What is "Real" People Analytics?

Hint: It's all real

Recently, I had a conversation with someone about an open people analytics posting at their organization. They were part of the people analytics team but said something to the effect of “that open role is not part of my team, it is part of another team that doesn’t do anything technical or real people analytics”. I was surprised by the dismissiveness of the comment, which seemed to stem from the idea that their team was doing more sophisticated analytics with more sophisticated tools. And that you are only doing “real” people analytics when you are using more sophisticated tools. 

Along that same vein, Rob Briner recently wrote an article where he argued that “evidence-based HR” is something different than people analytics. I don’t agree with Rob’s separation of people analytics as a different discipline than “evidence-based HR”.

Don’t get me wrong. Evidence-based HR is something organizations should be utilizing. Getting multiple sources of the right data to support the right questions is essential. Evidence-based HR is simply a fancy way of saying you are using the scientific method in HR to solve people problems. You won’t find any argument from me on the value of science or the importance of rigorous use of the scientific method. But it is not the use of the scientific method that makes people analytics real.

Similarly, there’s some cool stuff you can do with people data using tools like machine learning, sentiment analysis, organizational network analysis, and AI. I’ve used these tools which can provide valuable insights. But it is not the tools that make people analytics real. 

So then what is it that makes people analytics real? There are a million definitions for people analytics. How would I define it? Not in terms of tools or methods. Rather, I define it in terms of the problems. All people analytics is tackling some variation of the problem of  “getting the right people in the right place at the right time”. 

So people analytics is about using whatever data is needed to make sure organizations are getting the right people into their teams into the right roles that match their skills and passion while still meeting business timelines needed for that talent to do the work. That broad definition covers all the great HR work done by talent acquisition, talent development, succession planning, diversity, engagement, benefits, leadership development, and more.

Does people analytics include sophisticated projections and models used in workforce planning? Yes. How about a simple bar chart that demonstrates a lack of diversity in those in leadership roles? Yep. Using complex machine learning algorithms to determine employee sentiment from large amounts of survey data? Check. Tracking the attrition rate over time to determine the impact of initiatives that address the employee value proposition? You betcha. The list can go on and on.

Of course, people analytics teams are in different places on the maturity curve for different tools. Some teams are just trying to get a handle on basic operational reporting, getting information into the hands of those who need it. Others will be further along in their maturity in people analytics with large teams of data scientists and powerful predictive models. With so many people analytics teams starting up over the past few years, it is no surprise to see different maturity levels. Over time, as maturity increases, the scientific rigor of their work will increase. However, it doesn’t matter whether you are doing basic reporting or something more complicated. If you are trying to solve the problem of getting the right people in the right place at the right time, that’s people analytics.

Rather than denigrate and dismiss those “newbies/amateurs” who have the same intentions and goals that we do, why not welcome them into the people analytics discipline? Maybe they need our mentoring, education, and support in their journey to more rigorous people analytics. Maybe they need more case studies and practical examples of what works. The last thing we need is more divisiveness or different camps when we all have the same focus on what matters, the people. 

Let’s practice what we preach and keep a wide open tent for all types of people analytics practitioners, to allow us to learn from each other and support the profession.

Just as we define a discipline by the types of problems it solves, we can build groups and teams by the problems they solve. I suppose you can structure an organization by the tools they use, but I believe this is the wrong way to organize. It is the questions we are trying to answer that bring teams together to answer those questions. So when we are dealing with questions like “How can we improve the retention of our new hires?” or “What are the attributes that make for the most successful leaders?” you have a problem for a people analytics team to solve.  

As a side note, this suggestion to organize around problems, not tools is what makes me a bit wary about the increasing proliferation of AI roles/teams being spun up in teams. These teams are built around a tool, looking for a problem to solve. No matter how powerful the tools are, teams armed with tools searching for a place to apply them are bound to add less value than those teams that are relentlessly trying to solve business problems, no matter the tool. The proverbial hammer looking for a nail to pound never works as well as advertised.

Is AI a powerful tool that can solve some cool problems? Absolutely. But just make sure that you have first identified the right problems. So I predict that the recent hype around building up AI roles and teams will prove less successful than intended unless those teams take a step back and link their purpose to solving the problems that the business cares about.

So back to the original question. What is “real” people analytics? It is people analytics dedicated to solving problems, regardless of the necessary tools and approaches. If you are focused on real people, then people analytics is also real. 

If you like this content, consider subscribing to ensure you get future posts.