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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

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...