Baby, it’s cold outside. No doubt, just about every part of the U.S. is feeling the bitterest of winter chills to kick off 2014. It’s been so cold, even the polar bears said, ‘screw it, we’re going inside.’
So, what do we do? Well, we make the best of it of course, bundling up and getting outside to sled, ski and build things out of that cold white stuff. As everyone knows, the ideal time to build your best snowman/woman/dinosaur/whatever is when the snow first comes down, when it’s fresh and pure and still glistening white. If you’re out there on day two, you’re picking through mud and leaves and sticks and any other matter of snowfall detritus just to come up with a sad, messy lump of dirty snow that may, if you’re lucky, sort of resemble a snowman. Not the thing of winter picture postcards, that’s for sure.
You’re probably asking yourself, what in the name of Jack Frost can this possibly have to do with HR and recruiting technology? Well, many are saying that 2014 will be the year of Big Data in HR. We’ve of course heard this before, but evidence suggests that the early strides made in 2013 will begin to come to fruition this year. But before diving headlong into HR Moneyball, there is a lot to consider of course.
First among those considerations is how to deal with what’s already there – the tangled mess of unstructured, inaccurate and unreliable data (i.e. dirty data). Ahhhh, and here we get to the dirty snowman parallel: If you build a snowman from dirty snow, all you get is a dirty snowman. So if you build a Big Data HR solution from dirty data, what do you get? Josh Bersin knows, he’s seen it:
“We have talked to companies that have not done that [clean the data] and they have done the analysis on dirty data, and it has blown up in their face.”
Ok, so the concept seems simple enough – we must work with clean data, right? But how do we get there? And just how dirty is the data we have today?
Last summer, Jibe conducted a fairly comprehensive survey of corporate recruiters and talent acquisition leaders, asking them for thoughts about current technologies. Some of our findings around data management we’re not all that surprising, but alarming nonetheless:
It’s this last point that is, perhaps, most distressing. One of the biggest hurdles to overcome in order to move the HR Big Data conversation beyond hype and on to reality is the abundance of existing, historical data that, more often than not, is inaccurate and unclean. Essentially, we’re trying to build snowmen with mud.
We’ve talked before about the importance of working with accurate data and made sure when we launched our Recruiting Analytics solution last fall, that we had built a comprehensive pre-implementation workflow and data analysis offering into the service. No matter whose solution you’re implementing, this step is absolutely crucial to take from the very beginning. We work with our clients to pull historical data from all their systems and help them format and map it to our analytics schema. From there, we can begin to analyze the pre-existing data against live data coming in through our solution to verify accuracy and validate quality. Once that work is done, we can begin to scrub the inaccuracies that inevitably exist so the client can begin working with clean, accurate and meaningful data and start to reap the benefits of true recruiting analytics. In other words, they suddenly find themselves with a really beautiful (and powerful) snowman.
While this endeavor may seem daunting on the surface, the end result will be a more strategic, data-driven approach toward recruiting and all aspects of HR. And then, HR’s long-hoped-for “seat at the C-table” may very well become a permanent addition.
Until then, beware the dirty snow. Oh, and by all means, never eat the yellow snow.