The Moneyball Approach to Recruitment: Big Data = Big Changes

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This is the keynote presentation I delivered at the 2012 ATC in Sydney, Australia on the topic of the Moneyball opportunity that exists for companies when they are sourcing, identifying, assessing, and recruiting talent. Big Data and predictive analytics are just beginning to be leveraged in talent acquisition, and I am convinced it is the future. I think you will find the examples of how companies are leveraging analytics in their recruitment as well as in the analysis of their current workforce to be quite interesting. You may be shocked to find that data supports the fact that taller and more attractive men and women make more money than their shorter and less attractive peers - which gives us a glimpse into how people make hiring and promotion decisions based on unconscious prejudice, similar to how unconscious prejudice, wisdom, and "gut" instincts are used in athletic recruiting. As demonstrated in Moneyball, the best teams can be sometimes built with data-based decision making, throwing conventional wisdom to the wind.

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  • Moneyball: The Art of Winning an Unfair Game, a book by Michael Lewis about the Oakland Athletics baseball team and its general manager Billy Beane.Paul is nowvice president of player development and scouting for the New York Mets
  • Sabermetrics is the specialized analysis of baseball through objective evidence, especially baseball statistics that measure in-game activity. The term is derived from the acronym SABR, which stands for the Society for American Baseball Research. It was coined by Bill James, who is one of its pioneers and is often considered its most prominent advocate and public face
  • The Oakland Athletics' 2002 season featured the A's finishing 1st in the American League West with a record of 103 wins and 59 losses, despite losing three free agents to larger market teams: 2000 AL MVP Jason Giambi to the New York Yankees, outfielder Johnny Damon to the Boston Red Sox, and closer Jason Isringhausen to the St. Louis Cardinals. They are most notable for having set an American League record of winning 20 consecutive games between August 13 and September 4, 2002.[1]
  • http://www.flickr.com/photos/eggplant/22414700/The Curse of the Bambino was a superstition created because of the failure of the Boston Red Sox baseball team to win the World Series in the 86-year period from 1918 to 2004. While some fans took the curse seriously, most used the expression in a tongue-in-cheek manner.[1]The curse was said to have begun after the Red Sox sold Babe Ruth, sometimes called The Bambino, to the New York Yankees in the off-season of 1919-1920.[2] 
  • For instance, Sabermetricians doubt that batting average is as useful as conventional wisdom says it is because team batting average provides a relatively poor fit for team runs scored.
  • When I first heard that quote when I saw the Moneyball trailer, I immediately thought of all of the people who respond to my articles with “recruiting is about people and not about technology” (e.g., sourcing, information retrieval, databases, analytics, etc.). I also thought about all of the great people I’ve hired and the powerful teams I have put together with a computer. 
  • However, the problem with any conventional wisdom is though the ideas or explanations are widely held, they are also largely unexamined and untested, and thus not necessarily true.
  • Is there an equivalent to Moneyball in recruiting – in challenging conventional HR and recruiting wisdom and identifying and hiring top talent through the use of data, statistics, empirical evidence and objective facts?
  • Gut feeling, "I like him/her"
  • Paul Depodesta video
  • Average American male is 5'9"
  • Harvard developed
  • Offers online tests of unconscious preferences between racial groups, age groups, sexuality, political candidates, and associations between gender and science
  • This is not deliberate prejudice.
