Myths and Mathemagical Superpowers of Data Scientists

Data Scientists:Myths &
Mathemagical Powers
James Kobielus

James Kobielus shoots down
10 myths about Data Scientists

“Data Scientists: Myths and Mathemagical Powers,”
James Kobielus, Thinking Inside the Box, June 29, 2012

Myth #1

Data scientists are mythical
beings, like the unicorns.

Myth #2

Data scientists are an elite
bunch of precious eggheads.

Data scientists get their fingernails
dirty dumping piles of data into
analytical sandboxes, cleansing,
and sifting through it for useful
patterns that may or may not exist.
Then, they do it all over again.

Reality #2 IBMbigdatahub.com

Data scientists get their fingernails
It’s ofte
nu piles n mind- into
dirty dumpingm
bingly
of data
analytical sandboxes, detailed
grunt cleansing,
the sp work,
ort of a n useful
and sifting through it for ot
rm
data por may chairexist.
patterns that may hiloso not
phers.
Then, they do it all over again.


Myth #3

Data scientists are a nouveau
fad that will soon fade.

The term “data scientist” has been
around for years, and the various
advanced analytics specialties
that fall under it are even older.
Recently, the term has been used
in the convergence of disciplines
that have become super-hot.


The term “data scientist” has been
around for years, and the various
advanced analytics specialties
that fall growth
under n job
iit are even older.
Ste ady the academic been used
Recently,and term has.
st i ngs iable
unden
lithe convergence of disciplines
in ricula is
c ur fad.
that Thi s is no
have become super-hot.


Myth #4

Data scientists are all just
PhD statisticians who
failed to make tenure.

Many data scientists acquired
their quantitative and statistical
modeling skills in college, but
pursued degrees in business
administration, economics and
engineering. They actually know
about business problems.


M ny
Many dataascientists acquired
data s
c entis
you’ll and istatistical
their quantitativenco
e ts
the wo unter
modeling skills rking
in college, but in
are bu world
sine in business
pursued degreesss dom
sp e c ia ain
administration, economics and
l i st s !
engineering. They actually know
about business problems.


Myth #5

Data scientists are just BI
specialists with fancier titles.

Many longtime BI power users
are, in fact, data scientists of a
sort. They are business domain
specialists whose jobs involve
multivariate analysis, forecasting,
what-if modeling, and simulation.


nt
meBI power users
Many develop ey
er longtime
Care i f th
tdata scientists of a
are,yintall ou speed
a s fact, to
m p
y uare business domain
sort.t They e Hadoop
do n’ sta ik
on to ictiv
specialists e mod e ing.
pics l whose ljobs involve
pred
multivariate analysis, forecasting,
and
what-if modeling, and simulation.


Myth #6

Data scientists aren’t really
scientists in any meaningful
sense of the word.

Statistical controls are the
bedrock of true science—the core
responsibility of the data scientist. If
data scientists are confirming their
findings through statistical controls
and real-world experiments, they’re
scientists, plain and simple.


Statistical controls are the
bedrock of true science—the core
responsibility of the data scientist. If
True s
cience
data scientistsnare confirming their
othing is
withou
findings throughvstatistical tcontrols
obser
ationa
l data
and real-world experiments, .they’re
scientists, plain and simple.


Myth #7

Data scientists need fancy,
expensive statistical power
tools to get their work done.

The job of the data scientists is to
look for hidden patterns. They can
accomplish this through user-friendly
visualization tools, search-driven
BI tools and other approaches that
don’t require a deep mastery of
statistical analysis.


The job of the data scientists is to
look for hidden patterns. They can
accomplish rthisfo ory r cost- user-friendly
a ket through
The m explorat
visualization tools, y
ctive n search-driven
effe as ma g
BI tools tools h cludin
BI and other approaches that
don’t end ors, ina deep mastery of
v require gnos.
I BM C o
statistical analysis.


Myth #8

Data scientists simply pour
data into Hadoop and pull
out mind-blowing insights.

The data scientist will be the
first to tell you that Hadoop is
just another platform for deep
exploration into data.


There
i n’t a
The data scientistswill be the
Ouija magic
board
first to tell youich
wh that Hadoop h
throug is
the big
just anotherspirits sp forddeep
platform ata
eak to
me e m
exploration rintoodata. s u
rtals.


Myth #9

Data scientists are analytics
junkies who couldn’t care less
about business applications.

If you spend time with any real-
world data scientist, they’ll bend
your ear discussing how they
tackled a specific business problem,
such as reducing customer churn,
targeting offers across channels,
and mitigating financial risks.


If you spend time withnany real-
e t i st s
ta sci
world data ost da rds. They bend
Mscientist, they’ll
are n’t ne
your ear discussing how egarthey d
e ople r ingo
kn ow pbusinessl problem,
tackled a specific big data on.
al l th is g jarg churn,
u si n
such as reducing fcustomer
as con
targeting offers across channels,
and mitigating financial risks.


Myth #10

Data scientists don’t have any
responsibilities that force them
out of their ivory towers.

That used to be the case. However,
as next best action and real-world
experiments become ubiquitous, the
data scientist is evolving into the
role that stokes, tweaks and fuels
the operational engine.


That used to be the case. However,
Da best action and real-world
as nextta scien
analy tists te
s the
tic become t ubiquitous, the
experiments- cent
at the ric mo
dels
data scientistrt oevolving into the
hea is
busine f agile
ss pro tweaks and fuels
role that stokes,cess
es.
the operational engine.


For more from James Kobielus and
other big data thought leaders,
visit The Big Data Hub at
IBMbigdatahub.com

Myths and Mathemagical Superpowers of Data Scientists

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