they’ll still need sound decision-making tools that help them understand what happened yesterday and predict and prepare for the future.