The vast majority of companies in the retail, technology, banking, healthcare and life sciences sectorsRead More
Machine learning a key focus for big data initiatives
A large number of companies will look to introduce machine learning capabilities as part of their efforts to exploit big data in the coming years, a new survey has found.
Research by Evans Data found more than a third of big data developers (36 per cent) now use some elements of machine learning in their projects. While the market for this is still largely fragmented, the financial and manufacturing sectors are showing particular interest in the technology, as are businesses looking to take advantage of Internet of Things opportunities.
Janel Garvin, chief executive of Evans Data, explained that machine learning encompasses a range of techniques that are rapidly being adopted by big data developers, who are in an excellent position to lead the way and show what the technology is capable of.
“We are seeing more and more interest from developers in all forms of cognitive computing, including pattern recognition, natural language recognition, and neural networks and we fully expect that the programs of tomorrow are going to based on these nascent technologies of today.”
The most used analytical model that links in closely with artificial intelligence and machine learning development was found to be decision trees. This was followed by linear regression and logistics regression were as next most cited analytical models.
Logistics, distribution, or operations were the company departments found to be most likely to be using advanced data analytics or big data solutions.
Among the survey’s other findings, it was revealed that two-thirds of big data developers are spending at least some of their time instrumenting processes. Meanwhile, 42 per cent are embracing real-time data analytics, while 38 per cent are building capabilities to analyse unstructured data.
The top improvement to data and analytics that developers would like to see is the improved security of off-site data stores.