The future of data science looks spectacular

It wasn’t that long ago that we lived in an entirely analogue world. From telephones to televisions and books to binders, digital technology was largely relegated to the laboratory.

But during the 1960s, computing had started to make its way into the back offices of larger organisations, performing functions like accounting, payroll and stock management. Yet, the vast majority of systems at that time (such as the healthcare system, electricity grids or transport networks) and the technology we interacted with were still analogue.

Roll forward a generation, and today our world is highly digital. Ones and zeroes pervade our lives. Computing has invaded almost every aspect of human endeavour, from health care and manufacturing, to telecommunications, sport, entertainment and the media.

Take smartphones, which have been around for less than a decade, and consider how many separate analogue things they have replaced: a street directory, cassette player, notebook, address book, newspaper, camera, video camera, postcards, compass, diary, dictaphone, pager, phone and even a spirit level!

Underpinning this, of course, has been the explosion of the internet. In addition to the use of the internet by humans, we are seeing an even more pervasive use for connecting all manner of devices, machines and systems together – the so-called Internet of Things (or the “Industrial Internet” or “Internet-of-Everything”).

We now live in an era where most systems have been instrumented and produce very large volumes of digital data. The analysis of this data can provide insights into these systems in ways that were never possible in an analogue world.

Data science is bringing together fields such as statistics, machine learning, analytics and visualisation to provide a rigorous foundation for this field. And it is doing this in the same way that computer science emerged in the 1950s to underpin computing.

In the past, we have successfully developed complex mathematical models to explain and predict physical phenomena. For example, we can accurately predict the strength of a bridge, or the interaction of chemical molecules.

Then there’s the weather, which is notoriously difficult to forecast. Yet, based on numerical weather prediction models and large volumes of observational data along with powerful computers, we have improved forecast accuracy to the point where a five-day forecast today is as reliable as a two-day forecast was 20 years ago.