  • And this isn't confined to the corporate suite. Not long ago, researchers went back and analyzed the data from four large research studies, that had followed thousands of people from birth to adulthood,
  • One Sock.Nethttp://www.onesock.net/2010/09/meet-the-ceo-tall-stupid/http://people.anu.edu.au/andrew.leigh/pdf/BodySize.pdf
  • One Sock.Nethttp://www.onesock.net/2010/09/meet-the-ceo-tall-stupid/http://people.anu.edu.au/andrew.leigh/pdf/BodySize.pdf
  • One Sock.Nethttp://www.onesock.net/2010/09/meet-the-ceo-tall-stupid/http://people.anu.edu.au/andrew.leigh/pdf/BodySize.pdf
  • http://hbr.org/2011/10/hot-or-not/ar/1
  • HBR article
  • Hire onIntellence
  • Hire onIntellence
  • Other things equal, higher intelligence leads to better job performance on all jobs. Intelligence is the major determinant of job performance, and therefore hiring people based on intelligence leads to marked improvements in job performance – improvements that have high economic value to the firm.Intelligence is the ability to grasp and reason correctly with abstractions (concepts) and solve problems. However, perhaps a more useful definition is that intelligence is the ability to learn. Higher intelligence leads to more rapid learning, and the more complex the material to be learned, the more this is true. Intelligence is often referred to as general mental ability (GMA) and general cognitive ability
  • Robert Gibby, Ph.D.Senior Manager, HR Research & Analytics, Procter & GambleWonderlic Personnel TestDevelopment Dimensions International (DDI)
  • Interview decision process
  • Milan-based eyeglasses conglomerate Luxottica Group has used data analytics to disprove assumptions about gaps within the company’s recruiting strategy
  • Milan-based eyeglasses conglomerate Luxottica Group has used data analytics to disprove assumptions about gaps within the company’s recruiting strategy
  • Breaking away from the idea that the only way to hire great people is to “buy” and poach them from competitors or specific companies (look at how incestuous Facebook, Google, LinkedIn, Microsoft, Apple and Yahoo are with regard to their talent poolhttp://www.allfacebook.com/infographic-facebook-winning-war-for-best-talent-2011-06
  • Here’s where some real Moneyball recruiting can be implemented – instead of paying top dollar for an already highly paid industry retread, develop and use a structured and proven data and fact-based methodology for identifying the next superstar from a non-obvious company out or straight out of school. Wouldn’t it be interesting to see where the real game-changer employees come from, and not just assume they come from the obvious short list of companies? Who’s really to say that the best Facebook engineer isn’t one that came from IBM, GE, or some obscure company?
  • Napster, Plaxo, Facebook, Spotify – Sean was making $80,000/year in his senior year of high school, was recruited by the CIAMany companies make a college degree a prerequisite for hiring for specific roles or even at all, and many well-respected companies have hiring managers that are degree and university snobs – rejecting resumes based on schools attended and degrees earned.If you work for such a company, I have a few names for you: Steve Jobs, Bill Gates, Mark Zuckerberg, Michael Dell, and Sean Parker. Do any of them ring a bell?
  • If only companies would start to focus their business intelligence and predictive analytics horsepower that many already currently use (and spend  millions on) for marketing, product development, sentiment analysis, healthcare, etc., and focus it on human capital data to enable better hiring decisions (which always starts with talent identification, aka sourcing, by the way), we would begin to see Moneyball-like disruption develop in the HR and recruiting function.
  • SAS
  • SAS
  • Linking Open Data cloud diagram, by Richard Cyganiak and AnjaJentzsch. http://lod-cloud.net/This image shows datasets that have been published in Linked Data format, by contributors to the Linking Open Data community project and other individuals and organisations. It is based on metadata collected and curated by contributors to theCKAN directory. Clicking the image will take you to an image map, where each dataset is a hyperlink to its homepage.
  • 2.5 quintillion bytes of data being generated every day2,500,000,000,000,000,000
  • The size of the indexed Internet is estimated at 10.82 billion pages
  • How's that for velocity?
  • The variety of data sources and types should be obvious, especially when it comes to human capital data – LinkedIn profiles (which can now be converted into resumes/CVs) and updates, Facebook, Google+ and Twitter profiles and updates, recommendations/awards/endorsements, blogs, blog comments, mobile updates, press releases, and much, much more.
  • Wikibon is a professional community solving technology and business problems through an open source sharing of free advisory knowledge.
