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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.
Forgetting About The Science
Failing To Set Goals
Searching For Unicorns
Hiring The Wrong People
Ignoring Probabilities
Treating It Like A Fad
Resisting Evolution
Treating Models As The Real Thing
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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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Joao-Pierre S. Ruth, Senior Writer