The computers are 'watching' – data that can predict your future

Traditionally, ‘predicting’ the future was a process of trying to understand behaviour in the past and marrying this with predicted trends to produce an estimation of future behaviour. Advances in technology will change this as we move from using historic data to using live data and this will give rise to a world in which we are offered predictive, proactive choices.

Imagine finishing work, jumping into your car and seeing your phone flash the following text: “Twenty-eight minutes to your home in normal traffic conditions.” How did your phone know you were going home? In the future, all mobile devices will know this. They won’t only be able to predict your travel time, but your travel behaviours too. They’ll know where you’re going before you do!

How does predictive behaviour work?

Take public transport as an example. At 6am on any given day, your phone would send a notice of your intent to travel to an integrated mobility platform. You would then drive to your local train station. Based on this knowledge, your phone would indicate a desire to travel (demand) to the train function of the mobility platform. The system would then identify suitable services for you and identify any service delays or issues. This would be relayed back to you and you would replan your travel and, if required, nominate a new intent to travel.

What is its value?

Predictive travel keeps the consumer informed and in control of travel choices. It also provides visibility to transport and infrastructure operators of user demand. Imagine a scenario in which the predicted demand for a service exceeds capacity, as per current peak hour periods. Utilising predictive transport, the two-way feedback loop would provide alternative travel choices – alleviating congestion and consumer frustration. This kind of data could be a game changing force in terms of informing and shaping demand to match available capacity.

But predictive transport planning is just the tip of the iceberg. Already, computers are watching and mapping out your behaviours. They’re getting better at looking for trends and recording your habits. From this information, they can make predictions about what you want, where you want to go and what you might like to buy. Rather than relying on judgement calls, predictive analytics and self-learning algorithms will soon be able to make intelligent predictions about all sorts of things, and they will ‘serve’ you up a diet of your preferences, whether you actually knew you had a preference or not.

The use of real time data and predictive modelling could have an enormous impact on new ways of price setting across hundreds of industries. Transport is already priced in this way – you pay more for an Uber ride during peak hours than you do at other times. Imagine extending this model. It could influence the choices we make around where we eat (is the restaurant full?), and where we go for entertainment (where are my favourite movies showing tonight?). It could facilitate supply and demand price setting and provide ‘sale offers’ to you when demand is low, stimulating compulsive purchases and balancing supply and demand production at manufacturing plants and supply chains.

As a result of increased pricing at peak times, predictive economics could be used to smooth out peaks as opposed to engineering solutions that attempt to use storage systems or load shedding systems to smooth demand, or worse still, build more infrastructure to meet peak demand.

Matching supply to demand has been the nirvana that has eluded every manufacturer and every provider of mass infrastructure. With predictive analytics, nirvana may be within reach.

This is a whole new world of engineering. It rewrites many of the previously entrenched ‘rules’. Within this world of digital engineering, there is likely to be a digital solution to just about every problem we know. We just don’t know it yet.