How to use data to hire developers (data for dummies)

As a recruiter, you’re in a privileged position. Data is worth its weight in gold, and you have a treasure trove that’s almost as full as Jemaine Clement’s “Shiny” crab cave. (If the metaphor has confused you, watch Moana immediately). 

 

As long as you’ve obtained the data legally and gained the candidates’ consent (yes, we’re talking about the dreaded GDPR), you have what most companies would dream of – real, relevant, timely data you can use to inform your strategic hiring decisions.

 

The good news? You don’t need a data scientist, big data engineer, predictive analytics algorithms or complicated data processing tools to start using it. You just need access to your ATS, Excel or Google Sheets and (probably) a large coffee.

 

Are you sitting comfortably? Then let’s begin. 


First up, what does data have to do with hiring developers?

 

Everything! Analysing your own data, incorporating insights from data that has been collected by other companies and using predictive analytics tools can save you time and money, and result in more successful hiring decisions and happier employees.

 

“Analytics enables you to confidently double down on your strengths and eliminate the areas where you’re weakest so you can meet every talent need of your organization.”

 Ian Cook, HR analaytics expert

 

Data can help recruiters and HR teams:

  • Analyse trends in potential candidate demographics and candidate profiles
  • Analayse and forecast trends in programming technologies and skills
  • Understand how groups of potential candidates behave online
  • Identify which candidates are actively looking for a new role
  • Offer competitive salary, bonus and benefits packages
  • Analyse time-to-hire and identify bumps in the recruitment process
  • Forecast future hiring needs

 

 N.B. There is a difference between data and big data.

 

“Big data” is defined as data sets that are so large they must be analysed computationally. To pull your own big data sets, you’d probably need a data scientist or big data engineer, as they’d need to understand at least one programming language (likely to be Java or Python), computational frameworks, statistics and a bunch of other things we can’t wrap our heads around.

 

What we’re talking about is recruiter-friendly, not-so-big-but-just-as-useful “normal” data. The kind of data you can start collecting and analysing right now with no data-related experience.

 

“Armed with predictive analytic insights, recruiters can not only anticipate what will happen but be able to act on it as well.”

David Bernstein, VP of eQuest

  

As long as you have legally-obtained data on candidates, you can start using it to improve your hiring strategies. There is a wealth of data you can use, obviously depending on the type of data you’ve collected and your specific hiring needs.

 

 

How we’ve used data: talent maps

 

To give you an example, we’re going to focus on “talent maps”. It’s one of the easiest trends to spot and incorporate, so it’s a great place to start if you’re a data newbie or have limited resources to hand.

 

“Hiring plans should also include “talent maps”, so companies know where to look for talent based on data from previous hires and where an organisation’s best performers are from.”

Leslie Kivit, Director of Talent Acquisition at Berlin startup Door 2 Door

 

In other words, literally creating a global map of where your tech talent has come from. It’s as simple as it sounds!

 

 

Hopefully you’re still with us! Probably time for another coffee…

 

We’re lucky enough to have some amazing data to hand, so it didn’t take long to create our own “talent map”. Over the last few years, we’ve placed hundreds of candidates from all four corners of the world. We also have a database of over 50,000, including shortlists of candidates with particularly strong backgrounds and skill sets who are actively looking to relocate and start a new challenge.

 

So we set about pulling lists from our CRM detailing where each placement or shortlisted candidate was from and what their key skills were. It took us about 5 minutes to realise that in 2016 most of the UX designers we placed came from Brazil, whereas in 2017 there was a sudden influx of strong backend developers from Turkey. And the trends kept coming!

 

It seems simple (and it is!), but a surprising number of in-house recruitment teams and agencies don’t spend any time looking at trends from previous hires before they dive head first into sourcing.

 

 

How have these data insights changed our sourcing strategy?

 

After pulling the data for the first time, and realising what a big impact it could have, we decided to formally incorporate the process into our sourcing strategy.

 

Now, before we set a sourcing plan for a new role, we look at where our strongest candidates and previous hires with similar backgrounds and skillsets are from, and start our search in those countries.

 

We can also look at trends between skill sets (for example, what other programming languages or skills Java developers have), how long the hiring process takes and even what day of the week and time of day job ads get the most applications. There’s no end to the type of data you can pull, as long as you’ve set fields in your ATS (or even columns in your spreadsheets!) to capture it.

 

The results have been well worth the effort – and we didn’t even need the help of a data expert!

 

 

 

 

Now you have a real life superpower (yep, data really is that awesome), take a look at some of our wish-we-lived-in-the-Marvel-universe sourcing superpowers that would change tech recruitment forever.

 

Header image: Tetiana Yurchenko @ Shutterstock

Leave a Reply

Your email address will not be published. Required fields are marked *