A few years ago, a teenage girl received coupons in the mail for baby clothes, diapers, and cribs. Her father was angry – in his mind, the retailer was encouraging his daughter to have a baby.
What he didn’t realize is that she was already pregnant.
If her own family didn’t know she was pregnant, how is it possible that the retailer knew?
The answer lies in the capture and manipulation of big data.
The retailer in question was Target, and it was sending similar advertisements and coupons to expecting mothers all over the country.
By analyzing the buying habits of individual consumers, Target was able to detect and recognize patterns. These patterns could show, with shocking accuracy, who was most likely to become pregnant and when.
The Target story is perhaps the most infamous case of big data manipulation. But similar strategies have long been used in the world of marketing. A few that you have probably come in contact with are highly tailored Facebook ads, Amazon recommendations, and Netflix suggestions.
Today, the capture and manipulation of big data is starting to see other applications – it is no longer just a tool for marketers. In fact, some have said it could even play a role in preventing disease at the global scale.
Gary King, big data expert and Harvard professor, says, “big data’s potential benefits to society go far beyond what has been accomplished so far.” He goes on to cite the example of Google predicting flu outbreaks in the United States.
In the past, hospitals would eventually recognize flu outbreaks due to the influx of patients with flu like symptoms. This is obvious.
What’s not so obvious is how Google can pinpoint flu outbreaks long before hospitals even have a clue.
They do this by capturing and manipulating big data through the use of very complex algorithms. Simply by analyzing what people search for on the internet, Google can tell where the flu is breaking out. What else can they see and potentially prevent? Only time can tell.
Another example is that of Harvard professor Nathan Eagle, who was able to design an algorithm pinpointing cholera outbreak in Kenya. By analyzing patterns in the movement, he saw outbreaks two weeks before they happened.
If we can prevent the flu, cholera, and dozens of other contagions before they start, the global quality of life will improve. Not only will people live longer and healthier lives, but everyone will save big on health care costs.
There’s just one problem.
Sharing.
Data ethics is growing into a field of its own because of the complications in the sharing of data. Sure, Google might have the ability to prevent disease, but that’s not their business (yet).
That could, however, be the business of a non-profit, governments, or health care providers. But how do they access the big data archives of Google or other providers?
Data is worth money, but it’s not as simple as buying and selling. There are also highly sensitive security concerns.
Google, for example, knows a lot about you. It knows a lot about all of us. But is it really ethical to put that information in the hands of the government? What about selling it to another company?
Although this happens everyday, the complications are seemingly endless and we are far from understanding all of the ethical concerns of big data sharing.
Nathan Eagle says this about the marketplace of big data, “We need to figure out ways to mitigate that concern and craft data-usage policies in ways that make these large organizations more comfortable with sharing these data, which ultimately could improve the lives of the millions of people”
If governments, among other entities, can learn to ethically purchase large data sets from private, for-profit companies, they may be able to use that data to solve some major problems, disease prevention being just one.
Where will big data go next? Who wants it and what will they use it for? Join the conversation below.
