Top Recommendations to Ensure Performance for the IoT - Part 1

Gartner says: "By 2020, 21 billion of Internet of Things (IoT) devices will be in use worldwide."

"IoT is a growing concept in terms of exposure and implementation," explained John Myers, Managing Research Director for Business Intelligence at Enterprise Management Associates (EMA), in The Rise of IoT. "There are new estimates for the number of linked devices almost every quarter. Some of these estimates go as far as to say that within five years, there will be nearly 40 billion connected devices around the globe."

"Within the next few years billions of smart devices will be communicating and sharing important data on just about everything – healthcare, manufacturing, financial services, food processing, environmental science, lifestyles, and more," added Ron Lifton, Senior Enterprise Solutions Marketing Manager, NetScout.

The IoT is in position to become one of the greatest application performance management challenges faced by IT. The potential number of connected devices, the massive amount of data these devices will generate, and the growing complexity of the infrastructure all compound this challenge.

"As more business and industrial applications are created, more devices are being connected, forcing IT systems to handle greater volumes of data," confirms Ross Garrett, Director Product Marketing at Push Technology in a recent blog on APMdigest. "And more importantly, these connected systems don't have the same tolerance or understanding for tardiness their human counterparts do. Performance – no matter the number of connections, volume of data, distance to travel, or network capability – is critical, and that's the dilemma facing many enterprise architects and systems integrators."

With this challenge in mind, APMdigest asked experts across the industry – including analysts, consultants and vendors – for their recommendations on how to ensure performance for IoT applications. APMdigest will post the in-depth list of expert recommendations over the next four days. Part 1 covers visibility and Application Performance Management.

1. COMPREHENSIVE VISIBILITY

In terms of application performance for the IoT, we recommend that organizations focus their IT efforts on the visibility of all connected devices and the elements with which they are interacting in the IT network. Without full visibility of the entire network, it is impossible to understand interdependencies and impact on performance, and therefore not feasible to meet this new challenge.
Zvika Meiseles
CTO, Correlsense

The sheer surface area of an IoT infrastructure means that the lines blur between security of the infrastructure and performance of IoT applications. The common denominator that ensures maximum security of the IoT infrastructure and performance of the IoT applications is visibility into any data-in-motion between the different elements involved in an IoT deployment.
Ananda Rajagopal
VP, Product Management, Gigamon

2. UNDERSTAND DEPENDENCIES

The key to assuring performance is by understanding all application and service dependencies across the IoT infrastructure so when a problem occurs, it can quickly be identified. IoT performance is dependent on gaining unrestricted operational visibility to identify potential problems from the edge into the cloud and data centers. If the IT organization achieves that, then they can confidently navigate through IoT changes and help reduce business risk.
Ron Lifton
Senior Enterprise Solutions Manager, NetScout

3. MONITOR CONNECTION POINTS BETWEEN TECHNOLOGIES

IoT is about data collection for multiple use cases, but most IoT solutions rely on existing and legacy components as well whether it be IT or OT. Having consistent data collection across technologies is a challenge. Showing the interconnection points between technologies and how each is performing is key in order to isolate performance issues.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

4. APPLICATION PERFORMANCE MANAGEMENT (APM)

Since IoT started gaining traction among enterprises, IT teams have had to deal with an additional layer of complexity on top of the existing management challenges. As enterprises bring more connected devices online, IT has to deal with a large number of devices as well as the massive amounts of data that stream into their big data stores. Ensuring performance of IoT applications can be a cumbersome manual process, one that leaves performance blind spots and gaps in visibility. To address this performance challenge, enterprise IT teams should implement proactive application performance monitoring to gain end-to-end visibility into their distributed applications and the underlying infrastructure. They should understand the dependencies between the different application components and the transactions that flow through it. With the help of performance data collected, IT teams can quickly identify the root cause of application performance bottlenecks, and fix them before users are affected.
Arun Balachandran
Applications Manager Market Analyst, ManageEngine

Traditionally, APM solutions have been very adept at identifying approaching thresholds and bottlenecks in other critical systems. Similarly, ensuring strong performance for IoT depends on the ability to automatically detect and pre-empt performance issues in the systems and applications supporting IoT.
Mehdi Daoudi
CEO and Founder, Catchpoint

5. NEXT-GEN APM

Every new IoT device that connects to the Internet at the frontend, will have an impact on the network and also the hardware at the backend. Therefore, it is essential for Application Performance Management solutions to keep up! This will be extremely critical because its still early in the development and innovation stages of IoT. Who can predict the expansion, connectivity, layers and technology for IoT over in the next 3 - 5 years? Therein lies the challenge for APM!
Hayden James
Linux Systems Analyst, haydenjames.io

6. SEAMLESS INTEGRATION OF KEY COMPONENTS

From looking at comments and conversations on IT Central Station, I've noticed that IT professionals have broken down the key components of IoT into 5 components: the UI/UX Layer, Data Processing or Analytics, Connectivity, Sensors Layer, and the Embedded Systems processor. In order to ensure performance for the IoT, it is critical that each of these components work together harmoniously. Users seem particularly interested in the ability to automate each of these key components, but the critical point is that they seamlessly work together.
Russell Rothstein
Founder and CEO, IT Central Station

Read Top Recommendations to Ensure Performance for the IoT - Part 2, covering data and analytics.

