Three steps to building HR’s analytics capabilities

curved-strip-right bottom-curved-strip-white bottom-curved-strip-white-mobile

Unfortunately, with data flooding virtually every facet of every industry, the skills to interpret and analyse it are in short supply. According to the University of California, the global demand for data scientists has already exceeded demand by over 50%.

With those numbers in mind, it doesn’t take a data expert to see that most HR leaders’ chances of hiring in top data talent are obscenely slim (unless you’re willing to pay top-dollar).

Leaning on the data capabilities that exist in more mature states elsewhere; in sales, finance and marketing, some HR leaders are adopting a longer term but ultimately more sustainable approach.

Instead of pinning their data strategy on a silver-bullet hire, they’re fortifying their digital HR position, optimising data processes and steadily upskilling rank and file practitioners in data best practice and analytics thinking.

Over time, there’s vastly more value in this pragmatic approach. Instead of drafting in one highly paid data scientist, give me a team of HR business partners with enough basic data skills to leverage the granular knowledge of their organisation’s people, processes and culture.

It starts, like everything, with objectives.


Identifying and aligning objectives

HR objectives should align with business objectives. Yes, we know. We’ve heard it so much it’s almost become a cliché. So why do various studies still hint at persistent doubts among the C-suite around HR’s ability to deliver this?

In the past, people analytics projects have struggled to prove their value outside of the people management niche. This could be a question of mindset rather than skill. HR specific initiatives with HR specific objectives only solve HR specific problems.

Which is why it’s better for the project to be viewed as a business initiative, focussing on business problems. Seems like a petty distinction, but viewed at from a wider angle, the same questions can deliver greater boardroom relevance.

For instance, instead of looking at workforce measurements like engagement or absence, start asking about productivity or sales performance – because although both incorporate much of the same data, how you ask a question is almost as important as the question itself.


Data thinking

Some bad news. To deliver game-changing value from digital HR investment, analytics isn’t the first step. It isn’t even the third step. Platform choice and data competency are non-negotiable foundations for value-driving people analytics.

The good news, you don’t need to be an advanced statistician, data scientist or theoretical mathematician to develop a handle on data. Modern tools are basically supercharged calculators. They run on data and do much of the heavy technical lifting for you. But like any high-performance engine, they won’t operate effectively – or at all – on dirty fuel.

So if you’re running clunky data processes, over-reliant on manual input, distributed across various databases with limited accessibility or even paper-based, then an effective digital HR operation, let alone applying analytics to it, is going to be very difficult indeed.

Policies and governance that protect, maintain and periodically clean data are now incumbent upon employers as a result of fierce new GDPR regulations. But data quality should also take centre stage in every analytics discussion, with HR stakeholders, business leaders and line managers.

Enough has been written about how to streamline data processes; limit the number of databases you operate (ideally to one), simplify data collection, use self-service systems and eliminate manual data transfer where possible. It’s housekeeping and we won’t dive into it here.

For building your in-house analytics capabilities, your people need to learn how to think like data scintists. Author and data guru Tom Redman, aka “the Data Doc”, advises businesses to get their people started using data by formulating a question about issues that directly affect them, the using data to provide an answer. Redman’s brilliantly simple example: ‘Meetings always seem to start late. Is this really true?’

If 60% of meetings start late, how much time is wasted? How much does that cost the business if you extrapolate across an entire year?

Tracking and analysing data to apply basic analytical principles provides the grounding your team will need to apply simple people analytics to their organisation.

In your new data culture, ideas, proposals, solutions, reports and suggestions must come with a data-backed justification. This should be the new rule. And the key question from you, the part that ignites curiosity and unearths insight, is ‘so what?’. What does this data tell us about ‘why?’.

Are there variations that suggest correlation? If so, which factors matter most, and which can be ignored? How do those factors interact with each other? Crucially, what do they suggest in terms of insight and possible improvements?


Turning analytics into answers

A transition to data-based practices can be a difficult mindshift for HR staff who have traditionally trusted themselves to make educated gut decisions. Complex data sets, impenetrable statistical algorithms and mathematical formulas… intimidating.

But data isn’t impenetrable or intimidating. It doesn’t even have to be that complex. Take one of the most famous examples of data analysis – Billy Bean, the subject of the book ‘Moneyball’. Bean fundamentally changed Major League baseball, not with maths or esoteric statistics, but by using decades of data to predict which players were likely to be successful and hiring accordingly.

This is a great example because it’s perfectly relevant to HR. Billy Bean formulated his question, what factors indicate likely player success? He looked at the past, extrapolated a trend and used it to plan a talent strategy so effective that he’s now basically immortal in the world of baseball.

For HR teams getting to grips with data and analytics, it’s important to understand that it’s possible to make a huge impact with comparatively simple pattern analysis, trend spotting. In fact, a thrifty approach to data analytics, achieving more with less time and effort, is precisely the mindset you want to engender.

This stinginess, a familiar trait among data scientists, is what motivates the creative approaches that typify some of the very best.