Humans are very, very good at rapid pattern recognition. It is the basis of the flight or fight response and based on our ability to see past events in current and future situations.
… humans are amazing pattern-recognition machines. They have the ability to recognize many different types of patterns – and then transform these “recursive probabalistic fractals” into concrete, actionable steps …
This fact is leading to a number of advances in AI leveraging big data approaches. It enables us to understand what is happening right now or what might happen in the future based on recognizing patterns found in historical data. And this is good – and bad.
In stable systems – businesses that dominate their markets in particular, but also in political parties, social groups and non-competitive systems – cognitive biases can make your pattern recognition superpower your kryptonite. How? By convincing you that new data – competitors, market behaviors, demographic shifts, and disruptions – are false.
Too often the reaction to these leading indicators is disbelief or even retrenchment. In institutions that lack high quality data driven practices confirmation and conservatism biases often become the norm furthering the notion that the old patterns still apply. All too often this results in, what appears to be, a sudden collapse.
The key to avoiding this fate is to consistently apply solid data driven approaches which negate the biases and our very human tendency to dismiss data that doesn’t conform to our known patterns. Acknowledging the reality of the data “as it is” and attempting to validate the data via consistent, unbiased best practices enables us to recognize changes in the underlying patterns more rapidly.
That ability – to be open to questioning your pattern recognition and the biases inherent in it – can become your real superpower. That ability to be data driven; to continuously evaluate the data to understand reality in an objective way and apply what is learned is the superpower of enduring, innovative organizations.