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Big data deployments will become mainstream in 2016: Oracle
February 12, 2016 Blog big data

This article was originally published by indiatimes.com and can be viewed in full here

With each passing year, as the world transitions to digital era technologies, the consumption of data is increasing exponentially. On Facebook alone there are four million posts every minute and in same time Instagram users post 1.7 million likes. Every minute about 400 hours of new video is updated by users around the world. Now that translates to mind-boggling amounts of data generated every second. All this data hold mountains full of intelligence and companies are keen to know and analyse this big data heap.

“2016 will be the year when big data becomes more mainstream and is adopted across various sectors to drive innovation and capture digitization opportunities,” said Neil Mendelson, vice president, big data product management, Oracle.

According to a release from technology major Oracle, big data deployments will become mainstream in 2016. Below is what Oracle predicts for 2016:

Increased demand for data scientists: Professional data scientists will see increasing demand for their skills from established companies. Banks, insurers, and credit-rating firms will turn to algorithms to reduce price risk and guard against fraud more effectively. 2016 will witness an increase in the proliferation of experiments default risk, policy underwriting, and fraud detection as firms try to identify hotspots for algorithmic advantage faster than the competition.

Emergence of new management tools: New management tools will uncouple and enclose the big data foundation technologies from higher level data processing needs. We will also see the emergence of dataflow programming which provides simpler reusability of functional operators, and gives pluggable support for statistical and machine learning functions.

Emergence of big data cloud services with the help of IoT (Internet of Things): IoT cloud services will help manufacturers create new products that safely take action on the analyzed data without human intervention

Big data gives AI something to think about. 2016 will be the year where Artificial Intelligence (AI) technologies such as Machine Learning (ML), Natural Language Processing (NLP) and Property Graphs (PG) are applied to ordinary data processing challenges. The new shift will include widespread applications of these technologies in IT tools that support applications, real-time analytics and data science.

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