I Don’t Want No Stinkin’ Analytics!

I’m a nerd.  I get on my bike or I go for a run, and I’ve got my Garmin GPS running the whole time telling me how far I went, how fast I went, what my heart rate was, (ok I’ll stop the list well before it gets to 20 items).  I also happen to weigh myself 4 times a day.  I like to know how much I weigh, what my % of water weight is, how fat I am, etc.  I’m a total nerd and I like my data – lots of it.

When I get home from a routine Saturday ride, the first thing I do is download all the data.  The data by itself is interesting, but only for about 3 minutes.  The second thing I do is I trend the thing.  I’ll look at my ride side by side with the prior week’s and maybe several others.  Basically I’ll compare the stats and get an idea if I was slower, faster, more powerful, etc.  In other words, within a few seconds, I’ll know if I’m better or worse off.

The thing is, I really don’t care about that either.  What I really care about is that I was really slow up Mt. Tam, or that I got dropped on the way into Pt. Reyes.  It’s not that I care that I suck (that’s a given), it’s that where I suck tells me what to work on.  Sometimes, it even tells me that I didn’t eat enough (a common problem if you guys know me – yes, I’m neurotic).

In the last 15 years, most of us have gone from minimal data in our reports, to some pretty decent analytics and/or dashboards.  We’ve started moving away from the static and columnar operational reports and into trending and drillable analytics.  With these new reports, we’ve prided ourselves with the ability to tell our executives that “turnover is down compared to a year ago”, or “the cost of hiring in #BU has skyrocketed.”  It’s a wonderful day in the neighborhood!

But I don’t want operational reports.  Neither do I want analytics.  What I want has almost nothing to do with the data.  I want insightfulness into the business.

Let’s pretend I go to the head of my business unit and tell her that turnover is up over the last 3 quarters.  “Crap,” she says.  “What is the problem and what do we do about it?”  Joe HR stammers and says, “looks like we have an engagement problem???”  Trends and drills are really nice things.  I know I’m lying, our managers really do want this stuff.  But they only want it because we’ve starved them for data for decades.  They really are only mildly interested in the data.  Just as I only look at my bike ride speed out of curiosity, I know that the number alone tells me zilch.  Just as knowing my average speed compared to last week tells me only if I was a little better or worse, ignores conditions on the road, and the environmental context.  What I really want to know is how everything fits together to provide an analysis that give me the insight to act and make a decision.  I want to know what to act on, when, and how to do it.

HR’s job in data is starting to transition yet again.  We’re moving out of the business (I hope) of creating trends and drills, and moving into the business of context.  So the turnover trend looked bad.  Now the questions is how we create a regression model around our data to figure out what the primary actors are for that turnover trend.  Maybe for once engagement only has a small contributing score to turnover.  Maybe what we didn’t know was that the leader told their entire BU that nobody was getting a bonus this year.  Perhaps the cause of that was actually some severe cost containment driven at the corporate level.  How about some good analytics here to compare the cost of total turnover to the cost of those bonuses?

At the end of the day, we have much cooler data.  I’ll give us that one (and the vendors especially).  It’s time though to stop being producers of just the data alone.  It’s not enough anymore.  Perhaps when half of HR were “generalists” a decade ago this was ok.  But we’re supposed to be partnered with the business now and we still can’t really diagnose what’s going on, let alone how to fix things.  Once again, let’s stop being producers of data and become analysts of data.  We need to start producing some insightfulness.

Using HR Analytics to WIN

So I have to admit it’s hard to come off the election and not write about this.  This election was defined by some pretty deep population analysis, incredible forecasting and pretty significant actions to try to address the most important populations.  All of this went right along with a prioritization model that was pretty strong.  If we look at Obama/Romney, they each had target demographics: Obama needed the youth vote, managed to capture more than his fair share of the female vote (because the GOP was being stupid, and a couple contributions by Romney as well), and he also predictably got the “minority” vote.  As the election neared, both candidates narrowed down their activities to a few key states (Ohio, Florida, Virginia, etc.)  They had incredible models about what percentage of each population’s vote they would get, and if they followed those models, all they had to do was figure out how to get those people to the polls.  It turns out that in the end, the Obama machine was far better, and the Romney machine actually broke down (the phone app they were going to use literally went down on election day, and thousands of faithful Romney campaign volunteers had no idea which houses to go to and make sure people actually voted).

At the end of the day, the story is the same as the one we have in HR.  It’s about winning.  The difference is that we all want to win at different things.  Some of us want to win at engaging our employees.  Others want to win by having the best IP.  More yet want to win by producing the best products in our category in the world.  What is great is that if we’re good at what we do, we’re not focused on running analysis about turnover and headcount (although we’ll do that anyway), but instead we’re focused on understanding exactly what 5 things increase engagement in our populations.  Taking that a step further, we’d know exactly what populations are the most impactful on the entire workforce and target those people.  A 1% increase in engagement in the 30-something sales guys might yield an advantageous network effect while it takes a 3% increase in another population for the same to happen.

