For centuries people have been captivated by the idea of predicting the future. Crystal ball gazers and fortune tellers all promised to be able to do this. They played on our biases, weaknesses, and gullibility and counted on us attributing chance occurrences to their predictive powers.
The rise of predictive analytics gives us the ability to reduce uncertainty by applying statistics and determining the probabilities that future patterns will emerge in the behaviour of people and systems.
The Internet provides a platform for us to communicate, share, buy, play, and learn. And because people are largely creatures of habit and tend to repeat behaviours, our online activities when combined with today’s computing power and statistical knowledge, tell a lot about what we are likely to do. We can give odds, based on science, about what will most likely occur. To do this has required access to mountains of data about what we do, when we do it, how often we do it, and where we do it.
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By tracking things such as our location, Facebook likes, retweets, where we check-in, what and when we buy, what we search for and so on, analysts are able to make reliable predictions on our future behaviour. This data is often called “data exhaust” by analysts as in and of itself it has no real meaning or value. However, when aggregated, correlated, and combined and then analysed with the tools of statistics this data becomes not only relevant but commercially valuable.
We are being monitored and watched every time we log into any electronic device whether it is a computer, a mobile phone, a tablet, or a game. And everything we do is collected without us being aware. We do not give permission for it to be collected, in most cases, nor do we have any control over what is collected. And we have no way to turn off the monitoring.
For example, when we buy something, it is not hard to predict that we might buy more of it. It is even possible to narrow this down to specific types of items, the amounts we spend and the frequency we buy them. Or when we do something as simple as check into a restaurant or hotel, we leave a location trail as well as an economic trail. Combined with a profession, easily derived from a LinkedIn or Facebook profile, this data can predict with a high degree of certainty where we are likely to be at a given time. It can also predict how often we will be there, what kind of hotels we prefer, perhaps even the type of room we prefer, our income, and much more. And all of this can be sold to an hotelier or retailer, for example, without our knowledge or permission.
Kevin is a well known expert and futurist . He focuses on the future of work, recruitment, human resources, learning, and leadership development. He is a sought after speaker at events globally. He writes extensively about the future, ways to improve and streamline current work practices, and about emerging technology.