I recently blogged about the need to balance the hype of big data with some anti-hype. My hope was, like a collision of matter and anti-matter, the hype and anti-hype would cancel each other out, transitioning our energy into a more productive discussion about big data. But, of course, few things in human discourse ever reach such an equilibrium, or can maintain it for very long.
For example, Quentin Hardy recently blogged about six big data myths based on a conference presentation by Kate Crawford, who herself also recently blogged about the hidden biases in big data. “I call B.S. on all of it,” Derrick Harris blogged in his response to the backlash against big data. “It might be provocative to call into question one of the hottest tech movements in generations, but it’s not really fair. That’s because how companies and people benefit from big data, data science or whatever else they choose to call the movement toward a data-centric world is directly related to what they expect going in. Arguing that big data isn’t all it’s cracked up to be is a strawman, pure and simple — because no one should think it’s magic to begin with.”
In their new book Big Data: A Revolution That Will Transform How We Live, Work, and Think, Viktor Mayer-Schonberger and Kenneth Cukier explained that “like so many new technologies, big data will surely become a victim of Silicon Valley’s notorious hype cycle: after being feted on the cover of magazines and at industry conferences, the trend will be dismissed and many of the data-smitten startups will flounder. But both the infatuation and the damnation profoundly misunderstand the importance of what is taking place. Just as the telescope enabled us to comprehend the universe and the microscope allowed us to understand germs, the new techniques for collecting and analyzing huge bodies of data will help us make sense of our world in ways we are just starting to appreciate. The real revolution is not in the machines that calculate data, but in data itself and how we use it.”
Although there have been numerous critical technology factors making the era of big data possible, such as increases in the amount of computing power, decreases in the cost of data storage, increased network bandwidth, parallel processing frameworks (e.g., Hadoop), scalable and distributed models (e.g., cloud computing), and other techniques (e.g., in-memory computing), Mayer-Schonberger and Cukier argued that “something more important changed too, something subtle. There was a shift in mindset about how data could be used. Data was no longer regarded as static and stale, whose usefulness was finished once the purpose for which it was collected was achieved. Rather, data became a raw material of business, a vital economic input, used to create a new form of economic value.”
“In fact, with the right mindset, data can be cleverly used to become a fountain of innovation and new services. The data can reveal secrets to those with the humility, the willingness, and the tools to listen.”
Pondering this big data war of words reminded me of the E. E. Cummings poem i sing of Olaf glad and big, which sings of Olaf, a conscientious objector forced into military service, who passively endures brutal torture inflicted upon him by training officers, while calmly responding (pardon the profanity): “I will not kiss your fucking flag” and “there is some shit I will not eat.”
Without question, big data has both positive and negative aspects, but the seeming unwillingness of either side in the big data war of words to “kiss each other’s flag,” so to speak, is not as concerning to me as is the conscientious objection to big data and data science expanding into realms where people and businesses were not used to enduring its influence. For example, some will feel that data-driven audits of their decision-making is like brutal torture inflicted upon their less-than data-driven intuition.
E.E. Cummings sang the praises of Olaf “because unless statistics lie, he was more brave than me.” i blog of Data glad and big, but I fear that, regardless of how big it is, “there is some data I will not believe” will be a common refrain by people who will lack the humility and willingness to listen to data, and who will not be brave enough to admit that statistics don’t always lie.