Analytics

Malaysian Airline Tragedy Gains Closure Through Big Data Science

29th Mar `14, 04:40 AM in Analytics

Malaysian Airlines light 370 had been missing for longer than any other aircraft before a statement early this…

BDMS
Guest Contributor
 

Malaysian Airlines light 370 had been missing for longer than any other aircraft before a statement early this week by the Malaysian Prime Minister was finally able to confirm that the plane had crashed in the southern Indian Ocean. For weeks, baffled officials and terrified family members from a dozen countries asked themselves and each other what had happened, and where the plane had gone. How was the Malaysian Prime Minister able to confirm the fate of a plane that, as of that declaration, had not been found? Popular media, such as CNN, implored officials with headlines such as “WHERE IS THE EVIDENCE?”. Clearly, they were far more used to evidence coming from pictures of wreckage than from obscure, “groundbreaking number crunching” and big data science.

We don’t know why the plane’s course was diverted, or who diverted it, but at least there’s been some official declaration of the fate of the flight and its 227 passengers. The search for flight 370 has also shown us that, though we asked our questions in the past tense (what happened?), a special kind of forecasting — a new way of thinking about potential future outcomes — is what finally ended the search.

Flight 370 was lost for so long because it left behind it only a handful of clues: a last radio check-in as the flight deviated from its northeastern flight path to turn east and south, toward the Indian ocean; an electronic handshake between plane and a station on the ground registered by a satellite; a blip on Malaysian military radar. Another partial handshake between the flight and the same satellite was disclosed by the Malaysian government only days ago — but that was all officials had.

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