  • Forbes article
  • Forbes article, Feb 2012
  • Forbes article
  • caught wife tweeting about trip to Hawaii
  • caught wife tweeting about tripto Hawaii
  • Structured = employee/workforce data, parsed resumes, LinkedIn profilesUnstructured data = mobile, social and Internet
  • The Moneyball Approach to Recruitment: Big Data = Big Changes

    1. 1. The Moneyball Approach to Recruitment:Big Data = Big Changes Glen Cathey VP, Global Sourcing and Talent Strategy
    2. 2. Moneyball Moneyball: The Art of Winning an Unfair Game, a book by Michael Lewis about the Oakland Athletics baseball team, its general manager Billy Beane and his assistant Paul DePodesta Image: http://en.wikipedia.org/wiki/File:Moneyballsbn.jpg
    3. 3. Moneyball The central premise of Moneyball is that the collected wisdom of baseball insiders (including players, managers, coaches, scouts, and the front office) over the past century with regard to player selection is subjective and often flawed.
    4. 4. Moneyball The Oakland A’s didn’t have the money to buy top players, so they had to find another way to be competitive. Source http://www.flickr.com/photos/leadersevents/5964095428/ Billy and Paul took an analytical, statistical, sabermetric* approach to assembling their team, picking players based on qualities that defied conventional wisdom and the beliefs of many baseball scouts and executives. Source*Sabermetrics is the specialized analysis of baseball through objective evidence, especially baseball statistics that http://www.flickr.com/photos/sheridan/6324889354/measure in-game activity. The term is derived from the acronym SABR, which stands for the Society for AmericanBaseball Research.
    5. 5. Moneyball In 2002, with approximately $41 million in salary, the Oakland A’s were competitive with larger market teams such as the New York Yankees, who spent over $125 million in payroll that same season. They finished 1st in the American League West and set an AL record of 20 consecutive wins. Source: http://en.wikipedia.org/wiki/File:Osmar_Schindler_David_und_Goliath.jpg
    6. 6. Moneyball The Boston Red Sox built their 2004 team with Moneyball in mind. They won the World Series in 2004, after failing to do so for 86 years. Coincidence? Source: http://www.flickr.com/photos/eggplant/22414700/
    7. 7. Moneyball When sabermetrics was introduced into baseball, it was immediately rejected by many simply because it was new, different, leveraged statistics over intuition and experience, and frequently questioned conventional wisdom with regard to traditional measures of baseball skill evaluation. However, today, many MLB teams have full time sabermetric analysts.
    8. 8. Moneyball "You dont put a team together with a computer." - Grady Fuson, former Oakland As Scouting Director
    9. 9. Moneyball Much of what is accepted as sourcing, recruiting, interviewing and hiring best practices today is largely based upon conventional wisdom - ideas or explanations that are generally accepted as true. Conventional wisdom can be a significant obstacle to advancement because it is often made of ideas that are convenient, appealing and deeply assumed.
    10. 10. Moneyball "Its human nature to stick with traditional beliefs, even after they outlast any conceivable utility" - Jim Pinkerton, What Comes Next
    11. 11. Moneyball At some point assumptions and traditional beliefs can and should be violently shaken when they no longer match reality at all. Some people would call this violent shaking of conventional wisdom disruptive innovation, and I believe it is coming to talent acquisition in the form of Moneyball recruiting.
    12. 12. Moneyball What Could Moneyball Recruiting Look Like?
    13. 13. Moneyball Recruiting Moving away from using largely subjective means of assessing talent and making hiring decisions to more objective, fact and empirical data-based means. Identifying and acquiring top talent looking for traits, experience, accomplishments and information overlooked by traditional recruiting and assessment methods.
