There are a lot of things to admire about Google. It’s a groundbreaking company and consistently leads in so many areas, not least of which is Human Resources.

For many of us in the industry, it is fascinating to track how Google applies analytics and predictive analytics to solve workforce challenges.

Living near Harvard and MIT at the time, I watched Google’s brainteaser questions pop up on local billboards and in the subway stations. (More than once our Co-founder and Chief Scientist pulled out his HP-42S calculator to calculate something and claim he had figured the brainteasers out).

Many businesses have instituted this brainteaser question practice at their own firms — copying the best practices of Google.

Then we heard (from Death and Taxes Magazine, Business Insider and Mashable) that Google discontinued these questions.

We found that brainteasers are a complete waste of time,” said Laszlo Bock, Senior Vice President of People Operations at Google. “How many golf balls can you fit into an airplane? How many gas stations in Manhattan? A complete waste of time. They don’t predict anything. They serve primarily to make the interviewer feel smart.

Many organisations are still using these same brainteaser questions. They’re shocked to discover that Google has discontinued the practice.

Just recently, Business Insider published an article about Google’s hiring practices. Reading it, I wanted to learn more from the Google HR boss about any predictive analytics processes the company might use in its hiring processes. What I read really disappointed me, as nothing quantitative was discussed or even hinted about.

My comments on Google’s 4 hiring rules are below.

Google Hiring Rule #1: Set an uncompromisable high standard.

I love this rule and expected to learn how Google has used analytics and predictive modeling to identify patterns that can be used to predict, with a high degree of accuracy, candidates who will perform beautifully in the role they are applying for. Or candidates that are predicted to last in their role for a certain amount of time, and give the candidate a high degree of certainty that the role they’re applying for gives them a high probability of success.

An obvious question is who sets the high standard, and how, and what is it based on if it’s not based on data? Laszlo Bock provides some insight into the answer: “Before you start recruiting, decide what attributes you want and define as a group what great looks like,” Bock writes.

The world’s entire hiring process is broken, precisely because people get together and decide what they want based on mystery factors. They have no real evidence that these factors work. I completely believe people give their best efforts to deciding what great looks like. In an era of predictive analytics, we can do better. Predictive methodologies allow us to identify those mystery factors and become accountable. To move away from bias, low performance and high turnover, we start from the data, not from mystery factors.

Google Hiring Rule #2: “Find candidates on your own.”

“… ask your best-networked people to spend even more time sourcing great hires.

This rule would be great if the candidates were additionally matched to a predictive model to help identify high-potential roles for them inside of Google. It isn’t hard and we owe our businesses this rigour.

Google Hiring Rule #3: “Put checks in place to assess candidates objectively.”

I completely love this rule. This is how banks decide whether or not you are a good risk for paying off your mortgage; this is how a business decides whether or not your firm has a high likelihood of being a customer with a high lifetime value, thereby extending special discounts and coupons; this is the step when a predictive model could help recruiters find excellent roles for the candidate, roles where they are predicted to perform beautifully, and last for a long time in the role.

Google’s way of assessing candidates objectively is to “Include subordinates and peers in the interviews, make sure interviewers write good notes, and have an unbiased group of people make the actual hiring decision,” Bock writes. “Periodically return to those notes and compare them to how the new employee is doing, to refine your assessment capability.

In my experience, unbiased people don’t exist. It’s one of the lovely things that make us humans. We need machines that only care about a positive result to help us be less biased.

We can do so much better with predictive analytics.

Rule #4: “Provide candidates with a reason to join.”

Make clear why the work you are doing matters, and let the candidate experience the astounding people they will get to work with,” Bock writes.

Perfect. I couldn’t agree more.

So what is there to be disappointed with in the article?

Many of us look to leaders like Google for best practices and advice. It is known as a leader in so many respects, including HR analytics.

I was hoping for more. I was hoping that a company with perhaps the best visibility around what it’s doing in workforce analytics could help to move the industry towards using predictive analytics during the hiring process, to increase the drive towards accountability during hiring, and the death of mystery factors.

I love Google. I have many friends in its HR analytics teams and I continue to have huge respect for the work they are doing.

With predictive analytics, we can do better.

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