Machine learning will disrupt big data analytics landscape in 2017: Ovum

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Streaming analytics is primed to be the big breakout use case for big data in 2017, but the big disruptor in the big data landscape will be machine learning, according to Ovum.

Ovum’s new 2017 Trends to Watch: Big Data report says that big data remains the fastest-growing segment of the information management software market, and is forecast to grow from $1.7 billion in 2016 to $9.4 billion by 2020, comprising 10% of the overall market for information management tooling.

Among the key trends for big data analytics Ovum expects to see next year:

  • IoT use cases will push real-time streaming analytics to the front burner.
  • Making data science a team sport will become a top priority.
  • The cloud will sharpen Hadoop-Spark “co-opetition.”
  • Security and data preparation will drive data lake governance.

The most disruptive trend, however – and the one driving many of the above – is going to be machine learning, Ovum says.

Under the covers, machine learning is already becoming ubiquitous as it is embedded in many services that consumers take for granted. Increasingly, machine learning is becoming embedded in enterprise software and tooling for integrating and preparing data. Machine learning is placing 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.

Meanwhile, as machine learning continues to grab the headlines, real-time streaming will become the fastest-growing use case.

Ovum says 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.

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, the cloud will pass the halfway mark to dominate new big data deployments, said Tony Baer, principal analyst for information management at 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.”

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