This article will cover the following:

• Introduction – Big Data Role & Skepticism
• Third Platform Poker – Big Data is Wild
• Big Data Versus Underground Railroad – Skeptic’s Corner
• Conclusion – Let’s Be Careful

Introduction – Big Data Role & Skepticism

Big data, like “cloud,” is kind of a stupid term, but, well, it’s what we have to work with. By the way, if you ever need a definition of big data, don’t trust anyone who doesn’t include either the word humongous or gargantuan in their description. The word massive simply isn’t extreme enough to befit the scope of this treasure trove of computing information. It is not small or average in size.

Now that I’ve gotten that out of my system, let’s look at big data within the context of the third platform, what Mark Neistat of US Signal Company calls “the next phase of the IT revolution.” Then we can explore a November 7 Slate piece that questions the wisdom of collecting every possible piece of information (on a personal level, regardless the benefits for government and industry).

Third Platform Poker – Big Data is Wild

Neistat describes the third platform (which I discussed previously in this blog), the computing platform that is emerging following the mainframe (first) and personal computer (second). Its four cornerstones are cloud services, mobile devices, big data, and social media. Neistat considers big data to be the “wildcard of the 3^rd Platform.”

Big data is a broad concept that refers to the humongous, gargantuan pool of information collected by businesses on a daily basis. The fact that there is this incredible amount of information generated through computing is simple. However, its role within the third platform (working off the strengths of the other three components) is groundbreaking.

Big data, in the context of the cloud, is able to be processed at an extraordinary rate – often exceeding that of a supercomputer, per computer scientist Geoffrey Fox, PhD, of Indiana University. That rate of processing allows data scientists to find “patterns that can lead to insightful, life-saving, and profitable predictions.” Neistat gives the example of Google, which looked at flu searches nationwide in order to create a forecast of outbreaks.

The problem at this point is that no one is sure how to develop actionable strategies to make use of the mass of information. Nine out of ten business decision-makers in North America think that their firms are wasting the money-making opportunities that data presents. Big data is wild. With the cloud, you can potentially tame it. If you do, it will be converted from a maelstrom of transaction and search data into “profitable business intelligence.”

Big data Versus Underground Railroad – Skeptic’s Corner

Documentarian Morgan Spurlock, best known for his breakout 2004 film Super Size Me, recently visited a data analytics company in the Dallas area. According to one of the employees (this is anecdotal information), Spurlock barged in wanting to talk to the CEO, but he had accidentally targeted the wrong building.

Although Spurlock was unsuccessful, that incident indicates how concerned some critics are becoming with the issue of data collection.

In the age of slavery, the Underground Railroad couldn’t have saved so many lives if it were established in the era of big data. That’s the basic argument waged by Alvaro M. Bedoya in Slate (in an article published just a few days ago). Bedoya reports that Thomas Jefferson, although he was outspoken against slavery, legally owned well over 100 people. He once used a newspaper advertisement to track down a missing slave named Sandy. Sandy was found.

As the Underground Railroad emerged, it became more difficult for slaveholders to recover their “property.” The Underground Railroad was an active revolution against an unjust system. Bedoya is skeptical that slaves would be able to escape in the era of the third platform and big data.

Problems With Data Use

It’s possible that big data will be oppressive for those in vulnerable positions in society, at least in certain ways. One example is research that has revealed people who have lengthier commute times are less likely to be loyal to their positions. If companies take that information and start denying employment to those who have to drive farther, the effect may be particularly detrimental to nonwhite applicants. Is that fair?

Problems with Data Collection

Bedoya isn’t just concerned with the use of existing data but with the way in which it is collected. Many people in industry are lobbying in Washington to deregulate data collection. The basic argument is that “in our data-saturated world, giving consumers meaningful control over data collection is next to impossible.”

Typically we say that a person should think before they act. Perhaps with data, we should think before we collect. However, that’s not the popular political and business argument du jour. Instead, use restrictions are being promoted to replace the protections that have standardly accompanied collection.

As Bedoya notes, “This isn’t a fringe argument.” In fact, it’s the perspective that has been advocated by the World Economic Forum of Davos and the White House (the latter via an analysis released by the Council of Advisors on Science and Technology).

Conclusion – Let’s Be Careful

These arguments about data policy are essentially about whether industry and government should be regulated or not. One thing is for certain: if you are exploring the possibilities of big data, you want the system to be secure, especially if it’s cloud.

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By Kent Roberts