Concrete Data

where there is

NO DATA

Who this deck is for

We – as management practitioners – love

data. We all love to back our proposals

with concrete data. Problem is: concrete

data is not always available, and in some

instances, plain impossible to collect.

Though anyone working with numbers will

benefit from this deck, it will especially add

value to

Marketers, whose action need to be based on

research and analysis

HR people, whose recommendations must be

backed by data

Senior managers and Accountants, who love figures

Who has bigger income?

or

Most of us can answer that

question, but very few of us

could do it in an

“academically sound” manner

But this guy – Steven D. Levitt, professor

of Economics at Harvard Business School

– did just that in his ground-breaking book

Freakonomics.

4 meaningful factors determine a wage

SUPPLY

SKILLS

UNPLEASANTNESS

DEMAND

SUPPLY

Many kids grew up

dreaming of becoming

an architect when they

grow up

SUPPLY

However, no kid has ever

grown up wishing they

would become a prostitute

SKILLS

Many institutions can

provide the education

needed to excel in their

profession

SKILLS

The only institution that can

make one excel at the

profession is called

“Experience”

UNPLEASANTNESS

Zer0 chance of injury and

violence.

UNPLEASANTNESS

Likelihood of random

violence and the lost

opportunity of having a

stable family life

DEMAND

The architect is more likely to

hire the prostitute than the

other way round…

None Many

No institution

provides these skills

Skills provided by

many institutions

Uncountable Negligible

Significantly greater Significantly less

CONCLUSION

The typical prostitute earns more than the typical architect

It is that

“making your ratio zero:something or something:infinity”

approach which makes his assertions concrete. The approach

is useful because although we cannot always find the data for

both sides of the equation, we can almost always find one

side. This deck is dedicated to highlighting the fact that if you

look enough and think enough, you can talk concrete data

even when all or part of the equation is missing.

RECAP

Thank You!