How anticipatory analytics would have saved the Death Star

How could a dominant empire, with access to seemingly unlimited resources and technology, be thwarted by such inferior foes? The answer is the lack of forward-thinking models—namely anticipatory analytics.

It is logical to believe the flawed design of the Death Star would go unnoticed by the Empire. They did not have the historical data to account for external threats. In predictive analytics, such data is referred to as longitudinal data, and lack of longitudinal data can be an extremely daunting problem in developing an analytic solution.

Even if the Galactic Empire had used advanced, predictive analytics to guide their decisions, it would only be based on past trends continuing in the future. Up until this point, the rebels were nothing more than a nuisance and never demonstrated any real threat. Thus, the lack of any longitudinal data to suggest the ultimate outcome would undermine a predictive modeling exercise. Unfortunately for the Empire, predicative analytics is not as accurate in identifying real-time signals that can alter future outcomes as anticipatory analytics.

Anticipatory analytics, on the other hand, builds on the foundation of predictive analytics where they can identify and adjust predictions based on inflection points such as the acceleration and deceleration of certain behaviors or sudden changes in direction. They are not based entirely on longitudinal data. Of course, it helps to anticipate future needs before a problem materializes. The Empire could have taken into account that the rebels would be able to find and exploit a weakness despite their smaller numbers and resources. By looking at the pace of innovation or observing new behaviors that signaled increases in rebel commitment, the Empire could have saved the Death Star.

Whether you’re planning to build a galactic super weapon, or just looking for ways to gain a competitive business advantage, make sure you can adjust your business models for new events. Because outside variables are always changing, models must be able to keep pace. In order to make this possible, the right mixture of data, processing tools, technology and expertise plays a central role. Keep in mind:

• Although historical data is easy to obtain, it is not always an accurate indicator of the future.
• The world is transforming before our very eyes at an exceptional rate; your analytic approach should take this into account.
• Make sure that you are looking for new data trends or observations in the data not previously experienced.
• It pays to make decisions that are not solely guided by historical data—as long as your process is empirical and consistently evaluated for appropriateness and effectiveness.

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