    14. 14. Moneyball Recruiting Challenging conventional wisdom as to what top talent looks like and where it comes from: • Specific industry, company, or competitor experience • ANY prior experience • Specific universities • High G.P.A.s • Certifications • M.B.A’s
    15. 15. CEO Data Only 3.9% of American men are 6 feet, 2 inches in height or taller, yet 30% of all Fortune 500 CEOs are 6 foot 2 or taller. -Malcolm Gladwell, Blink: The Power of Thinking Without Thinking The Implicit Association Test (IAT) measures your level of "unconscious prejudice" - the kind of prejudice that you have that you arent aware of, that affects the kinds of impressions and conclusions that you reach automatically, without thinking.Source: http://www.gladwell.com/blink/blink_excerpt2.html
    16. 16. IAT
    17. 17. https://implicit.harvard.edu/implicit/
    18. 18. Leadership Height "No one ever says, dismissively, of a potential CEO candidate that hes too short. This is quite clearly the kind of unconscious prejudice that the IAT picks up." "Most of us, in ways that we are not entirely aware of, automatically associate leadership ability with imposing physical stature. We have a sense, in our minds, of what a leader is supposed to look like, and that stereotype is so powerful that when someone fits it, we simply become blind to other considerations." -Malcolm Gladwell, Blink: The Power of Thinking Without Thinking
    19. 19. CEOs Only? When corrected for variables like age and gender and weight, an inch of height is worth $789 a year in salary. That means that a person who is six feet tall, but who is otherwise identical to someone who is five foot five, will make on average $5,525 more per year. Timothy Judge, one of the authors of the study, points out: "If you take this over the course of a 30-year career and compound it, were talking about a tall person enjoying literally hundreds of thousands of dollars of earnings advantage."
    20. 20. Australia Too Tall & Stupid – Meet the CEO "The average height of an Australian Male in 1995 was measured at 174.8cm (~ 5ft 9in) by the ABS. I am 174cm (5ft 8in & ½) tall. So imagine my irritation when I met with the CEO of a major (AUD$1B in revenue) Australian organisation who was around 188cm (6ft 2")."Source: http://www.onesock.net/2010/09/meet-the-ceo-tall-stupid/
    21. 21. Australia Too "He followed me around the board room as we chatted and not only stood to close to me, but was even leaning over a bit. I realised afterwards that he was used to using his height to his advantage, which I thought was a bit stupid, so I decided to do a bit of research to make myself feel better."Source: http://www.onesock.net/2010/09/meet-the-ceo-tall-stupid/
    22. 22. Australia Too Andrew Leigh of Australian National University found that in Australia, taller people get paid more. For women, the researchers estimated that another 10 cm of height is associated with a 2 per cent increase in hourly wages. This is approximately equivalent to the wage returns from an additional third of a year of education, or another 4 years of labour market experience!Source: http://people.anu.edu.au/andrew.leigh/pdf/BodySize.pdf
    23. 23. Attractiveness In America, a person whose looks are in the top third will earn around 5% more, on average, than a person who, except for facial beauty, is otherwise exactly the same, even on seemingly related factors, such as self- esteem. Beauty Pays: Why Attractive People are More Successful Daniel S. HamermeshSource: http://hbr.org/2011/10/hot-or-not/ar/1
    24. 24. Attractiveness By Hamermesh’s calculation, the difference in the earnings of someone good-looking versus someone bad-looking might be $230,000 over a lifetime. His conclusion: The role of beauty in labor markets is pervasive. Beauty Pays: Why Attractive People are More Successful Daniel S. HamermeshSource: http://hbr.org/2011/10/hot-or-not/ar/1
    25. 25. Back to Moneyball Explore objective measurements that are relevant across any role, responsibility, company, and industry Imagine a score that can stick with each person as they move through their career, similar to a credit score…
    26. 26. Identified.comIf you think a score that can stick with each person as they movethrough their career is far fetched and an impossibility, Identified.com(funded in part by Google CEO Eric Schmidt, by the way) has alreadytried to come up with a numerical score for individuals based on workhistory, education history, and social network.
    27. 27. Identified.com
    28. 28. Identified.comIt has some serious flaws (how companies and universities areweighted and ranked, how someone with more than 1 job can beranked higher than someone who has only worked at 1employer, etc.) but it shows that there is already a movement to tryand represent and rank people based on a single numerical score, andIdentified.com won’t be the only foray into that space. I believe thatthere is a significant opportunity for companies to develop their owndata-based and statistically driven talent identification and acquisitionmodels.
