It is estimated that the average individual’s data footprint is just less than one terabyte.
According to Erez Aiden and Jean-Baptiste Michel’s book “Unchartered: Big Data as a Lens on Human Culture”, that is “equivalent to about eight trillion yes-or-no questions. As a collective, that means humanity produces five zettabytes of data every year: 40,000,000,000,000,000,000,000 (forty sextillion) bits.”
What does this data footprint mean?
Well to give us an idea, Aiden and Michel say that “if you wrote out one terabyte by hand, it would extend to Saturn and back twenty-five times.”
And “if you wrote out all five zettabytes that humans produce each year by hand, you would reach the galactic core of the Milky Way.”
Today’s data footprint is just the tip of the iceberg. With data doubling every two years, increases in social media uptake, improvements in data storage and faster internet connection speeds,is set to get bigger and bigger.
What are the implications of this for individual privacy?
According to Kenneth Cukier, co-author of the excellent book “Big Data: A Revolution That Will Transform How We Live, Work, and Think”, predictive analytics can have major consequences.
For instance, intelligence gathering, an essential part of needing to predict bad happenings, can ultimately end up penalising people who “only have [the] propensity to do [something], not for what they’ve done.”
Cukier adds that there is a fundamental and important difference between “what we want to measure and what we can measure.” This has significant implications for how employers, physicians, government agencies and law enforcement use the information they gather – and is hugely important for personal privacy.
A recent Forbes article by Michael Fertik highlights how companies are “aggressively targeting” individuals by using probability metrics.
Big Pharma can now identify all manner of information about people’s (possible) medical conditions and interests without setting eyes on medical records at all. While this may seem benign, the personal health data footprint can be used to the detriment of the consumer.
“Perhaps it’s a large Fortune 500 company that would much prefer to hire a workforce full of hale and healthy individuals who won’t drive up their premiums. (Goodbye, older workers, disabled employees, people with imperfect BMIs, workers with chronic but manageable diseases!). Maybe it’s the spouse who picks up the phone and learns from a telemarketer that his wife is pregnant or that her husband is worried about early signs of dementia. In fact, that has already happened – Target was able to market to a pregnant teen before her own father even knew she was expecting a baby, all thanks to her shopping habits.”
Has big data’s invasive dark side all but obliterated personal privacy?