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June 3rd, 2020
Chances are if you live on the moon you will have heard of the search for the missing flight MH 370. As the facts have slowly become clear there is no doubt as to the enormity of the task at hand. However the time taken to find the plane and the passengers on board has us wondering how, in today’s age of big data and global technological reach, it has taken so long to locate a $250 million aircraft and ease the suffering of the families of the 239 people who were on board.
This seems to be a big data task like no other. There must be a huge variety, volume and velocity of data from a multitude of sources including civil and military radar systems from a number of countries, on board communications systems, satellite surveillance, mobile phone cells, human witnesses and background checks on the passengers. Not to mention the huge crowdsourcing initiative by US based satellite imagery company DigitalGlobe who are using big data analytics to help trace the missing aircraft. The website reported more than two million users, with more than 645,000 features tagged making it the largest Tomnod campaign in history, according to this article in DataCentre Dynamics.
But how is all this information managed and then turned into actionable intelligence? Has there ever been a project like this? Who is in charge, ie who is the programme manager? Do they have experience of something similar where they have had to pull a major project team together at extremely short notice to deal with a catastrophic event with many lives at stake? We’re wondering whether 9/11 or the Asian Tsunami were comparable events, and if so how was the information managed then and has today’s big data infrastructure helped speed things up?
We don’t pretend to have the answers to any of these questions, and hopefully by the time you are reading this the plane will have been found, but do you have any thoughts on the questions raised? Does anyone have experience of pulling together a project at such short notice and what was the outcome?
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