systematicHR

I have an argument with my wife every few years.  I tend to like cars with a bit more horsepower.  I mean, that 1 time a year when there is a really stupid driver about to crash into you, a few extra horses comes in handy when you really need to speed away.  The problem is that 99.99% of the time, that extra horsepower is a luxury you really don’t need.  You’d get from point A to point B just as safely, and probably just as fast.  Sometimes though, that engine really does matter.  (My wife wins 90% of arguments by the way)

Everyone in HRIT is talking about big data these days.  Unless I completely don’t get it, I thought this is what we’ve been working towards for years.  I mean, having ALL of our talent data, core HR, learning, recruiting, payroll, benefits, compensation, safety, etc data all in the same place and running analytics against it all was always part of the data warehouse plan.  I mean come on, what else is ETL for if not to grab data from all over the place, aggregate it into the ODS, and then figure out how to make sense of it all?  We’ve built a nice engine that caters to our needs 99.99% of the time.

I’m going to propose something:  Big data does not matter to HR.  It’s just a new naming of something that does matter.  Business intelligence and truly focused analytics is what makes us focus our actions in the right places.  BI, Big Data, I don’t care what we call it.  Just do it.  Either way, HR does not have a big data need at this point.  I’d propose that we can use Big Data technology to speed up our analytics outcomes, but that’s about all we need for the next few years.

In my simplified definition of Big Data, it comes down to two major attributes: the use of external data sources, and the lack of need to normalize data across sources.  If we look at it from this point of view, the reality would state that almost no HR department on the face of the earth is ready to take HR data and compare it with government census data, or employment data.  Let’s get really creative and take local population health statistics combined with local census to get some really interesting indicators on our own employee population health.  Right, we’re just not there yet.

Let me reverse the thinking for a moment though.  What about the other 0.01% of the time that our traditional BI tools just won’t help us out?  Going back to benefits examples, how many global organizations can really directly compare benefit costs across the entire world?  How many of those same global organizations have a great handle on every payroll code?  Much of the problem is that the data is often outsourced, and definitely not standardized.  Collecting the information is problematic in the first place, but next to impossible to standardize annual changes in the second. The beauty of Big Data is that in these cases, you’d actually be able to gather all of that data and not worry about how to translate it all into equal meanings.  The data might aggregate in a more “directional” way than you’d like, but you’d probably still have an acceptable view of what global benefits or payroll is doing.  It seems to me that this puts us quite a bit further ahead of where we are now.

Listen, I know that HR has some place in Big Data at some point in the future, but the reality is that the current use cases for Big Data are so few and far between, and that we have so many other data projects to work on that we should continue investing in the current report and analytics projects.  Big Data will come back our way in a few years.

As I said, my wife usually wins the arguments.  We end up buying a car that has 175 horses under the hood, and I end up wishing we had more once a year.  But inevitably, automakers seem to up the game every few model years and come 5 years down the road, that same car model how has 195 horses.  If I just wait long enough, those extra horses in the engine just become standard.