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Empowering the Human Side of the Autonomous IT Age

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Top Recommendations to Ensure Performance for the IoT - Part 1

Gartner says: "By 2020, 21 billion of Internet of Things (IoT) devices will be in use worldwide."

"IoT is a growing concept in terms of exposure and implementation," explained John Myers, Managing Research Director for Business Intelligence at Enterprise Management Associates (EMA), in The Rise of IoT. "There are new estimates for the number of linked devices almost every quarter. Some of these estimates go as far as to say that within five years, there will be nearly 40 billion connected devices around the globe."

"Within the next few years billions of smart devices will be communicating and sharing important data on just about everything – healthcare, manufacturing, financial services, food processing, environmental science, lifestyles, and more," added Ron Lifton, Senior Enterprise Solutions Marketing Manager, NetScout.

The IoT is in position to become one of the greatest application performance management challenges faced by IT. The potential number of connected devices, the massive amount of data these devices will generate, and the growing complexity of the infrastructure all compound this challenge.

"As more business and industrial applications are created, more devices are being connected, forcing IT systems to handle greater volumes of data," confirms Ross Garrett, Director Product Marketing at Push Technology in a recent blog on APMdigest. "And more importantly, these connected systems don't have the same tolerance or understanding for tardiness their human counterparts do. Performance – no matter the number of connections, volume of data, distance to travel, or network capability – is critical, and that's the dilemma facing many enterprise architects and systems integrators."

With this challenge in mind, APMdigest asked experts across the industry – including analysts, consultants and vendors – for their recommendations on how to ensure performance for IoT applications. APMdigest will post the in-depth list of expert recommendations over the next four days. Part 1 covers visibility and Application Performance Management.

1. COMPREHENSIVE VISIBILITY

In terms of application performance for the IoT, we recommend that organizations focus their IT efforts on the visibility of all connected devices and the elements with which they are interacting in the IT network. Without full visibility of the entire network, it is impossible to understand interdependencies and impact on performance, and therefore not feasible to meet this new challenge.
Zvika Meiseles
CTO, Correlsense

The sheer surface area of an IoT infrastructure means that the lines blur between security of the infrastructure and performance of IoT applications. The common denominator that ensures maximum security of the IoT infrastructure and performance of the IoT applications is visibility into any data-in-motion between the different elements involved in an IoT deployment.
Ananda Rajagopal
VP, Product Management, Gigamon

2. UNDERSTAND DEPENDENCIES

The key to assuring performance is by understanding all application and service dependencies across the IoT infrastructure so when a problem occurs, it can quickly be identified. IoT performance is dependent on gaining unrestricted operational visibility to identify potential problems from the edge into the cloud and data centers. If the IT organization achieves that, then they can confidently navigate through IoT changes and help reduce business risk.
Ron Lifton
Senior Enterprise Solutions Manager, NetScout

3. MONITOR CONNECTION POINTS BETWEEN TECHNOLOGIES

IoT is about data collection for multiple use cases, but most IoT solutions rely on existing and legacy components as well whether it be IT or OT. Having consistent data collection across technologies is a challenge. Showing the interconnection points between technologies and how each is performing is key in order to isolate performance issues.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

4. APPLICATION PERFORMANCE MANAGEMENT (APM)

Since IoT started gaining traction among enterprises, IT teams have had to deal with an additional layer of complexity on top of the existing management challenges. As enterprises bring more connected devices online, IT has to deal with a large number of devices as well as the massive amounts of data that stream into their big data stores. Ensuring performance of IoT applications can be a cumbersome manual process, one that leaves performance blind spots and gaps in visibility. To address this performance challenge, enterprise IT teams should implement proactive application performance monitoring to gain end-to-end visibility into their distributed applications and the underlying infrastructure. They should understand the dependencies between the different application components and the transactions that flow through it. With the help of performance data collected, IT teams can quickly identify the root cause of application performance bottlenecks, and fix them before users are affected.
Arun Balachandran
Applications Manager Market Analyst, ManageEngine

Traditionally, APM solutions have been very adept at identifying approaching thresholds and bottlenecks in other critical systems. Similarly, ensuring strong performance for IoT depends on the ability to automatically detect and pre-empt performance issues in the systems and applications supporting IoT.
Mehdi Daoudi
CEO and Founder, Catchpoint

5. NEXT-GEN APM

Every new IoT device that connects to the Internet at the frontend, will have an impact on the network and also the hardware at the backend. Therefore, it is essential for Application Performance Management solutions to keep up! This will be extremely critical because its still early in the development and innovation stages of IoT. Who can predict the expansion, connectivity, layers and technology for IoT over in the next 3 - 5 years? Therein lies the challenge for APM!
Hayden James
Linux Systems Analyst, haydenjames.io

6. SEAMLESS INTEGRATION OF KEY COMPONENTS

From looking at comments and conversations on IT Central Station, I've noticed that IT professionals have broken down the key components of IoT into 5 components: the UI/UX Layer, Data Processing or Analytics, Connectivity, Sensors Layer, and the Embedded Systems processor. In order to ensure performance for the IoT, it is critical that each of these components work together harmoniously. Users seem particularly interested in the ability to automate each of these key components, but the critical point is that they seamlessly work together.
Russell Rothstein
Founder and CEO, IT Central Station

Read Top Recommendations to Ensure Performance for the IoT - Part 2, covering data and analytics.

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Empowering the Human Side of the Autonomous IT Age

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...