This is a reprint of an article I wrote for the Edenspiekermann Blog back in 2016.
Big data is kind of a big deal right now in the world of HR. With more and more HR processes being handled digitally, there’s much more data. And the temptation to find statistically relevant correlations between this data is huge—who wouldn’t want to avoid a miss-hire or wrong promotion?
It’s just one of the ways that Human Capital Management, Human Resources, People & Talent (whatever you want to call it) has changed in recent years. There are tons of tools that apply statistical and predictive methods to things like recruitment, used mostly by big companies. It’s not something we should be scared of, but we should know it exists and know how it works, because it affects us all.
So what’s big data all about?
Every serious ATS (that’s “applicant tracking system” for you non-HR nerds) has statistical modules that monitor recruitment process KPIs: speed, accuracy, and literally every other useful or useless KPI you could make up.
Google, for example, is way ahead of the game here: when making hiring decisions, they use current employees’ performance data combined with the weighting of the people who voted to hire them. In Work Rules!, their former SVP of People Operations, Laszlo Bock, describes how they use data to refine decision-making processes, from hiring, promoting, transferring, paying, and even firing people—and how they feed that knowledge back into hiring decisions.
It’s about predicting success, and that’s something everyone wants, right? It’s important though, especially for full-time recruiters, to remember that they’re not dealing with a number or piece of data, but with a real person.
And that’s only recruitment or talent acquisition (if it’s named a little less threateningly). There are a lot of solutions for Human Capital Management (yes, that one takes the cake for most dehumanizing term in this article) which have Performance Management modules, so you can match the employee’s performance, rate them, scale them, and make promotion or termination decisions. Don’t be scared—we don’t use these.
And there are definitely some other tools that I don’t use. One of the creepiest examples of big data in recruitment is a tool I was made aware of by the brilliant Anna Ott at hub:raum (thanks!), called CrystalKnows.
It plugs into your Facebook, LinkedIn and Twitter accounts, running its algorithmic magic on these profiles, and then on every other person’s profile, to tell you how well you match and how to approach them, be it asking for a raise or recommendation, selling them something, or recruiting them. Crystal even plugs into your Gmail and recommends more effective words to use when writing emails to them. Weird.
Here’s what Crystal thinks of me. Hint: not everything is wrong:
What are we using here?
At Edenspiekermann, I rely on our own ATS, Unchained, which we use to post jobs and manage all the fantastic applications we receive. We built our own system because the market doesn’t offer the mix of customization, flexibility and simplicity we were looking for—I guess we’re rather meticulous about this stuff.
Our system also has a built-in talent pool where we keep people who just weren’t quite the right fit at the time, because you never know: the person who doesn’t fit now might be tomorrow’s hot hire. In my opinion, no company can afford to turn away amazing people—ever.
I‘ve also grown to enjoy working with LinkedIn Recruiter. It helps me find the right people at the right time in the right place—and it does it faster. Sometimes that means poaching them out of current jobs, but that’s quite frankly unavoidable in today’s labor market (sorry!). And it helps to avoid paying recruitment fees to agencies (sorry not sorry).
I’ve even learned to love Excel. I got really good at creating statistics and visualizations through a combination of pivot tables, formulae, vlookups, Florian, and a lot of coffee:
Who would’ve thought that the median ZIP code of Berlin Edenspiekermann employees is currently 10779? Or that the average birthday of all current Berlin employees is September 13th 1983? (Happy birthday, average current employee!) Meanwhile, the former Berlin employee’s average birthday is November 11th 1987, whatever that means.
It’s actually become fun to play around with this, but don’t tell 18-year-old highschool-me that—he’d hate me.
How else has HR changed?
Aside from big data, there’s plenty of other changes and challenges in modern recruitment. Ten years ago you were happy to find a web designer who worked in Flash, or an ASP or Shockwave developer, but as digital design becomes more professional, user-centric, data-driven and business-relevant, that web designer now has to have broader skills.
Everything they design has a business impact for our clients, so designers need psychological, statistical, and business knowledge as well.
Attitudes to working life have become more flexible too, which is great, but bridging the gap between this and the rather strict, inflexible German labor laws and social security regulations isn’t always easy.
All hail the data?
Well, not really. Big data isn’t a solution; it’s a new development that offers guidance. But nothing can compare to meeting someone face to face.
As cheesy as it sounds, the most fun part of my role is the people. I love working with people for whom work is not just a job to pay the rent, but a form of creative self-expression and a way to impact other people’s lives, even just a little bit.