I don’t consider myself to be particularly morbid, but for most of my adult life I assumed that I would not live to retirement age. The reason was simple: both my father and my father’s father died in their early 60s. My grandfather died of cancer that was most likely caused by the environment in which he lived and worked. My father died of rheumatoid arthritis, a disease whose cause is still poorly understood but that is not believed to be genetically linked.
Nevertheless, without ever giving the matter a whole lot of thought, I lived my life – making decisions about everything from my career to my investments to my children’s education to estate planning – assuming that I wouldn’t actually be around to see the fruits of those decisions.
That was, until I stumbled upon a clever analytic tool designed to predict life expectancy. After entering answers to a series of what seemed like random questions about my zip code and dental hygiene, I learned that I had a life expectancy of 95 years. 95! That may or may not impress anyone else, but to me it was information that quite literally changed the way I make everyday decisions.
Predictive analytics as a science has been used most heavily in fields with high-stakes decisions – financial services, health care, government. Yet increasingly, it’s becoming part of our everyday lives. We’ve come to expect Amazon to recommend products that we’ll find useful; Pandora to identify songs we’ll love; LinkedIn to suggest (with sometimes startling accuracy) that we might be acquainted with someone on the other side of the world.
Yet as useful and amazing as these tools seem, we’re really still in the Stone Age when it comes to everyday predictive analytics. Over the next few years, we’ll see the introduction of analytic apps that are vastly more powerful and valuable than the crude, little life expectancy calculator that changed my life. And barring the possibility that I’ll be crushed by a passing meteorite, I expect to be here to reap the benefits.
