Asian Business Award
Harnessing the Power of Big Data
Ruth Williams, Life Fellow, explains the ground-breaking achievement of Girtonian mathematician Nikhil Shah
The term ‘Big Data’ was coined in the 1990s to describe data sets that are too large to be dealt with by traditional computing methods – for example, the results of particle physics experiments at CERN, human-genome sequences, meteorological data and, of course, the sum of all information held on the internet. Handling Big Data presents many challenges such as storage, access, transmission, analysis and visualisation. For large data sets to be useful, it must be possible to categorise their contents, identify correlations and make predictions, as well as screen out useless information. The study of Big Data has become a very active scientific area in the last twenty years and is likely to become increasingly important.
Inevitably, the tools developed for dealing with Big Data are very complicated technically. Parallel computing is needed, with many tasks performed simultaneously rather than in sequence. When the data is multidimensional – say, information on quantities in three spatial dimensions – it can be represented as data cubes or, to use the mathematical term, tensors. This is the approach taken by a Girton Mathematics graduate, Nikhil Shah, who was named Young Entrepreneur of the Year at the Asian Business Awards in London on 22 March 2019. Starting with material from Vector Calculus, a course in the first year of the Cambridge Mathematical Tripos, Shah developed the S-Cube Cloud with colleagues at Imperial College, London, where he did his PhD.
In technical terms, the S-Cube Cloud is an artificial-intelligence platform for Big Data processing that applies computational optimisation to industrial sensor data. This has major applications in the energy sector, from subsurface exploration to power plants to refineries to carbon storage. Shah and co-workers have applied the S-Cube Cloud to seismic data to predict rock velocities ahead of drilling in offshore locations. This can guide the placement of wells and improve drilling accuracy, and is being used to locate candidate rock formations for carbon storage in the North Sea.
Shah’s pioneering work has a very promising future in bringing enhanced automation and efficiency to data-intensive industrial processes. It should help accelerate a much needed transition to greener energy provision.