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Big Data Trends to Watch in 2017

Ovum predicts machine learning will be the big disruptor
Tony Baer

Big data continues to be the fastest-growing segment of the information management software market. New findings released by Ovum estimate that 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.

Ovum’s 2017 Trends to Watch: Big Data report highlights that while the breakout use case for big data in 2017 will be streaming, machine learning will be the factor that disrupts the landscape the most.

Key 2017 trends:

■ Machine learning will be the biggest disruptor for big data analytics in 2017.

■ Making data science a team sport will become a top priority.

■ IoT use cases will push real-time streaming analytics to the front burner.

■ The cloud will sharpen Hadoop-Spark “co-opetition.”

■ Security and data preparation will drive data lake governance.

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 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.

Fastest-Growing Use Case: Real-Time Streaming

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

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.

Cloud Dominates Big Data

Within the next 24 months, Ovum expects that the cloud will pass the halfway mark to dominate new big data deployments.

Big data has emerged from its infancy to transition from buzzword to urgency for enterprises across all major sectors. 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.

Tony Baer is Principal Analyst for Information Management at Ovum.

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Big Data Trends to Watch in 2017

Ovum predicts machine learning will be the big disruptor
Tony Baer

Big data continues to be the fastest-growing segment of the information management software market. New findings released by Ovum estimate that 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.

Ovum’s 2017 Trends to Watch: Big Data report highlights that while the breakout use case for big data in 2017 will be streaming, machine learning will be the factor that disrupts the landscape the most.

Key 2017 trends:

■ Machine learning will be the biggest disruptor for big data analytics in 2017.

■ Making data science a team sport will become a top priority.

■ IoT use cases will push real-time streaming analytics to the front burner.

■ The cloud will sharpen Hadoop-Spark “co-opetition.”

■ Security and data preparation will drive data lake governance.

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 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.

Fastest-Growing Use Case: Real-Time Streaming

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

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.

Cloud Dominates Big Data

Within the next 24 months, Ovum expects that the cloud will pass the halfway mark to dominate new big data deployments.

Big data has emerged from its infancy to transition from buzzword to urgency for enterprises across all major sectors. 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.

Tony Baer is Principal Analyst for Information Management at Ovum.

Hot Topics

The Latest

Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...

The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...

In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...

In MEAN TIME TO INSIGHT Episode 19, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA explains the cause of the AWS outage in October ... 

The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before. But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck ...

On September 16, the world celebrated the 10th annual IT Pro Day, giving companies a chance to laud the professionals who serve as the backbone to almost every successful business across the globe. Despite the growing importance of their roles, many IT pros still work in the background and often go underappreciated ...

Artificial Intelligence (AI) is reshaping observability, and observability is becoming essential for AI. This is a two-way relationship that is increasingly relevant as enterprises scale generative AI ... This dual role makes AI and observability inseparable. In this blog, I cover more details of each side ...