Big Data is changing the world.
It’s helping doctors to fight disease and prevent cancer. It’s also calculating maximum crop yields, which in turn helps to feed the masses and stave off deforestation. Big Data is even making space exploration possible.
As buzzwords go, it’s one of the buzziest of the last few years. The buzz is predicated on the fact that it gives us huge potential for change. “Big Data works on the principle that the more you know about anything or any situation, the more reliably you can gain new insight about what will happen in the future,” says Forbes.
We’ve already written about how Big Data is being used in healthcare and how it’s vital to manufacturing, so today we’re taking a look at some of the more interesting and useful (plus way less conventional) uses of Big Data in 2017.
When you think of Big Data in sports, most people think of the Brad Pitt-starring Moneyball. Based on the book of the same name by Michael Lewis, it’s about a manager who bucks tradition by signing players based on data rather than on the opinion of scouts. While some Moneyball-related player recruitment still happens, Big Data is changing the way many sports operate.
Amateur marathon running is one of the most interesting uses of Big Data, thanks to Connecticut man Derek Murphy, who is on a mission to stop marathon cheats.
To qualify to run the Boston marathon, men under 35 need to have run a previous marathon in three hours and five minutes or less; women of the same age have an extra half an hour.
As a result, there’s a burgeoning black market for ‘bibs’ to compete. Agencies offer packages whereby runners – ‘bib mules’ – can be hired to run a marathon to qualify as a participant under an assumed name.
Murphy’s work before the 2017 race led to 15 competitors having their entries revoked – eight for cutting the course during a qualifying run and seven more for hiring bibs. Looking at the data, Murphy identified one runner who took a train to skip 13km of the course. How did he know? The runner covered the section at a pace that would have shattered all known records.
He was also able to identify at least seven competitors who had cheated their way into the race in April of 2017.
In the American presidential election in November 2016, Clinton and Trump’s teams spent a combined $1.4 billion on digital advertising. Both the Democratic and Republican parties have over 900 data points on every member of the electorate (from credit reporting bureaus, marketing databases and subscription lists) with which they use to target ads on social media.
While the election was in 2016, much of the data analysis took place in the early part of 2017 – and it revealed some shocking insights into advertising tactics.
Comprehensive data analysis by ProPublica found that Facebook allowed advertisers to target people interested in anti-Semitic topics like ‘Jew hater’ and ‘how to burn Jews’. Slate showed how Facebook allowed users to target voters interested in phrases like ‘kill Muslimic(sic) Radicals’.
BuzzFeed found that Google offered groupings like ‘blacks ruin everything’ and other offensive racist terms.
The result? Facebook removed the ability for groups to self-report and promised to change its moderation policies.
In August of this year, Ecuadorian authorities raided a ship and found one of the biggest ever hauls of illegally caught sharks. 150 tonnes of shark had been captured – many of which were endangered and protected species. The ship was a ‘reefer’ – slang for a refrigerated cargo ship.
This ship didn’t catch the sharks – instead the reefer met other ships at sea to transport their catch back to land. So, Ecuadorian authorities were left with the question – who caught these sharks and how could they track them down?
The answer, as you’ve probably guessed, lay with Big Data. According to Quartz, “SkyTruth, a non-profit, retraced [the reefer’s] journey from China to Ecuador’s Galapagos Islands using Global Fishing Watch, a platform that employs artificial intelligence to collect and analyse ships’ satellite data, which SkyTruth created in collaboration with Google and another non-profit, Oceana, in 2014.
“After departing on July 7, it chugged steadily across the Pacific for a month and then, on August 5, suddenly stopped. But it wasn’t alone in the vast emptiness of the eastern Pacific. Soon, four vessels joined it, each sidling up to the reefer for about 12 hours at a time.”
The result? Authorities now know which boats transferred the sharks to the reefer and charges are currently being made against the guilty parties. f
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