A growing number of organisations in sectors such as banking and insurance are set toRead More
Security to be key big data use for 2016
A growing number of organisations in sectors such as banking and insurance are set to turn to big data analytics in 2016 in order to keep their critical information safe from hackers and other unauthorised users.
This is according to a new forecast for the year ahead from Oracle. It noted that 2016 will see big data become more integral to the day-to-day workings of many businesses.
Neil Mendelson, vice-president of big data and product management at Oracle, said: "2016 will be the year when big data becomes more mainstream and is adopted across various sectors to drive innovation and capture digitisation opportunities."
However it will be the technology's ability to identify unusual and potentially fraudulent activity that will be of particular interest to the financial services sector.
The company stated: "2016 will witness an increase in the proliferation of experiments [around] default risk, policy underwriting, and fraud detection as firms try to identify hotspots for algorithmic advantage faster than the competition."
Another key driver for big data security solutions will be increasing public awareness of the numerous ways their personally identifiable information can be collected, shared, stored and stolen. This will in turn lead to more calls for greater regulation to ensure this data is protected.
"The continuous threat of ever more sophisticated hackers will prompt companies to both tighten security, as well as audit access and use of data," Oracle continued.
Among its other predictions, the company forecast increased demand for data scientists from established enterprises, while the emergence of new management tools will allow more businesses to implement technologies such as machine learning, natural language processing and property graphs.
Simpler data discovery tools will also let business analysts identify the most useful datasets within enterprise Hadoop clusters, reshape them into new combinations and analyse them with exploratory machine learning techniques. This will improve both self-service access to big data and provide richer hypotheses and experiments that drive the next level of innovation.