    29. 29. Intelligence While conventional, when it comes to hiring, the single factor that’s the best predictor of performance across all jobs is not personality, not interview performance, not prior work experience, but intelligence.Source: http://www.downtothehire.com/moneyball-or-moneyhire-hire-on-intelligence/
    30. 30. Intelligence If you were just to select candidates based on intelligence, 65% of the time, you’d be selecting the top performing candidate out of your pool of applicants.Source: http://www.downtothehire.com/moneyball-or-moneyhire-hire-on-intelligence/
    31. 31. How Good is Intelligence? "When performance is measured objectively using carefully constructed work sample tests (samples of actual job tasks), the correlation (validity) with intelligence measures is about .84 – 84% as large as the maximum possible value of 1.00, which represents perfect prediction." Handbook of Principles of Organizational Behavior: Indispensable Knowledge for Evidence-Based Management "Select on Intelligence" by Frank L. Schmidt
    32. 32. Intelligence This isnt just IT people – its steel workers and police officers too! Despite beliefs to the contrary, hiring on job experience is inferior to hiring on General Mental Ability (GMA).
    33. 33. Procter & Gamble P&G, a company with 130,000 employees in 80 countries, uses an online, unsupervised cognitive assessment involving pattern recognition to determine GMA. Using pattern recognition is brilliant because it doesnt require any translation cost. P&G found this to be the strongest prediction of performance available from a single test, developed and calibrated with 180,000 people and validated with 2000 employees. Source: Procter & Gamble
    34. 34. Data and Analytics Is anyone using data and analytics heavily in their recruiting efforts?
    35. 35. Google "All people decisions at Google are based on data and analytics," -Kathryn Dekas, a manager in Google’s "people analytics" team This includes compensation, talent management, hiring and all other HR decisions. Some would argue that Google’s data-based HR may become a key factor in the company’s future success.Source: http://venturebeat.com/2011/09/20/people-analytics-google-hr/
    36. 36. Capital One has automated data reports on employee attrition, headcount and promotions and is beginning to analyze the characteristics of its most successful employees, like what schools they went to and what their majors were. "Now we’re going back through resumes and creating a lot of that data." - Mark Williams Statistical Analysis Manager for Workforce Analytics at Capital OneSource: http://it-jobs.fins.com/Articles/SBB0001424052702304444604577342260799623438/Moneyball-and-the-HR-Department
    37. 37. Their time to fill for external candidates was an average of 96 days, and management assumed the recruiting team was at fault. Statistical analysis found that the real cause was hiring managers dragging their feet about making decisions about who to hire. They have since reduced their time to fill to 46 days.Source: http://venturebeat.com/2011/09/20/people-analytics-google-hr/
    38. 38. Luxottica also uses analytics to determine if they are promoting their best employees. "Are we actually moving high potential people?" "Why is this person [who rates highly] in the way we evaluate talent in the same job they were four years ago?" - Sean Dineen VP of Talent Management and Organizational DevelopmentSource: http://venturebeat.com/2011/09/20/people-analytics-google-hr/
    39. 39. CompanMoneyball RecruitingCompanies will break away from the ideathat the only way to hire great people is to“buy” and poach them from competitors orspecific companies (look at how incestuousFacebook, Google, LinkedIn, Microsoft, Apple and Yahoo are with regard to their talentpool)Companies will develop "secret sauces" forsourcing, analyzing and evaluating potentialhires based on their own data and factualstatistical analysis of the makeup of theirideal hire and employee Source: http://www.allfacebook.com/infographic-facebook-winning-war-for-best-talent-2011-06
    40. 40. Moneyball Recruiting Pay top dollar for an already highly paid industry retread? Develop and use a structured and proven data and fact-based methodology for identifying the next superstar from a non- obvious company out or straight out of school?
