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Smiles, Googliness, and WARP: Human Talent Analytics

Only recently have companies discovered the power of analytics - of using big data to measure, interpret, and assess differently human potential, compared to traditional indicators. Casinos recruit based on one's smile, Google based on a person's "googliness" score, and baseball teams focus more on on-base percentage and WARP than home runs and RBIs. Can the same logic apply to unemployed youth?

Photo of Matthew Bird
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In the beginning (link to great Atlantic Monthly article) ... 

During World War II, governments in Europe and especially the United States had to build enormous organizations - i.e., armies and support for them - very quickly and "staff" them appropriately. But what was the most efficient way to ensure that people with the right talents were in the right positions?  Pencil and paper tests assessing human resource capabilities were born and after the war went mainstream, taken up by major companies - the Fords, IBMs, and Procer & Gambles. But by the 1970s, most had come to the conclusion that the qualitative interview was better. There were so many intangibles - how to truly get a feel for candidates, assess their potential. The two poles remained, more or less, until the late 1990s and early 2000s. 

In many ways, Sabermetrics - a new way of assessing the productive potential of baseball players and their contribution to "wins" - led the way, beginning in the 1980s. Instead of home runs and RBIs look at an athlete's on-base percentage and WARP (Wins abover replacemente player). In other words, measure your reality and use statistics to interpret it differently. Eventually, AC Milan was doing the same thing in European League Football, Casinos began to measure the relationship between smiles and consumer satisfaction, and Google learned that one shouldn't focus as much on the university where one graduated or their GDP but on other factors, such as orientation and attitude - thus creating a "Googliness" score. 

What lessons can we learn for creating youth pathways? 

Are there any examples of analytics being applied to the problem of "youth unemployment"? 

What are the risks and what are the opportunities? 

 

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Photo of Petar Vujosevic

Hi Mattew,

I am, up to a point, a fan of the “Moneyball” approach to scouting talent. It removes certain biases from the evaluation, which is always good. On a more basic level it opens our eyes to the fact that “undervalued talent” is out there and we need to change the way we define talent, in order to spot that talent.

However to transplant this to the world of youth employment there is a danger to lose the creativity that sabermetrics is trying to spot in sports.

Like human nature tends to make us do, if there is a test we train to pass the test, not always to master the skill. If we know what stats are important, we focus on those.

We at GapJumpers, and I am biased here, favour open ended problems to help companies spot great talent based on verifiable ability. The catch however is that it relies on data and on the eye of an expert.

We should measure, but always remember that the goal is impact, not just efficiency of hiring, for the way we measure impacts the way we educate.

Much like Sabermetrics and scouts together help the Red Sox win the world series (that and a big bankroll, because a pure Sabermetrics team like the Oakland A’s still has not won the Series) technology and human (expert) judging and training need to work together.

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Photo of Meena Kadri

We're amped to have you guys joining our challenge conversations, Petar. Changing the way we define talent is a great provocation – and GapJumpers is making inspirational strides on this. If Matthew or others are keen to check it out, read more here & join the discussion: https://openideo.com/challenge/youth-employment-pathways/research/hire-based-on-solving-a-challenge-not-static-background-info

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