The document discusses the significance of big data in human resources, highlighting its growth and the importance of data-driven decision-making in recruitment and employee analytics. It emphasizes the need for organizations to adopt effective strategies for managing big data, as many still lack formal frameworks to utilize these insights. Additionally, it illustrates the concept of 'Moneyball' in decision-making, using sports analytics as a metaphor for achieving competitive advantages through data.

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. 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. 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. 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. 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. 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. 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. 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. 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. So What is Big Data?

11. Data explosion explained….

12. Big Data for employees….
analytics does not equal big data unless it meets the V3 criteria

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. 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. So what happened?

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. 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. 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. 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. 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. Next Steps
Click here to
learn More about
EmployeeBoss
Click here to read
the Data Analytics
post-event blog