Evil HR lady nails it. Bring on the math(s) and stats.

cross posted on my Gartner blog.

I read many HR blogs, and one of my favourites is the Evil HR lady. She blogs a much of her HR stuff on the BNET site.

I have been speaking to a lot of HR audiences lately, and sometimes they get a little uncomfortable when I bring up the analytics topic. Most HR departments don’t do a good job at analytics, and then they wonder why they don’t get the budgets to make an impact on the business.

One of my suggestions to HR is to hire a good numbers person, someone with strong undergraduate or preferably graduate statistics.

Looks like this is exactly how the Evil HR lady got her start.

I had a master’s degree in political science, with a strong emphasis in statistics.  Since you never see a “Help wanted:  Political Scientist” sign in the window, I knew I needed to look outside my field of study.  What I really wanted to be was a trainer.  I knew that trainers often work in Human Resources departments, but how in in the heck was I going to get a job in HR?

Well, first, I went to a temp agency and asked to work in an HR department.  “I’ll do anything!” I said, and I meant it.  If the temp agency had sent me to wash windows in an HR department, I would have done it.  Instead, they placed me as an admin (I can type!), and for 6 months in a couple of different assignments I worked as an HR admin.  Then, I saw a job posting.  Job title?  HR Metrics Specialist.

What on earth does an HR Metrics Specialist do?  Well, I didn’t know, but one of the qualifications was the ability to do statistics.  That, I had.  So, I applied, interviewed and was hired.  My boss told me flat out that the only reason they had hired me is that I appeared to be the only person in the universe with a modicum of HR experience and the ability to do statistics.

I’ve been ranting on about this topic for ages, here are a few.






so it is nice to see some validation…

Mathematics is a beautiful thing. Spend 20 minutes in the company of Benoit Mandelbrot. (click here if you can’t see the video)


If Hans Rosling can do this with demographics on a global scale, why can’t HR do it with their data? (click here if you can’t see the video)


By the way: Jim Holincheck and I recently published quite a bit of research on workforce analytics, and there is buckets of cool stuff on pattern based strategy on Gartner.com


One thought on “Evil HR lady nails it. Bring on the math(s) and stats.”

  1. My start into HR has similarities to the story above from Evil HR lady. I went off to university to read maths and economics, was wooed by subjects like econometrics, decision making under uncertainty and game theory and ended up with a straight economics degree.

    HR appealed as organizations appealed – I tended to see them as markets which could be modelled and understood. That is still my passion. I love applying techniques from outside HR to workforce issues. For example analysis techniques from manufacturing on how to identify reasons for product failure can be applied to attrition; there is quite a bit in supply chain design that helps with talent pipelines and then my favourite, techniques for understanding consumer behaviour which can help with a vast number of workforce related issues.

    HR departments’ treatment of data often falls into two depressing buckets. On the one side is a disregard for data, on the other a realization that numbers are needed but then a focus on metrics that really add no clarity. Unfortunately hiring a few folk with strong technical statistics skills isn’t the complete solution as you need both the technical skills and the business understanding to be a good analyst. The other way HR tries to address this issue is by buying more technology.

    I don’t really think of my work as formal statistics. I probably have a far too unhealthy love for getting my hands on a big data set and enjoy data exploration and visualisation. The end goal is to be able to tell stories from the evidence, to help explain what is happening and why and ultimately to be able to build a few scenarios to predict the near future.

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