This document discusses the increasing use of big data and analytics in human resources. It provides examples of how data can be used to predict job performance, sales performance, and who may not be suitable for customer support roles. The document also discusses research comparing human judgment to statistical models in making predictions. While statistical models often outperform experts, combining multiple predictions from humans and models tends to provide the most accurate forecasts. Intuition alone is unlikely to be valid without direct experience and the ability to learn from mistakes. In conclusion, the document advocates applying what is already known about improving predictions by using simple models, multiple independent predictions, and combining human and machine judgments.

1. MAN VERSUS MACHINE
PROF DR MARC BUELENS

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OVERVIEW
1. Big data & HR: the hype has started.
If you can keep your head, when all around you are losing
theirs.
2. Some promising applications (social sciences, HR
…)
If you can dream and not make dreams your master.
3. Man versus Machine. Who’s the winner?
Lose and start again at your beginnings …
4. And what about intuition?
Fill the unforgiving minute with sixty seconds' worth of
distance run.
5. Some conclusions
Yours is the Earth and everything that's in it.
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1. BIG DATA, ANALYTICS & HR
THE HYPE HAS STARTED
“There is an even bigger opportunity to apply
Big Data to Human Resources” (Bershin, 2013)
“When Big Data meets HR” (The New York Times,
April 20, 2013)
“Human capital management is entering the
corporate mainstream as new tools make it
possible for business people without advanced
analytical training to manipulate Big Data”
(Brenon Daly, June 2, 2013)
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Of late, growing numbers of academics and
entrepreneurs are applying Big Data to human
resources and the search for talent, creating a
field called work-force science (NY Times,
8/6/2013)
Work-force science will increasingly be applied
across the spectrum of jobs and professions,
building profits, productivity, innovation and
worker satisfaction (NY Times, 20/4/2013)
Robot recruiters. Algorithms and big data are
powerful tools (The Economist, 6/4/2013)
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2. WHY?
So much wasted talent …
Biases, discrimination, stupidity
Efficiency, economies of scale …
A ‘learning’ attitude
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POTENTIAL APPLICATIONS ARE ENDLESS
What drives performance?
Can we predict whether a candidate will really
perform?
Can we predict who will leave the company?
The vast majority of recruitment, selection,
promotion, rewards, training, career planning
are made on gut feel, ‘”experience”, “beliefs”
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WHAT DOES CORRELATE WITH SALES PERFORMANCE IN A
LARGE FINANCIAL SERVICES COMPANY?
1. No typos, errors, grammatical mistakes on
resume
2. Where they went to school
3. What grades they had
4. Did not quit school before obtaining some degree
5. Had experience selling real-estate or autos
6. The quality of their references
7. Demonstrated success in prior jobs
8. Ability to succeed with vague instructions
9. Experience planning time and managing lots of
tasks
10. Sign of the Zodiac
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WHOM TO AVOID FOR CUSTOMER SUPPORT IN
CALL CENTRES?
Job hoppers
Job candidates with a criminal record
People who live nearby
People who can get to work easily
People who had joined one or two social
networks?
People who belong to four or more social
networks?
Honest people?
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PREDICTING JOB PERFORMANCE
THE MONEYBALL FACTOR
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3. Man versus Machine
When should we use our head
instead of formula?

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PHILIPPE TETLOCK
284 experts
Made predictions (yes-no) inside or outside
their domain
82 361 predictions
Compared to
Simple statistical models
Opinions of non-informed non-experts
Opinions of informed non-experts
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“Tetlock contends that the fox--the thinker who
knows many little things, draws from an eclectic
array of traditions, and is better able to improvise
in response to changing events--is more successful
in predicting the future than the hedgehog, who
knows one big thing, toils devotedly within one
tradition, and imposes formulaic solutions on ill-
defined problems.”
“He notes a perversely inverse relationship between
the best scientific indicators of good judgement and
the qualities that the media most prizes in pundits-
-the single-minded determination required to
prevail in ideological combat.”
Source: Armstrong, 2005
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THE SEERSUCKER THEORY OF PREDICTIONS
(ARMSTRONG)
“No matter how much
evidence exists that seers
do not exist, suckers will
pay for the existence of
seers.”
A Seersucker
Manifesto
27 Apr, 2012 - Kevin Gosa
No more dangerous fabric
has ever been woven,
washed, and worn in the
history of mankind than
seersucker.
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GROVE & MEEHL, 1996
Mechanical method wins: 64 studies
No significant difference: 64 studies
Clinical judgment wins: 8 studies
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“THE BROKEN LEG”
“A HIP-CASTED PROFESSOR WILL NOT GO TO THE
MOVIES”
Should be an objective fact, determinable with
high accuracy
The correlation with ‘immobilization’ is near
perfect
No interaction effects between broken leg and
other factors that influence going to the movies
The prediction does not use doubtful theories
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THE FINE ART OF WRONG PREDICTIONS
(MAKRIDAKIS)
The future is never exactly like the past. This means that the
extrapolation of past patterns or relationships cannot provide
accurate predictions.
Statistically sophisticated, or complex, models fit past data well but
do not necessarily predict the future accurately.
“Simple” models do not necessarily fit past data well but predict the
future better than complex or sophisticated statistical models.
Both statistical models and human judgment have been unable to
capture the full extent of future uncertainty. People who have relied
on these methods have been surprised by large forecasting errors
and events they did not consider.
Expert judgment is typically inferior to simple statistical models.
Forecasts made by experts are no more accurate than those of
knowledgeable individuals.
Averaging the predictions of several individuals usually improves
forecasting accuracy.
Averaging the forecasts of two or more models improves accuracy
while also reducing the variance of forecasting errors.
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BASIC RULES – (FIRST CONCLUSIONS)
Try to develop simple models
Know that only very few runners have broken
legs
Go for many independent experts
Avoid ‘convincing’. Embrace ‘informing’
Combine human and non-human predictions.
Combine it in a mechanical, not in a clinical
way!
Feed your simple model with good data and
learn ‘the errors of your ways’
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4. AND WHAT ABOUT INTUITION?
Half a Minute: Predicting Teacher Evaluations From
Thin Slices of Nonverbal Behavior and Physical
Attractiveness.
Ambady, Nalini; Rosenthal, Robert
Journal of Personality & Social Psychology. 64(3):431‐
441, March 1993.
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RED FLAG
YOUR INTUITION IS UNLIKELY TO BE VALID
When you did not have a varied,
direct, frequent exposure
When you did not ‘learn the
errors or your ways’
When it is not a warning signal.
When you are in the Bermuda
triangle of hope, anxiety and
greed
When we are not talking ‘thin
slices of behaviour’
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5. CONCLUSIONS
Men occasionally stumble across the truth, but
most of them pick themselves up and hurry off
as if nothing has happened.
Winston Churchill
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UNFORTUNATELY…
It is not always “and … and
…”; in many cases it seems
to be “or”.
We reap only what has been
sown.
In some areas it seems that
we never learn : “The
checklist manifesto”.
Let us at least practice what
we know.
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