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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

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...