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Machine learning to be the biggest disruptor for big data analytics in 2017: Ovum

Machine learning biggest disruptor big data analytics in 2017

While real-time streaming will become the fastest-growing use case for big data in 2017, machine learning is expected to be the biggest disruptor for big data analytics in 2017 according to advisory firm Ovum’s 2017 Trends to Watch: Big Data.

IoT use cases is predicted to push real-time streaming analytics to the front burner.

“A perfect storm has transformed real-time streaming from a niche technology to one with broad, cross-industry appeal. Open source technology has lowered barriers to entry for both technology providers and customers; scalable commodity infrastructure has made the processing of large torrents of real-time data in motion economically and technically feasible,” informs Tony Baer, Principal Analyst for Information Management.

Apart from embedded in many services that consumers take for granted, increasingly, machine learning is also becoming embedded in enterprise software and tooling for integrating and preparing data. Machine learning is placing a stress on enterprises to make data science a team sport; a big area for growth in 2017 will be solutions that spur collaboration, so the models and hypotheses that data scientists develop do not get bottled up on their desktops adds the report.

The explosion in bandwidth and smart-sensor technology has opened up use cases ranging from location-based marketing to health and safety, intrusion detection, and predictive maintenance, appealing to a broad cross section of industries.

Underscoring and enabling the growth of big data is the growing predominance of cloud computing as the default path to deployment.

Within the next 24 months, Ovum expects that the cloud will pass the halfway mark to dominate new big data deployments. “The cloud will sharpen Hadoop-Spark “co-opetition,” says Ovum.

“Big data has emerged from its infancy to transition from buzzword to urgency for enterprises across all major sectors,” said Baer. “The growing pains are being abetted by machine learning, which will lower barriers to adoption of big data-enabled analytics and solutions, and the growing dominance of the cloud, which will ease deployment hurdles.”

According to Ovum the big data market will grow from $1.7bn in 2016 to $9.4bn by 2020, comprising 10% of the overall market for information management tooling.

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