If you thought machine learning and predictive analytics were one and the same, you’re not alone.

Though the terms ‘machine learning‘ and ‘predictive analytics‘ aren’t interchangeable, they are complementary – less like apples to apples and more like apples and caramel. Excellent apart and unstoppable together.

Food metaphors aside, in today’s hyper-competitive landscape, the only way to get ahead is to be future-ready. To predict things before they happen so you can put your business in a favorable position. Luckily, with all the data that’s available to HR, you don’t have to be a soothsayer to answer questions like:

  • Which candidates in our applicant pool will become top performers?
  • Who on our staff has the greatest growth potential – and who is at risk of turning over?
  • When will I experience my next staffing shortage?
  • What will my expected time to fill be in next year’s economic climate?
  • How will projected business growth effect employee engagement?

Machine learning and predictive analytics can work together to answer HR’s most burning questions.

How does it work? Think of predictive analytics as the what and machine learning as the how.

Predictive analytics is a practice that attempts to quantify possible future events. Predictions are made by finding patterns in current and historical data, often through sophisticated mathematical and statistical models.

Fun fact: Predictive analytics dates back to World War II when it was used to decode encrypted German messages.

Machine learning is a way to apply AI to predictive analytics so that predictions can be made without human guidance. Machine learning gives us a superhuman edge because its algorithms can analyze massive amounts of data and identify every possible pattern (and remember – patterns are key to predictions!)

While predictive analytics can be done without AI, machine learning unlocks new predictive power. Free from the constraints of human analysis, machine learning can use continuous data streams to make real-time predictions, then analyze the outcomes and improve its own performance.

How can your HR team bring machine learning and predictive analytics into practice?

As other arms of the business (looking at you, Marketing, Finance…) sharpen their predictive intelligence, the expectation is that HR do same. But where to begin?

If our peers can predict buying patterns and market trends, then the same capability should be available to HR. And it is! In fact, it’s built into many of the tools that your team is using today, or will be using in the near future.

Modern pre-employment assessments, for example, are really predictive analytics tools using machine algorithms to identify the highest potential candidates in your applicant pool. You’ll also find predictive analytics and machine learning in resume screening software, recruiting chatbots, and video interviewing platforms, which are beginning to use speech and facial analysis to predict job performance.

Whatever tools you chose, make sure they’re tied to the questions you’re trying to answer (Who are our high potentials? Where are our risk areas? What resources will we need…?). Implementing predictive analytics or machine learning without a defined purpose won’t do you any good. It can even get you into trouble.

So go! Explore the brave new world of HR and become the strategic player you’ve always wanted to be. But also, be smart and don’t succumb to everything that’s shiny and new – especially when it comes to AI.

For help cutting through the hype and investing wisely in new tools…

Download the HR Buyer’s Guide: How to Evaluate HR Tech in the Machine Learning Era.