The Moneyball Recruiting Opportunity: Analytics & Big Data

 

Earlier this year, I traveled to Australia to present a keynote at the Australasian Talent Conference on the topic of the Moneyball opportunity that exists for companies when they are sourcing, identifying, assessing, recruiting, and developing talent, and how big data and predictive analytics will be the next major area of competitive advantage in the war for talent.

Below you will find my keynote presentation, including a couple of YouTube videos.

Big Data and predictive analytics are just beginning to be leveraged in talent acquisition by a few forward thinking companies, and I am convinced they will both play major roles in the near future.

Unfortunately, at this time there is still some confusion around exactly what “Big Data” is and is not. For example, this Wall Street Journal article incorrectly references the use of personality assessments and other online tests to facilitate hiring as an application of Big Data, when in fact it is really just an example of analytics.

Data from personality assessments and online tests coupled with other human capital data doesn’t represent a combination of high-volume, high-velocity, and/or high-variety information assets, which most experts agree is required for something to be classified as “Big Data.”

In this presentation, I think you will find the examples of how companies are currently leveraging analytics in their recruitment as well as in the analysis of their current workforce to be quite interesting, as well as some of the tools that already exist that do in fact harness high volume, high velocity, and high variety information assets.

You may be shocked to find that data supports the finding that taller and more attractive men and women make more money than their shorter and less attractive peers (especially shocked to find out exactly how much more!) – which gives us a glimpse into how people make hiring and promotion decisions on a daily basis based on unconscious prejudice, similar to how unconscious prejudice, wisdom, and “gut” instincts are and have been used in athletic recruiting – which Billy Beane and Paul Depodesta of the Oakland A’s specifically set out to counter.

As demonstrated in Moneyball, very strong teams can be built with data-based decision making, throwing conventional wisdom to the wind.

Enjoy the presentation, and please do let me know your thoughts. Thanks!

 

 

If you like what you’ve seen in the Slideshare, you may want to read this post I wrote on Big Data, Data Science, and Moneyball recruiting last year.

 

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About Glen Cathey

Glen Cathey is a sourcing and recruiting thought leader with over 16 years of experience working in large staffing agency and global RPO environments (>1,000 recruiters and nearly 100,000 hires annually). Starting out his career as a top producing recruiter, he quickly advanced into senior management roles and now currently serves as the SVP of Strategic Talent Acquisition and Innovation for Kforce, working out of their renowned National Recruiting Center with over 300 recruiters. Often requested to speak on sourcing and recruiting best practices, trends and strategies, Glen has traveled internationally to present at many talent acquisition conferences (5X LinkedIn Talent Connect - U.S. '10, '11, '12, Toronto '12, London '12, 2X Australasian Talent Conference - Sydney & Melbourne '11, '12, 6X SourceCon, 2X TruLondon, 2X HCI) and is regularly requested to present to companies (e.g., PwC, Deloitte, Intel, Booz Allen Hamilton, Citigroup, etc.). This blog is his personal passion and does not represent the views or opinions of anyone other than himself.

  • Hakon Verespej

    I love this. I don’t like the buzz phrase “big data” because conversations often devolve into arguments about terminology, but however one defines it, it is absolutely important and I completely agree that it will play an increasingly critical role in how we operate now and in the future.

    I feel that the Money Ball analogy breaks down where the number of baseball players are highly limited and vast statistics about them are readily available. This will likely become less of an issue in the future, particularly for internal tracking. However, it runs into a lot of issues around people who have minimal or no (or protected) online presence, privacy, etc.

    I think something that tends to be missed in the context of Money Ball is that they were assembling a team. The skills of the players compliment one another and that’s something important that I hope everyone will pay attention to. Statistics about how top performers are 10x more productive in working on creative tasks than average performers are interesting, but I’d also like to see more stats on the comparison between an average team full of top performers versus a well-matched and well-oiled team with “average” performers.

    Thanks for sharing Glen! Would love to hear more of your thoughts in this area in the future.

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