UK startup launches big data analytics platform to help businesses predict potential outcomes

- April 23, 2015 2 MIN READ

A UK startup providing enterprises with big data analytics has launched the open beta phase of its platform to help businesses analyse their plans and strategies to predict potential outcomes more accurately.

George Frangou, founder and president of Massive Analytic, said that the company is opening its Oscar AP (artificial precognition) in order to enable business leaders to make decisions with more confidence.

“It enables outcomes of decisions to be understood before they are taken. Oscar AP offers enterprises the opportunity for better outcomes in terms of increased revenue and lower cost, as well as identifying previous undetected revenue opportunities,” Frangou said.

The startup, which has partnered with Amazon Web Services and Microsoft Azure, said that Oscar AP is the first analytics platform to combine artificial intelligence with access to big data in order to examine the performance of a business in a broader scope.

The technology works with real-time and batch data, and can be used and configured without any knowledge of SQL.

Massive Analytic has worked with a variety of companies across different industries, including Fortune 500s, to help them predict business outcomes. Oscar AP has been used to predict patient outcomes in the healthcare industry, for example combining weather data with patient records to look at readmission numbers.

It has been used in retail to help with personalisation, reducing carrying costs, and maximising site conversion, and is of particular use in markets, where Oscar AP can help anticipate market changes.

The much-publicised high startup failure rate means that the advantages of big data analytics should not be ignored. As Professor Jana Matthews of StartupAUS has said data has become a fundamental key to business growth. Companies such as Google and Amazon have been able to grow thanks to the data they produce and the insights gained by analysing the data.