Like many of you, I admire google for most things they do. They’re a groundbreaking company and they consistently lead in so many areas. Like many of you, I also read in earnest news about how they apply analytics and predictive analytics to solve workforce challenges.
Living near Harvard and MIT at the time, I watched their 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 their brainteasers out).
“We found that brainteasers are a complete waste of time,” said Laszlo Bock. “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.”
I know many organizations still using these same brainteaser questions today. They’re shocked when I mention that google has discontinued the practice.
This weekend I excitedly read this article from the Business Insider
I wanted to learn more about any predictive analytics processes Google might use in their 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.The article mentions setting an uncompromisable high standard.
- An obvious question is, how and who sets the high standard 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 with the data not “data 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.”
- I mostly agree with this rule. I would love this rule 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 rigor.
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, “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.
Why My Response to This Article?
I’m conscious of how many of us look to leaders like Google for best practices and advice. 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 they’re 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 their HR analytics teams. I continue to have huge respect for the work they’re doing.
With predictive analytics, we can do better.