8 Ways You're Failing At Data Science - InformationWeek
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11/26/2015
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Lisa Morgan
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8 Ways You're Failing At Data Science

Data scientists and the Wizard of Oz have something in common: Few people really know what they do behind the curtain, which makes it hard to tell good from bad data science. These tips can help you discern the difference.
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(Image: Geralt via Pixabay)

(Image: Geralt via Pixabay)

Data science would be easier to comprehend if there were a standard definition of it. True data science comprises several disciplines, including mathematics, statistics, machine learning, and computer science. A data science team must also understand how to curate and prepare data, analyze it, and present the results to business leaders in terms of potential business impact.

Many organizations are placing far greater emphasis on data than science, however. As a result, their outcomes may be falling short of expectations, and the reason for it may not be obvious.

Nevertheless, the search for the ultimate silver bullet continues. Companies are investing millions of dollars in platforms, solutions, and open source consulting resources hoping to get actionable insights that lead to competitive advantage. Doing data science right can take considerably more time and investment than may be apparent, however.

"It's really hard to get valuable, actionable insights out of data. You've got to build a team and use the scientific method," said Michael Walker, founder and president of the Data Science Association. "There are right ways and wrong ways to do it, and I think a lot of companies and governments are doing it the wrong way."

[ Having trouble making sense of disparate data? Read Data Visualizations: 11 Ways To Bring Analytics To Life. ]

Because the global demand for data scientists exceeds the number of qualified professionals, less qualified candidates are assuming the title. As a result, the data science practice in an organization may be less rigorous -- and ultimately less valuable -- than it would be if more qualified players were on the team.

"Data science is a formal methodology. You have a process. It's about having a hypothesis and testing it to see if the signals in your data really inform you of the things you think," said Kirk Borne, principal data scientist at Booz Allen Hamilton.

Testing a hypothesis sounds easy enough, but it's actually a lot more difficult and time consuming, and requires considerably more effort, than may be apparent to others in the organization. Here are a few things to consider if you want get more value from your data science efforts.

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Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include ... View Full Bio

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nasimson
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nasimson,
User Rank: Ninja
12/27/2015 | 9:03:20 AM
Re: The Searching for Unicorns Slide...
@Bertrand:

> we have a list from Kirk Borne, principal data scientist at Booz Allen Hamilton, of ten skill areas,
> but in it Machine Learning is listed twice

There are about ten reasons of this duplication. One of the reasons is that its doubly important ;)
LisaMorgan
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LisaMorgan,
User Rank: Moderator
11/30/2015 | 3:54:20 PM
Re: The Searching for Unicorns Slide...
Thanks for bringing that to our attention.  He actually did say, "about 10" which in this case equals nine but which I apparently took literally.  :-)
BertrandW414
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BertrandW414,
User Rank: Strategist
11/30/2015 | 3:37:03 PM
The Searching for Unicorns Slide...
In the "Searching for Unicorns" slide we have a list from Kirk Borne, principal data scientist at Booz Allen Hamilton, of ten skill areas, but in it Machine Learning is listed twice.
LisaMorgan
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LisaMorgan,
User Rank: Moderator
11/30/2015 | 11:43:04 AM
Re: models are not reality
Excellent example.
kstaron
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kstaron,
User Rank: Ninja
11/30/2015 | 11:29:02 AM
models are not reality
All of these are good points. I especially like the treating models as the real thing. If there is one variable unaccounted for, or estimated wrong it can easily throw off an entire model. Look at the global climate change model: Initially they accounted for the polar ice caps being white and  reflecting energy back. Now with massive forest fires the soot has turned the ice black so it's absorbing more heat causing more ice melt faster than predicted. Do something similar and it may be your business that sinks.
LisaMorgan
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LisaMorgan,
User Rank: Moderator
11/30/2015 | 10:55:04 AM
Re: Grasping at straws
Absolutely.  Let's hope the boss understands.  I feel for the people who are told, "Nevermind that.  I want the data to say..."
jries921
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jries921,
User Rank: Ninja
11/30/2015 | 10:22:40 AM
Grasping at straws
As inexplicable as one might find it, it is very possible that there really isn't enough of a signal in your data to build a reliable model; which means you either need more data, better data, different data; or you may have to let the boss know that what you're trying to predict isn't all that predictable.
shamika
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shamika,
User Rank: Ninja
11/30/2015 | 6:57:31 AM
Resources
Talk about resources Right data analysts seems to be a scare resource now. Most of them are good with theory, but when it comes to practical they can't perform the task.
shamika
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shamika,
User Rank: Ninja
11/30/2015 | 6:57:02 AM
Failing To Set Goals
This is so true. Many of the data analysts today collect data for the sake of collecting it. They don't know the big picture on the use of it. Sometimes they waste their time on collecting unwanted data which will not add any value to the business.
LisaMorgan
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LisaMorgan,
User Rank: Moderator
11/29/2015 | 11:34:38 AM
Re: ROI on your million dollar investments
Thank you!  
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