This data-driven decision making, which was termed statistical process control, was an early example of enterprise-driven data exploration, using all possible data to understand and control manufacturing processes and determine the importance of data with statistical methods. While there have been many spectacular success stories of enterprise-driven data exploration like this, there have also been high-profile failures, most notably the intelligence and data sharing failures prior to the terrorist attacks of September 11, 2001.
Before then, during the summer of 2001, fragments of information possessed by the CIA, FBI, and NSA pointed to the possibility of a terrorist attack on American soil. Because this information was not shared swiftly and efficiently within and across these organizations, there was little chance of detecting what was to occur. CIA director George Tenet later reflected that “the system was blinking red during the summer of 2001.” The U.S. Congress investigated these intelligence failures prior to the terrorist attacks, and Senator Richard Shelby made one of the more articulate statements on these failures in a report, “Our joint inquiry has highlighted fundamental problems with information sharing within the intelligence community. Depriving analysts of the information access they need in order to draw inferences and develop the conclusions necessary for informed decision making. The intelligence community’s abject failure to connect the dots before September 11, 2001, illustrates the need to wholly rethink the community’s approach to these issues.” This is not to suggest that these attacks could have been prevented if the intelligence community had shared information, but simply to highlight the conclusions drawn in the wake of the attacks.
Enterprise-driven data exploration can satisfy the desire among today’s business leaders to “connect the dots,” in other words, to avoid catastrophic failures and experience levels of success like those of post-war Japan. This approach to decision making is part of a mind-set change involving how we think through problems and situations that is emerging in business schools and has been termed integrative thinking at the University of Toronto Rotman School of Business. Integrative thinkers search for nonobvious factors and causes for the problems they see, and they do not assume linear cause-effect relationships. These mind-set changes drive the need for data and are innovations themselves; they are indicative of “faith that there will be a future.”
