It may not feel like it, but people analytics has been in a lovely honeymoon period. Over a number of years, vendors and the press convinced everyone from the CHRO to the Board that it made sense to invest in analytics. As a result, people were hired to do analytics and investments were made in technologies that were bought, in part, based on how good the analytics would be once they were in place.
It varies from company to company, but many have been through a grace period of several years to get the technology, data and expertise in place. Now, the CFO is beginning to get a bit uneasy.
He or she is strolling over to the people analytics group and saying, “Hey folks, we spent a ton of money over the past few years on analytics, do you mind dropping off a spreadsheet showing all the fantastic cost savings this has delivered?”
In these still early days, it can be hard to show solid cost savings. You may be able to show you have much cleaner data. You probably have far better dashboards. You may have done some sophisticated work on flight risk. Yet for all that it probably still falls short of the expectations that leaders had and the heady promises that were made for what analytics would deliver.
Promises and the plight of the CHRO
Let’s step aside from this looming pushback on the cost of analytics to reflect on what promises were made and why. The promise was that through the magic of people analytics we’d discover insights that would lead to breakthrough results.
A better promise would have been that analytics would generally lead to better decisions that cumulatively would be important over time. Alas, we ended up with promises based on what we hoped was true rather than a more realistic view of what was likely to be true.
It’s not really anyone’s fault that expectations were overblown—that’s just the way the world works. The press wanted exciting stories, the vendors wanted to sell their products, and no one on the corporate side had enough experience in analytics to know for sure what results they could expect. It was easier for everyone to adopt an optimistic view and forge ahead.
What the CHRO and CFO have learned
As companies forged ahead with people analytics one of the most important successes has simply been learning. Companies now know a lot more about what analytics is capable of, and what it will take to get results.
Perhaps the first hard lesson for CHROs and CFOs was how far behind they were on the processes and technology infrastructure needed to provide the foundation for people analytics. A lot of unglamorous work in cleaning data and putting in more up-to-date HR technology was unavoidable. This is the sort of work that doesn’t lend itself to demonstrating a clear return on investment, but it had to be done.
The second hard lesson was that much of the magic promised in the press was just hype. Analytics is useful but it’s not magic. More than anything else it helps reduce uncertainty. We may have thought a factor was important in hiring—analytics can reaffirm that belief is correct. We may have hoped a program was working to improve engagement—analytics may cast doubt on that. Data does lead to better decisions; however, it rarely provides a shocking insight that is a game changer.
A third lesson was how quickly the core analytics team can become buried in a long stream of requests. It’s almost amusing how fast a group of managers can go from having no interest in analytics to wanting data on every idea that crosses their mind.
Analytics projects can take a long time, and the demand for analytics is essentially unlimited. If analytics teams did not find ways to eliminate unproductive activities and off-load work, then they were constantly dealing with managers who were unhappy with the slow response times.
The path forward
Now that we’ve learned the hard lessons and got the basic infrastructure in place, we should reassess where to go with people analytics.
The next step for analytics lies not in data science or technology but in HR professionals working on important business problems with a data-savvy mindset. I’ve seen analytics successes in everything from assessing the value of an AI-based resume screening tool to preparing for labour negotiations.
In all cases the starting point was a project that mattered to the business where real decisions needed to be made. Rather than jump to solutions based on opinion, the data-savvy HR pros took a step back, determined what they needed to solve for, and then gathered the best available evidence to guide their decisions.
I see the analytics savvy HR function having three lines of expertise:
- Senior HR professionals who understand the business issues and can formulate how analytics can help inform their decisions.
- HR pros who are quantitatively skilled and can gather and analyse data under the direction of the senior HR pros. I say “under the direction” but it’s really better seen as a partnership.
- Data scientists who pitch in when the quantitatively skilled HR pros get in over their head. These data scientists will also take on a handful of special projects of particular importance.
The payoff with this tiered approach is that you get lots of successful projects that use analytics—and these projects matter to the business.
Who should take the lead in post-honeymoon people analytics?
The good news is that the pressure to prove analytics pays off is really just an opportunity to reset expectations and direction. We shouldn’t be surprised that some of the initial investment in people analytics didn’t yield a clear return because it was really an investment in learning—in exploring a new domain.
There are any number of people in the HR function who can benefit from leading analytics into the next stage of maturity. The most obvious leader is the head of analytics because they are most directly in the firing line—followed closely by the CHRO who is responsible for everything.
However, the head of every department, and every ambitious HR professional, should recognize that people analytics will be a key to success in the future if it’s integrated into ongoing decision making.
Let’s start with a clean slate. We now have a much better idea of what people analytics can do, what it can’t do, and how to make it work. Let’s lead into the post-honeymoon based on that and not linger over some of the unrealistic promises made when we first encountered analytics.