People Analytics - Show Me, Don't Tell Me

People Analytics - Show Me, Don't Tell Me

Over the past couple of years, I've gone from being an economics major with an interest in social psychology to starting a career in the emerging people analytics field. I owe much of that transition, as well as much of my people analytics knowledge, to the hundreds of articles I’ve found on LinkedIn and Twitter, as well as the writers who took the time to publish them. Their willingness to share insights allowed me to see what people analytics was, is, and can be; who is doing it; what kind of people and teams do it well; and how to be successful as a people analytics leader within an organization.

As I continue reading today, I continue to find more articles on those topics, as well as a rich discussion within the people analytics community about psychology topics (should we measure employee engagement?), business topics (which business leader should a people analytics team report to?), and ethical concerns (how do we make sure people analytics reduces bias, and doesn't inadvertently increase it?).

Two main audiences – who, what, where, why

These articles seem to be targeting two audiences. The first audience is made up of HR and business leaders who may have heard about people analytics and would be interested to learn how popular it is, who else is using it, and what kind of problems could be solved with people analytics. Some articles are also “excitement articles,” as Richard Rosenow referred to them. These help evangelize people analytics and get HR and business leaders excited to try what is still new and untested in many organizations.

The audiences people analytics articles seem to be targeting are (1) HR and business leaders, and (2) people analytics leaders.

Excitement articles also target the second audience – people analytics leaders. They are energized by these type of articles, especially while they are still building their team. People analytics leaders are interested in solving their own problems – how to People analytics leaders are interested in solving their own problems – how to gain credibility with business leaders, what questions to answer first, which skills they should make sure are on their team, and how to move from analysis to implementation. These leaders may not have both a strong HR and technical background, but they are capable of doing the analysis necessary. Their focus lies beyond the technical details.

A third audience – how

Occasionally I find articles, sprinkled in among the rest, that target a third audience. This audience includes two subgroups: people in HR who have little first-hand knowledge of statistical techniques, but know what questions they want to answer and could “get it” with a solid example; and people who came through more technical backgrounds, but don’t yet know what kind of data HR has to deal with or what kinds of questions would be best to answer. People in this audience might read an article on people analytics, and want to try something similar, but don’t know how to get started without a little more detail. They're saying, through more technical backgrounds, but don’t yet know what kind of data HR has to deal with or what kinds of questions would be best to answer. People in this audience might read an article on people analytics, and want to try something similar, but don’t know how to get started without a little more detail. They're saying, "show me, don't tell me!"

There is a third audience that just needs to understand how to do people analytics.

Articles for this audience don't explain people analytics at the macro level, but at the micro level, giving explanations of which kinds of analyses are useful for people analytics, as well as how to run them. Richard touches on the lack of articles for this audience, and provides the few examples he’s been able to find, including several turnover-focused examples in another article. Two other posts I would mention here are less how-to guides and more explanations: Jeroen Delmotte from iNostix by Deloitte put together a concrete example of when it is necessary to use regression analysis instead of summary statistics in HR, and Ben Taylor from HireVue explained natural language processing in a simple way, again with an HR data example. That’s not a bad sampling, but it’s nothing compared to the deluge of articles that allowed David Green to put together a “best of” list of them that reached 27 articles – just from the last 6 months! 

Why is this third audience being neglected?

A big reason is the sensitive nature of the work done in people analytics. Practitioners are rarely permitted to share even the results of internal analyses publicly, let alone the methodology used to get them. This sensitivity regarding employee data, even at an aggregate level, is laudable insofar as it protects the employees we serve, but we can probably do a better job of sharing information generally. For example, which kind of analyses are working and which ones are not, and for which questions, or data cleaning techniques that help deal with clunky HRIS data. HR Open Source also has good examples of sharing successful HR efforts in a way that doesn’t compromise employee privacy.

A second reason is that HR is using very few techniques that aren’t used elsewhere. Writing up an example of how to use regression analysis to understand performance drivers might feel redundant when entire series of textbooks have been devoted to regression analysis. But even though examples of these techniques are already widely available, you have to know where to look for them. That requires that you be both fairly technically skilled and knowledgeable about HR to know how to apply that technique – but I think people analytics still faces a shortage of people like that.

How can I help?

A good place to start would be with some examples of HR problems that analytics could solve – then the community could put together examples of how to answer them in a methodical, detailed way. Manoj Kumar recently proposed a crowdsourced list of dummy HR data and codes for people analytics (see bullet 10). And Tracey Smith recently published a list of examples of problems analytics could solve in HR. Sharing the tools and methods that would help solve those kinds of problems would be a perfect place to start. Let’s add to the articles written on who, what, where, and why with a few good articles that describe the all-important how. 

Updates & New Articles

As I come across articles that serve the third audience, I'll put them here. Please share any you find with me, and I'll add them.

Wei Chen

People Analytics at Google

3y

Thanks Ben, really good resource to read!

José Luis Muñoz

Strategic Product Manager @ Scale AI | MBA | MPA | MSTC | PMP

4y

Thank you for sharing. Do you have any resources for how to 'socialize' the results of people analytics to executives? Things like regressions analysis are sometimes too technical for non-analyst. 

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Sheritz Keji S.

Data Engineer & Nerd | Analytics Engineer

5y

Your name seemed familiar and it completely flew over my head that it's because you're the HR Analytics in R Instructor.  I completed it last month and have since recommended it to others, because it's a great resource. Thanks for the post & the course!

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Elyse C.

Sr Manager, Employee Platforms

5y

This is a great article and I am happy that someone has finally put my feelings into words. I am definitely in the third audience and still very new to HR analytics. Can you explain to me what R is? I have seen it referenced in all of your links.

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Robert Kruzel, CPA

Director of People Operations,Technology and Insights

5y

Thank you for putting this together!  Going from "interesting" to "let's use this!" is such a great step.

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