How do you find that one special thing in the middle of all this big data? It depends on what you’re looking for. Machines can help you find things, but first you have to teach the machine to understand what you want. With recruiting data, a few simple formulas evolve into something far more complex.
This article from CIO.com, by Sharon Florentine summarizes how Artificial Intelligence is revolutionizing recruiting and hiring. Long story short, if you have really good data about who your high-performers are and what the process was like to recruit them, you can reverse-engineer the recruiting to predict which applicants will perform well after hire.
I hate to imply that it’s so-last-month, but the basic concept is straightforward. Collect large amounts of data, fine-tune its quality, run a statistical analysis to determine causation, and make a forecast. That’s what it looks like in a lab environment. But the good stuff is in the war stories of how this kind of experimental analysis plays out. The article names a few hot-points worthy of more discussion.
Where’s Waldo: Finding the Best-Fit Candidate in the Middle of Big Data
Citing Glen Cathey of Randstad, the new job search is similar to the “Where’s Waldo?” book series. “…it’s not difficult to search anymore, what’s of greater importance now is a data problem.” That is, you have good applicants, but you have to identify that one great fit. Cathey describes three types of search that make this viable.
- Semantic Search, which seeks to understand a searcher’s intent and the context in which the search is being made. (Remember, good fit is circumstantial and conceptual)
- Conceptual Search, which creates a basic concept from just a few key words.
- Implicit Search, which pushes information to you making assumptions about what you’re trying to accomplish “…much like how Google automatically pushes restaurant recommendations in your local area…” I have to admit, I’m always impressed when Google knows that I only want local
Dark Matter: The Missing Job Applicant You Don’t Know You’re Missing
In spite of his faults, former Defense Secretary Donald Rumsfeld did pioneer an important concept called “unknown unknowns.” That is, there are unknowns that you are somewhat aware are a risk factor, but there are deeper unknowns where you just have no idea information was lacking in the first place.
As it relates to recruiting, Cathey notes that “…you’re excluding people with those [machine-driven] searches. Doing it this way means you’re actually looking only for the best of the easiest candidates to find.” So, they use Artificial Intelligence and machine learning to find overlooked candidates. A strong candidate might have done a mediocre job customizing their resume to your posting, but still have exceptional virtue. They might use the wrong key words. They might have special skills that your organization needs but it’s not on the job posting. And your recruiting expectations might be biased towards a certain type of white male, or white males generally. The modified formulas can open-up the under-used areas of the candidate pool.
So, while it’s great if the machine gets you to a great candidate quickly, you can also get the machine to do the tedious exercise of finding the diamonds-in-the-rough.
While it’s true that some of this work can be done with basic statistical tools and a good data set, that’s actually an ambitious starting-point to get to in the first case. The advanced class is that you must create new data from scratch, revise the model on an iterative basis, and eventually run the model off live data such that the predictions change as the ground underneath the data shifts.
But that’s only if your attempt to do this kind of thing matches the business context. The big challenge is when the work is incompatible with organizational strategy, or the initiative needs a compelling business case to shift resources, or you need to win-over new people who are in the middle of a leadership change. At that point you will get sucked back into the complex world of humanity and empathy. So much for robots making our lives easier!
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