Malcolm Gladwell wrote an article in The New Yorker magazine in January, 2007 entitled “Open Secrets.” In the article, he pointed out that a national-security expert had famously made a distinction between puzzles and mysteries.
Osama bin Laden’s whereabouts were, for many years, a puzzle. We couldn’t find him because we didn’t have enough information. The key to the puzzle, it was assumed, would eventually come from someone close to bin Laden, and until we could find that source, bin Laden would remain at large. In fact, that’s precisely what happened. Al-Qaida’s No. 3 leader, Khalid Sheikh Mohammed, gave authorities the nicknames of one of bin Laden’s couriers, who then became the linchpin to the CIA’s efforts to locate Bin Laden.
By contrast, the problem of what would happen in Iraq after the toppling of Saddam Hussein was a mystery. It wasn’t a question that had a simple, factual answer. Mysteries require judgments and the assessment of uncertainty, and the hard part is not that we have too little information but that we have too much.
This was written before “Big Data” was a household word and it begs the very interesting question of whether organizations and corporations that are, by anyone’s standards, totally deluged with data, are facing puzzles or mysteries. Consider the amount of data that a company like Western Union deals with.
Western Union is a 160-year old company. Having built scale in the money transfer business, the company is in the process of evolving its business model by enabling the expansion of digital products, growth of web and mobile channels, and a more personalized online customer experience. Sounds good – but get this: the company processes more than 29 transactions per seconds on average. That’s 242 million consumer-to-consumer transactions and 459 million business payments in a year. Nearly a billion transactions – a billion! As my six-year-old might say, that number is big enough “to go to the moon and back.” Layer on top of that the fact that the company operates in 200+ countries and territories, and conducts business in 120+ currencies. Senior Director and Head of Engineering Abhishek Banerjee has said, “The data is speaking to us. We just need to react to it.” That implies a puzzle, not a mystery – but only if data scientists are able to conduct statistical modeling and predictive analysis, systematically noting trends in sending and receiving behaviors. Check out what Banerjee and Western Union CTO Sanjay Saraf have to say about it here.
Or consider General Electric’s aggressive and pioneering move into what’s dubbed as the industrial internet. In a white paper entitled “The Case for an Industrial Big Data Platform: Laying the Groundwork for the New Industrial Age,” GE reveals some of the staggering statistics related to the industrial equipment that it manufactures and supports (services comprise 75% of GE’s bottom line):
- A modern wind turbine contains approximately 50 sensors and control loops which collect data every 40 milliseconds.
- A farm controller then receives more than 30 signals from each turbine at 160-millisecond intervals.
- At every one-second interval, the farm monitoring software processes 200 raw sensor data points with various associated properties with each turbine.
Phew! I’m no electricity operations expert, and you probably aren’t either. And most of us will get no further than simply wrapping our heads around the simple fact that GE turbines are collecting a LOT of data. But what the paper goes on to say should grab your attention in a big way: “The key to success for this wind farm lies in the ability to collect and deliver the right data, at the right velocity, and in the right quantities to a wide set of well-orchestrated analytics.” And the paper goes on to recommend that anyone involved in the Industrial Internet revolution strongly consider its talent requirements, with the suggestion that Chief Data officers and/or Data Scientists may be the next critical hires.
Which brings us back to Malcolm Gladwell. In the aforementioned article, Gladwell goes on to pull apart the Enron debacle, and argues that it was a prime example of the perils of too much information. “If you sat through the trial of (former CEO) Jeffrey Skilling, you’d think that the Enron scandal was a puzzle. The company, the prosecution said, conducted shady side deals that no one quite understood. Senior executives withheld critical information from investors…We were not told enough—the classic puzzle premise—was the central assumption of the Enron prosecution.” But in fact, that was not true. Enron employed complicated – but perfectly legal–accounting techniques used by companies that engage in complicated financial trading. Many journalists and professors have gone back and looked at the firm’s regulatory filings, and have come to the conclusion that, while complex and difficult to identify, all of the company’s shenanigans were right there in plain view. Enron cannot be blamed for covering up the existence of its side deals. It didn’t; it disclosed them. As Gladwell summarizes:
“Puzzles are ‘transmitter-dependent’; they turn on what we are told. Mysteries are ‘receiver dependent’; they turn on the skills of the listener.”
I would argue that this extremely complex, fast moving and seismic shift that we call Big Data will favor those who have developed the ability to attune, to listen and make sense of the data. Winners in this new world will recognize what looks like an overwhelming and intractable mystery, and break that mystery down into small and manageable chunks and demystify the landscape, to uncover the important nuggets of truth and significance.