Companies must teach employees to swim in oceans of data

Analytics is becoming a part of the job at every level of the organization, but few companies are prepared

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A full 70 percent of DataCamp’s students so far have been professionals working in the business world, he noted. “They get confronted with data in their jobs and need to be able to visualize and understand it.”

Future plans for the online school include courses on topics such as data cleaning, manipulation and modeling as well.

Educational programs are one approach to helping employees think about data in new ways, but they’re not the only one. Some organizations are moving in a different—and far more unconventional—direction.

At the Albert Einstein College of Medicine in New York, for instance, the Center for Epigenomics is working with artist Daniel Kohn for help cultivating new insights into genetic data.

“I’m interested in how we create meaning,” said Kohn, who previously spent a decade as the founding artist in residence at the Broad Institute for Genomic Research. “People tend to think that what we see is what is, and all we need to do is assimilate it.”

Born in India and raised in France, Kohn credits his multicultural and artistic background for his unconventional approach. “There was always the sense that you can bring multiple frames of reference to understand any situation,” he said. “We don’t just paint what is—we paint from a point of view with a tradition.”

Similarly, when viewing scientific data, it’s important to explicitly recognize the scientific traditions underlying your approach, along with the fact that they might not be the only possibilities, Kohn said.

It’s a common belief that tackling big data is simply a matter of building bigger machines and better algorithms. “I say you need to develop new metaphors to understand the reality you’re looking at,” Kohn said. “It’s the stories that give the meaning. We need to find out what are the new, appropriate stories for the world we’re looking at.”

Kohn’s approach is helping, said John Greally, director of Einstein’s Center for Epigenomics.

“His naivete is his strength,” Greally said. “He comes in and says, ‘what are you showing here? Why did you choose blue and red in that scatter plot? What if you could click on a dot and see a whole other dimension?’ He has a very good sense of what’s in front of him in a visual way, and it challenges us greatly.”

The hope is that, by encouraging scientists to think outside the traditional scientific “box,” Kohn will enable the sparks of insight that so often seem to come from out of the blue.

“A lot of discoveries are due to a certain amount of chance, when you have a flash of intuition that’s not just rational,” Kohn said.

Ultimately, what’s needed are new visualization tools that allow users to make intuitive judgments about data without requiring an extensive understanding of the systems at hand, Greally said.

The partnership has been in place for only about 18 months, so it’s still too early to assess the full benefits, Greally said. Still, at the very least, “I would say that the visual representations we’re doing now are probably more sophisticated” than the standard heat maps and scatter plots that are traditionally used.

In the past, analytics may have indirectly informed business decisions, but today, it has taken on a much more central role, said Kirk Borne, a data scientist and professor at George Mason University.

That, in turn, requires new, multidisciplinary approaches. In a company, that means giving a wide variety of employees data analysis skills.

An orchestra that consists of musicians who play only one type of instrument might give a few great performances, but, Borne said, “a truly great data-science team needs a variety of members in order to compose and perform great ‘music’ together.”

Copyright © 2015 IDG Communications, Inc.

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