    41. 41. Moneyball Recruiting What do all of these people have in common? • Steve Jobs • Bill Gates • Mark Zuckerberg • Michael Dell • Sean Parker**Napster, Plaxo, Facebook, & Spotify ring a bell? He was making $80K/yr in high school & was recruited by the CIA
    42. 42. Moneyball What if you could leverage data to identify the potential in people before they were 18, regardless if they were on a path to college or not? How many brilliant, high-potential people could be given the right opportunity to fully realize their potential, regardless of whether or not they were born into the right family, in the right place, at the right time, and the stars aligned for them to be able to attend a prestigious university, let alone any college?
    43. 43. Moneyball Does the technology exist? Companies already spend millions on business intelligence software for marketing, product development, sentiment analysis, healthcare, etc. Why dont companies similarly invest in human capital analytics?
    44. 44. “What if you could increase revenue by 66% using your data to make confident, fact-based decisions?” Source: SAS ad
    45. 45. “What if you could increase revenue by 66% using humancapital data to make confident, fact-based recruiting and hiring decisions?” Source: SAS ad
    46. 46. Big DataLinking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
    47. 47. Big Data Wikipedia claims that "Big data is a term applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time." "Big data sizes are a constantly moving target currently ranging from a few dozen terabytes to many petabytes of data in a 2.5 exabytes (quintillion bytes) of data single data set.” are being generated every day
    48. 48. Big Data Other sources attempting to define big data include "the tools, processes and procedures allowing an organization to create, manipulate, and manage very large data sets…" Regardless of definition, the big data concept centers around huge amounts of data that are not only increasing in volume, but also in velocity and variety.
    49. 49. Data VolumeSource: Mashable - http://mashable.com/2012/03/06/one-day-internet-data-traffic/
    50. 50. Data Velocity The data velocity aspect is the speed at which new data is generated. One example of the increasing velocity of human capital data would be social media posts/updates. For example, Twitter crossed the 340,000,000 tweets/day mark on March 21, 2012 - that’s 1 billion tweets every 3 days!
    51. 51. Data Variety Human Capital Data: • ATS CVs • LinkedIn, Facebook, Twitter, Google+, etc. profiles and updates • Youtube, Quora, Flickr, Github, Stack Overflow, etc. • Mobile check-ins and updates • Recommendations/awards/endorsements • Blog posts and comments • Press releases/announcements • and much, much more!
    52. 52. The Big Deal "The amount of data in our world has been exploding and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus…" - The McKinsey Global Institute Big data: The next frontier for innovation, competition, and productivity
    53. 53. The Big Deal "From the standpoint of competitiveness and the potential capture of value, all companies need to take big data seriously. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value from deep and up to real time information. Indeed, we found early examples of such use of data in every sector we examined." The McKinsey Global Institute Big data: The next frontier for innovation, competition, and productivity
    54. 54. How Powerful is Big Data? "Make no mistake: Big Data is the new definitive source of competitive advantage across all industries. Enterprises and technology vendors that dismiss Big Data as a passing fad do so at their peril and, in our opinion, will soon find themselves struggling to keep up with more forward-thinking rivals." - Big Data Manifesto, Wikibon**Wikibon is a professional community solving technology and business problems through an open source sharing of free advisory knowledge.
    55. 55. Big Data Potential Big Data isnt a new concept – just new to HR Forbes estimates big data to be a $50B market in 5 years! Big Data is more than an I.T. play - the majority of the 2011 big data-related revenues came from services (44%), followed by hardware (35%), and then software (21%).
    56. 56. How Powerful is Big Data? Catalina Marketing is in the coupon and promotions business, and they can deliver real-time insights in less than a second of a retail transaction. Their primary database holds more than 2.5 petabytes (1015) of information and adds data on more than 300 million retail transactions per week.
