Looming Gloom and Doom for Big Data

Author

Howard Baldwin, Contributor

May 11, 2015

If you listen to some people, it seems as though the four signs of the apocalypse are famine, war, death, and big data. There’s a whole lotta gloom and doom swirling out there about big data, so much so that it sounds like you’d better slip on your biohazard suit before delving into it. Or not.

Speaking Truth to Hypocrisy. Take Jacob Silverman’s piece in Salon at the end of April, excerpted from his new book, Terms of Service: Social Media and the Price of Constant Connection. Titled “Big Data’s big libertarian lie: Facebook, Google and the Silicon Valley ethical overhaul we need,” it skewers the hypocrisy of companies like Facebook and Google. One just wants to bring people all over the world together and the other’s corporate oath is don’t be evil, but all the while they’re making gazillions of dollars by essentially spying on what people do and say and then selling the information to whoever wants it.

It’s a long, in-depth provocative piece, suitable for transcontinental travel – but, I fear, ultimately both socialistic and Luddite in its viewpoint: “[A] handful of large companies have successfully socialized our data production on their behalves. We need some redistribution of resources, which ultimately means a redistribution of power and authority. A universal basic income, paid for in part by taxes and fees levied on the companies making fabulous profits out of the quotidian materials of our lives, would help to reintroduce some fairness into our technologized economy.”

Ever since the dawn of mass communication, survey-takers have gathered information from citizens. Did they pay them for it, beyond the token dollar bill that might come in an envelope? No. One could argue that what Facebook and Google only do something that’s been done for years, just on a bigger scale. And now, unlike before, we actually get something in return – easily accessible connectedness and information.

A Myth Is As Good As A Mile. Another slam at big data comes in an article entitled The Eight Most Common Big Data Myths, from Insead University affiliate professor of marketing Joerg Niessing and Strategy& consultant James Walker. It dissects issues we’ve touched upon in this column in the past, including the idea that big data isn’t just big, it’s diverse; the need for good data, not just big data; and the importance of filtering out noise. However, the authors are somewhat dismissive of real-time big data, which can be important in cybersecurity, among other areas, as well as somewhat inconsistent.

At one point they cite as myth the idea that analysts become all-important with big data, but then they admit that big data is “an awesome, game-changing tool, but only when wielded by people who know the right commands and coordinates.” That last thought is the truth among the myths: data from machines is great, but you need sentient beings to ultimate apply insight to its veracity.

Whose Ox Is Gored? Related to the idea of applying people to the big data equation is a CIO article from late last month wondering if better predictive analytics software meant “doom and gloom for data scientists and applied math experts” (whom I wondered about earlier this year). The shortage of data scientists is hobbling adoption in certain industries, such as health care, according to consultant Paddy Padmanabhan.

Coincidentally, I just returned from an industry conference on robotics, during which one of the presenters posited that while some people wring their hands about robots replacing humans in jobs, robots actually may become saviors. He cited demographic trends indicating that in some countries, half the population will be over 50 by 2030, and we’ll be in dire need of robots to do certain tasks.

Similarly, if we can’t find data scientists, there’s no problem at all with software that makes big data analytics easier.

The Lamppost and the Statistician. Finally, we wrap up with another Salon article, this time skewering the conclusions of a New York Times feature about the best and worst places to bring up children. Of all these stories, this one is the most damning toward big data because it speaks to the damage that “head-spinning conclusions” can do (he also cites my favorite quote about statistics, referring to them as being used the way “a drunk uses a lamppost, for support rather than illumination”). The Times story covers the work of a couple of Harvard economists, who were actually looking at income inequality and upward mobility.

But as writer Scott Timberg notes, “The criteria, it turns out, of what makes a place a best or worst place to grow up turns entirely, it seems, on the how much children from various income groups earn at age 26.” Hey, I grew up in Palo Alto and got a liberal arts degree from the university next door, but I’d hate for my income at age 26 to be used as an indicator for whether it’s a good place to bring up kids.

Timberg continues, “The fact that this kind of thing comes out of Harvard – a school attended, we’ll wager, by folks from Chapel Hill and Beverly Hills and Newport Beach at a far higher rate than from the Appalachian counties that do better in this survey — and the New York Times, still for all its faults America’s smartest newspaper – shows just how far the tech-utopian worship of Big Data has gone.”

The Thing To Remember. What’s the takeaway from all these stories about big data? The same as the takeaway from big data itself: never dispense with critical thinking, and don’t be afraid to draw your own conclusions.

This article was written by Howard Baldwin from Forbes and was legally licensed through the NewsCred publisher network.


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