“Big data recruiting.” “People analytics.” You’ve heard these buzzwords before, but maybe you’re not exactly sure what they mean, or if they’re even applicable to your small or midsize organization.
All you know is that everyone’s talking about it, and how it’s supposedly the future of recruiting.
That’s where we come in.
We’ve created this beginner’s guide to big data recruiting and hiring. It’s formatted as a Q&A so you can get a better understanding of this growing phenomenon and learn how to implement it in your own HR department. (Spoiler: Big data isn’t just for the big guys anymore.)
Without further ado…
Q: What exactly is big data recruiting?
A: It’s not as scary as it sounds.
Another way to read “big data” is “a lot of data”—the blessing and curse every recruiter faces in the Internet age. Every corporate job opening attracts an average of 250(!) applicants, according to Glassdoor. And each of these applicants comes with an infinite number of data points related to their likelihood to take a job, stay and succeed.
And we’re not talking about just bullets points on a resume—this data can include how many social media accounts a person has, the management style they prefer or even their level of curiosity. (More on where this data comes from later on.)
Big data recruiting is simply the collection and analysis of this information to make better hiring decisions.
Q: Is big data recruiting really a big deal?
A: Yes and no.
There’s no denying that large organizations have turned their attention to the benefits of big data: Gartner estimates it will account for over $232 billion in IT spending through 2016. But just from looking at HR applications, it’s clear big data recruiting isn’t exactly mainstream yet.
In a Software Advice survey conducted in 2015, we found that 37 percent of small businesses use some form of recruiting analytics software to help with hiring decisions. So while big data recruiting adoption may not yet be ubiquitous, it’s clearly starting to catch on.
Q: What is the process of big data recruiting?
A: There are three steps.
Michael Morell, founder of executive search and placement firm Riviera Partners, describes big data recruiting in three steps: discovery, visualization and insight.
The first step is massive data gathering—not only on applicants, but also on current employees. This information comes from resumes, social media accounts, HR databases, personality assessments, performance reviews, and so on. It’s all used to create comprehensive profiles of everyone in your workforce.
Now that you have all the raw data, how do you interpret it? This is where software and algorithms come into play: Software systems analyze the data to make connections and find patterns; e.g., workers with a shorter commute stay at the company longer, or self-described “workaholics” don’t tend to make great managers.
Collecting as much data on as many people as possible creates stronger connections and clearer patterns.
The last step is to weight the factors that matter most to your organization—expertise level, cultural fit, likelihood to have a long a tenure—so that the ideal candidates for an open position will float to the top.
Results might be choice-based; e.g., “hire this person out of these 20,” or score-based, e.g., “this candidate is a 9 out of 10.” Either way, the result is that you can make more effective hiring decisions more quickly. As time passes, and you collect more data, these evaluations become more fine-tuned.
Q: So am I being replaced by a machine?
A: Not at all.
While big data recruiting can give you more information and evidence to work with, the ultimate hiring decision is still a human one. There are a couple reasons for this:
First, big data can never capture those intangibles that really matter when finding the perfect fit for your organization. It can’t tell you things you’ll discover in an interview, such as a person’s confidence level or likeability.
But even more important: leaving all your hiring decisions to technology could get you in big trouble. Employers can still be held liable if their big data practices exclude protected groups like racial minorities or those with a disability, even if it’s unintentional. Recruiting will always need the personal touch.
Q: Are companies having success with this approach?
Here are some examples:
• Wells Fargo learned that bank tellers with accounting degrees did not tend to stick around, but those with degrees in financial services and hospitality did. Teller retention improved by 15 percent (Source: BAI).
• AT&T and Google found that an employee’s ability to take initiative is a far better predictor of high performance than good grades from a prestigious university. Using quantitative analysis, they’ve both adjusted their recruiting practices accordingly (Source: Harvard Business Review).
Q: Those are all massive companies. Are there any big data recruiting options for smaller organizations?
Big data recruiting and hiring has come out of complex proprietary algorithms created for giant multinational corporations. However, recruiting software vendors are starting to create systems that integrate analytics and help smaller businesses dip their toe into big data recruiting.
Here are a few examples:
Cornerstone OnDemand: Modeling After Your Best Performers
Cornerstone OnDemand’s recruiting application, Cornerstone Selection, combines customized personality assessments with work simulations to identify potential high performers in a group of applicants.
Using current top-performing employees as a baseline, the platform highlights those candidates who represent the best fit for your organization. Best of all, Cornerstone Selection’s machine learning provides continuous, small tweaks to ensure that candidate matching improves over time.
Entelo Search: Using Big Data to Poach Passive Candidates
Attracting top talent from other organizations is a tough sell, but Entelo Search uses proprietary algorithms and big data functionality to give you an edge.
The platform uses over 70 different factors, such as social media activity and hiring trends, to predict which candidates are “More Likely to Move.” These candidates are six times more likely to leave their current job and potentially work for you than your average prospect.
There’s even a diversity filter to help you find workers with a high probability of meeting requirements for gender, race or military experience.
Gild: Predictive Candidate Grades at a Glance
Gild maintains a database of over 100 million job prospects, and information on candidates is pulled from hundreds of sources and updated in real-time. Use a simple keyword search or an advanced search with more criteria, and Gild will float the best candidates to the top of the stack for you.
What’s more, the system uses predictive modeling and algorithms to score each candidate on a variety of aspects: from their expertise relative to your position requirements to their likelihood to leave a current job.
A web browser extension for Google Chrome keeps all this information at your fingertips when you visit a candidate’s social profile.
Q: Where can I learn more?
A: Visit our recruiting software page.
Head to our recruiting software page, where you can compare and filter over 200 recruiting platforms to find the system that works best for your size of business, industry, budget, functionality needs and more.
Then, call us at (855) 998-8505, where our software advisors will send you a shortlist of recommended products for free. You can dive into big data recruiting for yourself today.