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Moneyball & Data Analytics


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On 17 January 2014, HRBoss Japan organised a highly successful breakfast event held at Tokyo American Club centred on Data Analytics.

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Moneyball & Data Analytics

  1. 1. The Growing Role of Big Data in HR Bernie Schiemer Chief Executive Officer HRBoss Click on the twitter button to follow him on Twitter: @bernieschiemer
  2. 2. About HRBoss Founded in 2011 in Asia We are a globally minded data driven HR software solutions provider HRBoss fast facts: 86 staff in Asia 58 Males / 28 Females 19 nationalities 35 languages spoken Average age is 34 yrs old 7 countries (Singapore, China, Japan, Vietnam, Malaysia, Indonesia & Hong Kong) 3 software solutions
  3. 3. Award winning solutions Gold Winner at the SiTF Awards 2013 for best Cloud Solution for Singapore Winner of China HR Pioneer awards 2013 for top HR Big Data Solution in China Finalist in the 'Best SaaS Solution' (outside of the US) at The Cloud Awards 2013-14
  4. 4. Big Data In 2013 “Big Data” on Twitter was being mentioned 4,000+ times per hour! 25% of US organizations now have a data scientist on staff 34% of organizations say they have no formal strategy to deal with Big Data
  5. 5. DATA, DATA, …. 38% BIG DATA of organizations don’t understand what Big Data is according to CIO Magazine 75% of companies say they will increase investments in Big Data within the next year according to Avanade
  6. 6. Agenda Concepts of big data, analytics, Moneyball as they relate to HR and recruiting How facts become our friends and how this drives competitive advantages through fact based decision making The road to predictive analytics Obstacles faced with data management EmployeeBoss solution Why now? - Time to value Next steps
  7. 7. So What is Big Data? Big Data is often characterized by the 3-V’s: Volume  Large amounts of data, updating historical data sets Velocity  Speed at which new data is created Variety  Derived from many sources, and as a new event takes place, this can exponentially expand that variety and size of the data
  8. 8. Analytics versus Big Data Many people use the term “big data” when they are really referring to analytics and data-based decision making Many companies use analytics in human resources to analyze correlations between: 1. 2. 3. 4. Recruiting Assessment data Employee performance Retention
  9. 9. Analytics versus Big Data data-based decisions alone doesn’t correlate with “big data”… unless the data being analyzed meets certain criteria Grasping the concept of big data, there is still confusion as to exactly what “big data” is and what it is not
  10. 10. So What is Big Data?
  11. 11. Data explosion explained….
  12. 12. Big Data for employees…. analytics does not equal big data unless it meets the V3 criteria
  13. 13. What is Moneyball? Moneyball: The Art of Winning an Unfair Game by Michael Lewis The premise of the book is that the collected wisdom of baseball insiders is subjective and often flawed The Oakland A’s didn’t have the money to buy top players, so they had to find another way to be competitive In 2002 they took a sabermetric approach to assembling their team, picking players based on qualities that defied conventional wisdom Sabermetrics was originally defined by Bill James in 1980, as "the search for objective knowledge about baseball"
  14. 14. Facts become your friends They found that on-base percentage and slugging percentage are better indicators of offensive success than batting averages These qualities were cheaper to obtain on the open market than more historically valued qualities such as speed and contact They often picked players that other scouts and teams would overlook because the players didn’t have the right body type or they had a funny swing
  15. 15. So what happened?
  16. 16. 2002 Facts In 2002 the salary cap of the Oakland A’s was $41 million The A’s finished 1st in the American League West and set an American League record of 20 consecutive wins New York Yankees spent over $125 million in payroll that same season Though the Yankees made the World series finals they were swept by the Anaheim Angels in 4 four games
  17. 17. 2002 Facts The essence of “Moneyball” lies in using data and statistics to: “arbitrage miscalculated pay rates” to avoid overvalued skills/experience to identify undervalued skills when building teams and to develop a competitive advantage without having to “buy” expensive talent Though the A’s did not win the World Series, Moneyball allowed them to remain competitive and profitable in a market that was becoming dominated by big spenders
  18. 18. 2013 Facts The NY Yankees now employee a whole team of sabermetric analysts There is a real focus now on using historical data sets to analyze and predict future player performance The Boston Red Sox embraced the analytic Moneyball approach when they tried to poach Billy Beane from the Oakland A’s in 2002 Though Billy did not accept their offer, since 2003, they have won 3 World series titles These are the banners you see today on the Oakland A’s and New York Yankees websites...
  19. 19. Talent Analytics Maturity Model The ultimate aim of a big data solution is to reach the holy grail of insight called predictive analytics To be able to accurately forecast events before they occur Bersin at Deloitte forecasts 5 years to achieve this goal Of the 480 companies they spoke with only 4% have achieved any kind of predictive analytics capabilities
  20. 20. Why now? Now is the time to focus on talent analytics. Key drivers for some of our clients include: Employee retention – what creates high levels of engagement and retention? Sales performance – what factors drive high-performing sales professionals? Leadership pipeline – who are the most successful leaders and why are some being developed and others are not? Customer retention – what talent factors drive high levels of customer satisfaction and retention? Expected leadership and talent gaps – where are our current talent gaps in the organization and what gaps can we predict in coming years? Candidate pipeline – what is the quality of our candidate pipeline? How do we better attract and select people who we know will succeed in our organization? 2-3 years later START The path to predictive analytics the later you start the later you arrive…
  21. 21. Next Steps Click here to learn More about EmployeeBoss Click here to read the Data Analytics post-event blog