Julia King: We're all data scientists now

It's up to each one of us to figure out what in the daily surge of data is useful, what's crap and what's truly valuable

If you're like me and broke into a sweat when you were confronted with any kind of mandatory math or statistics courses in college, you're in for a world of hurt now -- a world where data reigns and making sense of it all is compulsory.

Every day, the world's data volume balloons by another 2.5 quintillion bytes, according to Eric Siegel, a former assistant professor at Columbia University and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die. I should explain to all the English majors out there that a quintillion is a 1 with 18 zeroes after it.

That daily surge in data includes point-of-sale information, customer service data from all of those conversations that are "recorded for your protection" and myriad digital emissions from your car, refrigerator and even from some artificial joints. Data associated with legal records, social media, financial calculations, government reports, supply chain updates and even signals from outer space is also piling up.

And here's the scary math part. It's up to each one of us -- and that includes fellow English literature majors, my friend -- to figure out what's useful, what's crap and what's truly valuable, not only in our work, but in our schools, our homes, our neighborhoods, even our bodies. Look no further than the nutrition facts on your granola bar.

That means attaining analytics literacy. This is especially critical as more and more people gain greater, faster mobile access to data on personal smart devices.

"As front-line workers have their capabilities augmented by digital technologies, they are emboldened to make informed, real-time decisions and encouraged to become more engaged with the organization," notes a recent report by McKinsey Global Institute.

But these workers must know how to deal with all of the data coming their way if it's to yield the flabbergasting productivity gains McKinsey predicts. In the manufacturing sector alone, the business consultancy maintains that big data and analytics can yield improvements in production, supply chain and R&D amounting to something between $125 billion and $270 billion.

The sobering news is this, courtesy of IDC analyst Dan Vesset: "Better or easier-to-use software tools alone are not going to solve the problem. There's also a need to educate people on how to interpret results, how to establish hypotheses and how to test them. It's very easy to royally mess up and come up with a wrong conclusion."

Virtually all organizations require the critical input of non-IT workers to define and collect the right data that will produce value. At Texas Children's Hospital, nurses, physicians and billing and administrative personnel all helped decide which data to collect on the hospital's electronic medical records.

"In healthcare, that level of analytics behavior and mindset is where we're going," to accomplish goals like improving patient health and reducing hospital stays, minimizing infection rates and cutting overall costs.

To prepare clinicians for their inevitable big data and analytics work, "I foresee applied analytics courses in college for everyone," says Davis.

So does Jennifer Priestly, a professor of applied statistics and data science at Georgia's Kennesaw State University, which already offers 50 different applied statistics courses, all of which require working on real-world data sets supplied to the university by participating corporations.

"I think data science is going to be as foundational to education as English," Priestly says. "The reason is everybody is dealing with data. Everybody has to understand the basics of how to analyze data. Data science doesn't belong in the business school. Everybody should take data science."

Like I said, English majors are in for a world of hurt. Roll over, Emily Dickinson.

Executive editor Julia King can be contacted at jking@computerworld.com.

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