The rise of big data – the ability to gather massive amounts of information about both environment and operations – rests on the assumption that having more data gives organizations better control and the ability to avoid nasty surprises. It doesn’t. To understand why, consider the Cuba missile crisis.
Fifty years ago exactly on October 16th, 1962, at precisely 8:45 AM, President Kennedy was informed that the Soviet Union had been secretly installing ballistic missiles in Cuba. It was the beginning of a crisis that brought the world to the brink of nuclear war. Yet Sherman Kent, founding father of the analytic arm of the CIA had repeatedly dismissed the possibility of such action by the Soviets., That morning, as he left the CIA Director’s office, he declared: “I’ve just been made a charter member of the bleeding assh*** society.”
How could the CIA have been blindsided about such a massive logistical operation, taking place for the previous ten months less than 150 miles from American soil? In short, how did it become the Cuban Missile Crisis? Every time a strategic surprise takes place, whether a revolution or attack on a government, or a disruption or competitor move in the business world, the same question comes back: how could they not see it coming?
Organizations scan their environment by relying on so-called weak signals that are meant to predict upcoming changes. Alan Greenspan’s favorite way of monitoring consumer confidence, for example, was tracking male underwear sales; others watch movie ticket sales, hotel occupancy rates, the earnings of Fedex and UPS, etc. Ivan Boesky hired the former head of the Shah of Iran’s secret police to track the registration numbers of private jets landing at airports closest to likely takeover targets to figure out which companies might be in play in M&A. This was later ruled illegal, but the search for “watch lists” and early warning signals goes on. In fact, there is often an abundance of weak signals.
Even worse, with the progress of computing power and the development of the Internet, the ability to scan the environment to detect patterns and weak signals has taken a new dimension with the advent of so-called ‘big data’. The idea is that developing what a Pentagon program called ‘total information awareness’, or massive real-time databases about competitors, customers, patents, etc., will provide organizations with complete control of their environment.
But will it? In her landmark study of the Pearl Harbor attack, Roberta Wohlstetter argues that the Japanese attack succeeded because of an overabundance of data: “At the time of Pearl Harbor the circumstances of collection in the sense of access to a huge variety of data were (…) close to ideal.” Problems arose, not from too little information, but from the inability to glean ‘information’ from mere ‘data’. She adds, “the job of lifting signals out of a confusion of noise is an activity that is very much aided by hypotheses.” (After 9/11, this is referred to as finding “Red flags in a sea of red flags”).
In the case of Cuba, the US had access to a vast amount of data. As the secret missile operation was building up as of January, 1962, so new data was piling up. For instance, through an informant in the Cuban ministry of defense, the CIA knew of the island’s meat requirements in nearly real time. By the fall, the Soviets were requisitioning thirty thousand pounds of meat daily. While the Cuban source was never told the number of troops for whom the requisitions were intended, he told the CIA that the Soviets were probably using about sixteen ounces per day per man. If so, this would mean that the Soviets had more than forty thousand men in Cuba, a sure indication that a massive operation was underway. What did the CIA make of this? Nothing. The figure was deemed unlikely and the informant unreliable because the hypothesis that “The Soviets would never mount an operation on that scale, especially to install missiles in Cuba” held strong. The CIA’s ‘big data’ of the time was of no use until the lucky break of U2 aerial photos made it plain that its working hypothesis was just plain wrong.
Maybe as you read this, a nasty surprise is brewing and will come to your attention in the form of a 3AM phone call or email. Sure, your competitive intelligence people scan your environment and monitor weak signals like crazy, but on the basis of what hypotheses? How do they know what to look for?
In making sure your big data is more useful to you than it was for the CIA sixty years ago, you might want to consider this:
- Do you develop explicit, institutional opinions about your industry? If yes, do you systematically question them and the assumptions on which they are based?
- Do you encourage your advisors to act as ‘Socratic agnostics’, producing questions rather than answers?
- Do you scan your ‘big data’ with a view to challenge your hypotheses, not just confirm them?
- Do you maintain diverse sources of information, relying on big data as only one source among many?
- Do you prick up your ears when a “Cassandra”, persistent doubter, or industry heretic speaks out?
- Do you systematically investigate anomalies – i.e. get to the bottom of data that doesn’t fit?
Surprises will inevitably happen. Just make sure they don’t happen because you failed to question your hypotheses about your environment. Or soon you’ll find yourself an unfortunate member of Sherman Kent’s club.