Analytics

 

Data analytics are key to the future.

In Cambridge – where we are located – barely a day goes by without the local newspaper carrying an article on data analytics whether applied to housing, health, public transport, education, social care or similar.

Analytics

The difference to what we see these days is not just the sheer volume of applications but, rather their sophistication.

On a local scale, real-time data analytics are being used to display bus arrival times, congestion monitoring and the availability of health resources.

These may seem like trivial applications but, for example TfL (Transport for London) runs an immense real-time analytics system to keep 25M passengers a day on the move in the face of adverse weather, multiple large scale events (such as the Olympics) and breakdowns. Similarly, for EasyJet and Ryanair: the numbers may be smaller, but the problems of keeping planes and passengers in sync across Europe and beyond are enormous!

The message here is that we are already deeply buried in Big Data analytics – mostly without realising it. However, there are huge new application areas which are only just beginning to hit the market. These include health (the huge opportunities for predictive interventions once the data protection issues are resolved); agriculture; the environment; and, of course, the myriad of business applications from ‘recommendations engines’ (such as Amazon) to ‘digital fitting rooms’ for clothes shopping.

 

The technologies for extracting meaning from data are already in place. It is probably safe to say that:

  • The technologies (tools) for analysing well-ordered data (databases, etc.) will become democratised – they will progressively move onto the desktop and away from the specialist departments.
  • Processing and analysing unstructured data (text files, speech, images and social media feeds) is in its infancy but software tools for domain-specific applications already exist and will grow in sophistication.

 

Technology no longer presents barriers for data analytics: the real problem is to find people who have the skills to use and exploit technology which already exists, and which will become both more capable and easier to use.