2018 Predictions: Rapid Transformation, Smart Data and Mission-Critical Connectivity

Michael Segal
Learn more about NetScout

With more than one-third of IT Professionals citing "moving faster" as their top goal for 2018, and an overwhelming 99 percent of IT and business decision makers noticing an increasing pace of change in today's connected world, it's clear that speed has become intrinsically linked to business success.

For companies looking to compete in the digital economy, this pace of transformation is being driven by their customers and requires speedy software releases, agility through cloud services, and automation.

Speed becomes a primary business objective

As we look ahead to 2018, we therefore expect businesses to place increased focus on accelerating the development and deployment of applications and services, while maintaining quality and cutting costs: juxtaposing tasks. To achieve this, more and more companies will look to elastically expand their infrastructure by moving compute applications and storage workloads to the cloud and delivering services through hybrid, on-prem and public cloud environments.

In the rush to embrace digital transformation, organizations must ensure they don't lose sight of whether the hybrid cloud is delivering real business value

However, in the rush to embrace digital transformation (DX), organizations must ensure they don't lose sight of whether the hybrid cloud is delivering real business value. To best evaluate its effectiveness, it is imperative that organizations continuously monitor their entire infrastructure to provide a 360 view of business services, infrastructure and their interdependencies, which will enable them to quickly identify current or potential problems.

Assuring networks and applications will be paramount

DX will also power a surge in momentum for the IoT, with the number of connected devices predicted to reach 23.14 billion by 2018. We expect to see the IoT continue to touch all aspects of the digital economy, unlocking enormous benefits in a wide range of sectors, from agriculture to automotive.

With more and more IoT technologies underpinning critical applications, such as disaster monitoring and military situational awareness, and the amount of IoT devices and use cases increasing, businesses will be under increasing pressure to maintain connectivity and communication across a myriad of wireless and wired, physical and virtual, local and wide area networks. In 2018, assured delivery of IoT services will therefore become key determiners for success.

As operators in the US and around the world take steps towards delivering 5G connectivity, IoT applications and services would significantly benefit by utilizing the 5G technology to achieve a truly ubiquitous, reliable, scalable, and cost-efficient Device-to-Device connectivity between nearby mobiles. This will support use cases such as vehicle-to-vehicle communications, public safety, or mobile data offloading, as well as sensors deployed throughout a smart city. However, for 5G to be truly heralded a success, organizations and governments will need to know how to assure availability, reliability, responsiveness and security of applications and services delivered across their networks.

Environmental data comes to the forefront

With the amount of data in the world predicted to increase at least 50 fold between 2010 and 2020, we'll also start to see growing emphasis being placed on how that data is stored. Collecting large volumes of raw log data from multiple applications and infrastructure components and sending it to a central location for storage and processing, for example, increases the size and cost of storage and communications over the Wide Area Network (WAN).

Furthermore, the surging demand for data has environmental implications; by 2020, 12 percent of the world's energy consumption will be taken by our digital ecosystem, and this is expected to grow annually at approximately 7 percent until 2030. As these high costs and inefficiencies could hugely undermine the advantages big data brings, we expect to see more and more businesses take a smarter approach to data collection, organization and processing, saving not only on storage costs, but also on communications, electricity and raw material, beginning the journey towards a greener and brighter data-driven future.

Data gets smarter

By utilizing smart data, which distills the essence of the traffic flows that traverse the service delivery infrastructure in a distributed fashion, close to the source, and compresses it into metadata, businesses can ensure they only store the information that holds real value. This information can then be used to gain meaningful and actionable insights, helping organizations to gain a competitive edge while driving efficiencies by enabling data to be rapidly compressed, and substantially reducing the volume of data stored by an order of magnitude or more.

Smart data is already used to power a range of service, operations and business analytics across different industries including automotive, manufacturing and healthcare, and we expect its usage to increase dramatically in 2018. With the proliferation of IoT sensors, mobile devices and digital services creating an abundance of data used by the various applications and services that rely on hybrid cloud infrastructure, having the ability to convert smart data into meaningful and actionable IT and business insights, will help corporations to thrive in 2018 and beyond.

