[Inside Big Data] Transforming Big Data Into Meaningful Insights

“Big Data” is all around us – it’s even on TV. Hulu fans most recently discovered this as they watched The Looming Towers. On the show, we helplessly watch as the FBI and the CIA fail to share data about the impending 9/11 attacks. Their inability to break down information silos allows obvious clues to become buried in a sea of unrelated data. This scenario is hardly unique. In fact, it resonates in many of the most infamous civil and corporate disasters. British Petroleum’s Deepwater Horizon rig explosion, Enron’s collapse, the Takata airbag recall; each of these disasters began with siloed data, the puzzle pieces of which – if properly pieced together – might have revealed the problem patterns leading to the event.

Take Deepwater Horizon, for instance. When one of BP’s largest oil rigs suddenly exploded, resulting in a massive oil spill into the Gulf Mexico, the event itself was actually the culmination of dozens of ignored warnings, worried messages, buried reports, and seemingly unrelated signals. BP and Transocean had tons of individual data points available – from emailed warnings to bypassed alarm systems – that, if pieced together, might have raised the red flags needed to avert disaster.

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