    57. 57. How Powerful is Big Data? "When you check out with a loyalty card at any one of 50,000 grocery, drug, or mass-merchandise retail stores in the U.S., Europe, and Japan (and in many stores, more than 90% of customers use loyalty cards), insight derived from Catalinas database triggers promotions and offers based on your past purchases. The coupons stream out of Catalinas point-of-sale printers at every checkout lane and are handed to customers along with their receipts within seconds of the transactions."Source: http://www.informationweek.com/global-cio/interviews/catalina-marketing-aims-for-the-cutting/231600833
    58. 58. How Powerful is Big Data? Target figured out that there are about 25 products that, when analyzed together, allow them to assign each shopper a "pregnancy prediction" score. They can even estimate a due date to within a small window, so Target can send coupons timed to very specific stages of pregnancy.Source: http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/
    59. 59. How Powerful is Big Data? An angry man went into a Target outside of Minneapolis, demanding to talk to a manager: "My daughter got this in the mail!" he said. "She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?"Source: http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/
    60. 60. How Powerful is Big Data? After the manager apologized in person and then called a few days later to apologize again, the father admitted: "I had a talk with my daughter," he said. "It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology." Image: http://www.flickr.com/photos/craigmdennis/3027962567Source: http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/
    61. 61. Moneyball Recruiting "You dont put a team together with a computer." - Grady Fuson, former Oakland As Scouting Director
    62. 62. Yes, You Do. Salesforce.com wanted to hire top sales people from Oracle, so what did they do?
    63. 63. Oh, the things people tweet Salesforce.com was able to identify 83 of Oracles top sales people based on social data, including people tweeting about attending Oracles annual presidents club trip to Hawaii. They identified one person because their spouse tweeted several times about the presidents club trip – evening using the event and company hashtags While Salesforce used their own Radian6 tool to accomplish this, any company can accomplish mush the same thing with Hootsuite
    64. 64. How Powerful is Big Data? Salesforce.com also generates "derived intelligence" from the data they collect, and they can predict whether someone is loyal to their current employer or non-loyal and thus more likely to be "recruitable" They leverage data in the form of employment status changes, social network updates, relationship status updates (yes, divorce can render someone more recruitable), poor company earnings releases/stock performance, commute length, company re-orgs, and even weather dissatisfaction!
    65. 65. The Big Data Deal The dig deal about big data is that data can be used to make better decisions. While McKinsey found that some companies are using data collection and analysis to make better management decisions, there is a huge opportunity to collect and analyze human capital data, specifically to make better hiring decisions - to gain a holistic advantage over competitors by finding, identifying, and enabling the recruitment of top talent.
    66. 66. Source: Karmasphere
    67. 67. Social Data Aggregation and Disambiguation The Social CV and TalentBin are two brilliant examples of social data aggregation and disambiguation solutions. You can use either to search for and find people theyve indexed across multiple social channels and internet sources. Each can determine the difference between people with the same name, and for each person, you can see and gain access to their social and online presence (LinkedIn, Facebook, Twitter, Blogs, StackOverflow, YouTube, Quor a, Meetup, Github, etc.)
    68. 68. TalentBin
    69. 69. TalentBin
    70. 70. SocialCV
    71. 71. Geologists and Geophysicists Too!
    72. 72. Data Science The sourcers of tomorrow will be human capital data scientists Data scientist = the hottest job you havent heard ofSource: http://www.dataists.com/2010/09/the-data-science-venn-diagram/
    73. 73. Data Science Data scientists are an integral part of competitive intelligence. Ken Garrison, CEO of the industry group Strategic and Competitive Intelligence Professionals (SCIP), explains, "The field involves collecting data, analyzing it and delivering the data as intelligence that is actionable," giving businesses a competitive edge.
    74. 74. Human Capital The future belongs to the companies who figure out how to collect and use human capital data successfully. That’s because the companies that can consistently hire great people, through identifying people and basing hiring decisions on data and not intuition and conventional wisdom, are more likely to develop the best teams. And the best teams win.
    75. 75. Moneyball The old guard in baseball thought that using statistics and unconventional measures of performance defied everything they knew about baseball. They were right.
    76. 76. Moneyball Recruiting If you think the idea of leveraging data and statistics to find and hire top talent defies everything we know about human resources and recruiting, I say you’re right. I also say it’s a good thing, and that we’re just getting started.
    77. 77. The Naïve Question "If we werent already doing it this way, is this the way we would start?" – Peter Drucker

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