myths and mathemagical superpowers of data scientists

Data Scientists:Myths & Mathemagical Powers

James Kobielus

James Kobielus shoots down10 myths about Data Scientists

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

Data scientists are mythical beings, like the unicorns.

Myth #1

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Data scientists are an elite bunch of precious eggheads.

Myth #2

Reality #2

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.

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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

It’s often mind-numbingly detailed grunt work, not the sport of armchair data philosophers.

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Data scientists are a nouveau fad that will soon fade.

Myth #3

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.

Reality #3 IBMbigdatahub.com

Reality #3

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.

Steady growth in job

listings and academic

curricula is undeniable.

This is no fad.

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Data scientists are all just PhD statisticians who

failed to make tenure.

Myth #4

Reality #4

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.

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Reality #4

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.

Many data scientists you’ll encounter in the working world are business domain specialists!

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Data scientists are just BI specialists with fancier titles.

Myth #5

Reality #5

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.

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Reality #5

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.

Career development

may stall out if they

don’t stay up to speed

on topics like Hadoop

and predictive modeling.

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Data scientists aren’t really scientists in any meaningful

sense of the word.

Myth #6

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.

Reality #6 IBMbigdatahub.com

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.

Reality #6

True science is nothing without observational data.

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

Myth #7

Reality #7

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.

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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.

Reality #7

The market for cost-

effective exploratory

BI tools has many

vendors, including

IBM Cognos.

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

Myth #8

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

exploration into data.

Reality #8 IBMbigdatahub.com

Reality #8

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

exploration into data.

There isn’t a magic Ouija board through which the big data spirits speak to us mere mortals.

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

Myth #9

Reality #9

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.

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Reality #9

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.

Most data scientists

aren’t nerds. They

know people regard

all this big data lingo

as confusing jargon.

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Data scientists don’t have anyresponsibilities that force them

out of their ivory towers.

Myth #10

Reality #10

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.

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Reality #10

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.

Data scientists test the analytic-centric models

at the heart of agile business processes.

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For more from James Kobielus and other big data thought leaders,

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