Similarly, if I want the best IP, I need to understand the roots of this.  Chances are it’s not just the standard talent management equations we need to figure out.  I mean, performance and succession are only going to take us so far.  Instead, if we’re figuring out the profile of the employees that created the most patent applications, the people who have the highest levels of trust in their subject matter, and specifically what are the attributes that made them successful, then we can start recruiting for those people, aligning our performance reviews not to achievements, but to the competencies we know work, and doing talent reviews that direct us to the right employee profiles rather than who we think is ready from a job perspective.

My assumption is that if you have created your HR strategy in the right way, and you have aligned that strategy with the corporate strategy, then HR is designed to make the organization WIN.  Therefore every analytic you are running should be directing your organization to that win.  Don’t forget about the mundane operational reports, but understand that focusing on that isn’t really helping you.

At the end of the day, Romney actually did almost everything right, and he really thought he was going to win.  The problem that he had was that he made a few wrong assumptions.  The GOP really thought that all the pollsters were wrong – that they were over emphasizing the Democratic vote that would turn out on election day – that Democrats, the youth, and Hispanics were really not as excited about Obama as they were 4 years ago.  Obama on the other hand had callers identifying who were voters, if they were voting for Obama, and then instructing the voter where their polling place was, good times to go, and what the plan was to get them there.  At the end of the day, forget about the other guy.  Just go run your analytics, make sure you are focused on what matters, and go out there and WIN!

Note:  Please assume no political commentary, simply an example of who analytics can be put into action.

 

 

Why Aren’t We Using Our Analytics?

There are everyday annoyances, and then there are just things that piss you off.  Usually the things that make me really angry are when something should have been fixed, but it hasn’t been.  Or something is happening too slowly where I should have just done something myself and been done with it.  Personally, I think I’m a pretty patient guy, but every now and then I’m shocked into anger.  Today’s topic?  NPR last week did a piece about the male and female wage gap.  Apparently it’s still there:

So, as the Washington Post notes, the authors tried to make everything as similar as possible. They tracked graduates with identical collegiate experiences, limited familiarity with the work world, and those who didn’t have spouses or children.

But the wage gap persisted.

The study found that in teaching, female college graduates earned 89 percent of what men did. In business, women earned 86 percent compared to men. In sales occupations, women earned 77 percent of what men took home.   (1)

So here’s the thing: WHY THE EFF ARE WE NOT USING OUR BASIC REPORTING SKILLS???

If we were back in 1989, I get it – it was hard to pull and aggregate all of that data.  If it was 1995, only half of us were on PeopleSoft and the other half of us were managing something so clunky I’ve probably never seen it.  By 2001, everyone had upgraded due to the Y2K non-event and we all had basic reports.  We’re a decade after this and if we all just ran a report that had 2 rows on it, we’d have fixed this already.  Row 1 = Average Wage of Males in Org.  Row 2 = Average Wage of Females in Org.  You can get more sophisticated and run this by jobs or departments.  You could run this by job AND tenure.  You could get really creative in whatever you want to do.  But here’s the bottom line:

EFFING RUN THE REPORT!!!!!  (and while you’re at it, RUN IT ON ETHNICITY TOO!!!)

And helpful hint number 1:  You might want to run the thing and give it to managers right before/during merit time so they can see just what bastards they are.  And just so we are clear, there is no hiding behind “we don’t have the budget to give you more than 3%” on this one.  This is called the equity increase.  What you don’t have budget for is the $1M lawsuit that I hope is coming if you don’t act on it.

Helpful hint number 2:  Organizations I’ve worked with on analytics always had a gender and ethnicity pay, hiring and tenure scorecard as part of their monthly or quarterly deck.  Companies who do this right are looking at this every month/quarter and know exactly where they stand.  They won’t get it right 100% of the time because we’re still looking at aggregated data on a scorecard, but at least they know they are on target as a whole.

What’s sad, is that while there are a variety of reasons this stuff happens (some managers really do suck, sometimes women may not negotiate as strongly, whatever), the effects of this are pretty serious.  Whether it’s gender or race, the impacted have about 10% less money in doing the same job to pay their bills, buy a nicer house, and sending kids to better colleges.  Guys, this is a big big deal, and we are actually empowered to do something to expose it, and are empowered to bring this to light.  There’s no excuse if we don’t.

</end rant>

  1. Korva Coleman, NPR.org. October 24, 2012, “ Equal Pay For Equal Work: Not Even College Helps Women”

(apparently I’ve forgotten how to APA footnote and I don’t care to look it up…)