Related Links

Hot Topics

Related Vendors

The Latest

Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

2018 Predictions: Rapid Transformation, Smart Data and Mission-Critical Connectivity

Michael Segal
Learn more about NetScout

With more than one-third of IT Professionals citing "moving faster" as their top goal for 2018, and an overwhelming 99 percent of IT and business decision makers noticing an increasing pace of change in today's connected world, it's clear that speed has become intrinsically linked to business success.

For companies looking to compete in the digital economy, this pace of transformation is being driven by their customers and requires speedy software releases, agility through cloud services, and automation.

Speed becomes a primary business objective

As we look ahead to 2018, we therefore expect businesses to place increased focus on accelerating the development and deployment of applications and services, while maintaining quality and cutting costs: juxtaposing tasks. To achieve this, more and more companies will look to elastically expand their infrastructure by moving compute applications and storage workloads to the cloud and delivering services through hybrid, on-prem and public cloud environments.

In the rush to embrace digital transformation, organizations must ensure they don't lose sight of whether the hybrid cloud is delivering real business value

However, in the rush to embrace digital transformation (DX), organizations must ensure they don't lose sight of whether the hybrid cloud is delivering real business value. To best evaluate its effectiveness, it is imperative that organizations continuously monitor their entire infrastructure to provide a 360 view of business services, infrastructure and their interdependencies, which will enable them to quickly identify current or potential problems.

Assuring networks and applications will be paramount

DX will also power a surge in momentum for the IoT, with the number of connected devices predicted to reach 23.14 billion by 2018. We expect to see the IoT continue to touch all aspects of the digital economy, unlocking enormous benefits in a wide range of sectors, from agriculture to automotive.

With more and more IoT technologies underpinning critical applications, such as disaster monitoring and military situational awareness, and the amount of IoT devices and use cases increasing, businesses will be under increasing pressure to maintain connectivity and communication across a myriad of wireless and wired, physical and virtual, local and wide area networks. In 2018, assured delivery of IoT services will therefore become key determiners for success.

As operators in the US and around the world take steps towards delivering 5G connectivity, IoT applications and services would significantly benefit by utilizing the 5G technology to achieve a truly ubiquitous, reliable, scalable, and cost-efficient Device-to-Device connectivity between nearby mobiles. This will support use cases such as vehicle-to-vehicle communications, public safety, or mobile data offloading, as well as sensors deployed throughout a smart city. However, for 5G to be truly heralded a success, organizations and governments will need to know how to assure availability, reliability, responsiveness and security of applications and services delivered across their networks.

Environmental data comes to the forefront

With the amount of data in the world predicted to increase at least 50 fold between 2010 and 2020, we'll also start to see growing emphasis being placed on how that data is stored. Collecting large volumes of raw log data from multiple applications and infrastructure components and sending it to a central location for storage and processing, for example, increases the size and cost of storage and communications over the Wide Area Network (WAN).

Furthermore, the surging demand for data has environmental implications; by 2020, 12 percent of the world's energy consumption will be taken by our digital ecosystem, and this is expected to grow annually at approximately 7 percent until 2030. As these high costs and inefficiencies could hugely undermine the advantages big data brings, we expect to see more and more businesses take a smarter approach to data collection, organization and processing, saving not only on storage costs, but also on communications, electricity and raw material, beginning the journey towards a greener and brighter data-driven future.

Data gets smarter

By utilizing smart data, which distills the essence of the traffic flows that traverse the service delivery infrastructure in a distributed fashion, close to the source, and compresses it into metadata, businesses can ensure they only store the information that holds real value. This information can then be used to gain meaningful and actionable insights, helping organizations to gain a competitive edge while driving efficiencies by enabling data to be rapidly compressed, and substantially reducing the volume of data stored by an order of magnitude or more.

Smart data is already used to power a range of service, operations and business analytics across different industries including automotive, manufacturing and healthcare, and we expect its usage to increase dramatically in 2018. With the proliferation of IoT sensors, mobile devices and digital services creating an abundance of data used by the various applications and services that rely on hybrid cloud infrastructure, having the ability to convert smart data into meaningful and actionable IT and business insights, will help corporations to thrive in 2018 and beyond.

Related Links

Hot Topics

Related Vendors

The Latest

Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...