Big data, artificial intelligence and predictive analytics

Big data

In an increasingly digitalised world, tremendous amount of information about us and our environment is generated every moment, transmitted over networks and stored in computer servers. From who we ‘liked’ on social media to how well we slept, which our fitness apps dutifully tracked.

According to the technology historian, George Dyson (2013): ⁽²³⁾

‘Big data is what happened when the cost of storing information became less than the cost of throwing it away.’

Big data is commonly characterised by the 3 ‘V’s, where: 

‘Volume relates to massive datasets, velocity relates to real-time data and variety relates to different sources of data.’

(Quoted from: ICO, 2017, pg. 6) ⁽²⁴⁾

A hospital database that contains every single patient’s medical records is an example of ‘big data’. So is the rich genetic information contained within biobanks.  

Artificial intelligence (AI) and predictive analytics

Advances in AI technology means that machines (computers) are now increasingly capable of performing human tasks, sometimes beyond what is humanly achievable.

For example, a computer may be able to predict (or rule out) breast cancer based on a single mammogram image of a patient.

This can be achieved after the computer has been ‘trained’ to look through a large repository of mammogram images from confirmed breast cancer and non-breast cancer patients. The computer, directed by mathematical formulas known as ‘algorithms’, learns by identifying specific patterns (correlations) within and between the images (the data). 

The larger the quantity and variety of data that you ‘feed’ to a computer, the more patterns the computer can find and learn, and the more precise its predictions. This is when big data becomes particularly valuable.