Top Recommendations to Ensure Performance for the IoT - Part 2

The Internet of Things (IoT) is in position to become one of the greatest application performance management challenges faced by IT. APMdigest asked experts across the industry – including analysts, consultants and vendors – for their recommendations on how to ensure performance for IoT applications. Part 2 covers data and analytics.

Start with Top Recommendations to Ensure Performance for the IoT - Part 1

7. REAL-TIME DATA

The IoT is still too new and the technologies and protocols too diverse to ensure anything, let alone performance – but that doesn't mean you can't get started. The first step: realizing the IoT operates in real-time. Any performance management for the IoT will have to deal with an ongoing deluge of real-time data.
Jason Bloomberg
President, Intellyx

User engagement is fundamentally changing. The broad scale onset of smart sensors, voice interaction, AR/VR is creating an increasingly connected world where customer engagement spans across digital and physical touch points. To ensure optimal business outcomes, it is imperative for businesses to measure near-live time performance of software across devices, connecting microservices, and the clouds supporting the uniform experience.
Prathap Dendi
GM, Emerging Technologies, AppDynamics

We scaled orders of magnitude when we transformed from Client-Server to Internet. This caused dramatic changes how we built, tested, measured, and maintained our systems. With IoT, it's about to happen again. Sensors and tags aren't clients. They're emitters. IoT will demand capture, analytics, and querying millions of data points an hour, in real time. Anything less would be like claiming data that fits on a laptop is a big data problem.
Eric Proegler
Product Manager, SOASTA

Big Data flowing from IoT-connected devices helps organizations be more responsive, adaptive and competitive in a constantly changing business environment. The ability to analyze massive volumes of data as they are collected allows businesses to predict and respond to trends with superior accuracy and precision. Data becomes more actionable and reliable the closer it is analyzed to real-time, and for this reason, organizations cannot afford bottlenecks anywhere in the IoT data collection and analysis process.
Mehdi Daoudi
CEO and Founder, Catchpoint

8. ADVANCED ANALYTICS

People wrongly assume that connectivity is the biggest challenge facing IoT initiatives, when in fact, this is getting easier everyday. The real challenge isn't accessing data, it's gaining knowledge from the data. The more devices we connect, the more noise we create, and — effectively — the more garbage we churn out. Without establishing an intelligent way to make sense of this information, we're simply going to drown in noise.
Assaf Resnick
CEO, BigPanda

Advanced analytics is not only critical to maintaining IoT performance, it also influences business, technology and investment decisions. The best way to help IT teams learn what is happening with edge computing and IoT — such as what devices are interacting with others, what levels of performance are normal, and what are anomalies—is to gather operational data from these log files, and use advanced analytics to move from reactive to proactive problem solving. Log files are a source of the truth and advanced analytics can be used to identify pattern, decrease mean time to identification and predict potential issues before they happen. By understanding critical usage system trends, proactive decisions can be made that positively influence the business and ensure the best customer experiences.
Ramin Sayar, CEO of Sumo Logic
Ramin Sayar
President & CEO, Sumo Logic

9. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

IoT is going to make Big Data into Giant Data. It's the next level of scale, but what will the impact be? Companies will no longer be able to manage the hundreds of millions of connected devices … you simply can't hire enough people to chase down that many alarms. This is where AI and machine learning becomes a "must have" for IT operations tools. AI learns what is normal and abnormal behavior, then will be able to heal itself before an anomaly causes an incident. AI and machine learning will power the growth of IoT and vice versa.
JF Huard, Ph.D.
Founder and CTO, Perspica

10. DATA BATCHES

One of the biggest challenges of building an IoT application is collating the data from various sources. But when an application makes many repetitive requests to different IoT devices to obtain data, it can slow app performance. As such, the best way to ensure performance of IoT applications is to consolidate data into batches. Data can be pushed to the application at low latency in small chunks, as it becomes available. At the same time, deploying an application through a web browser makes it's usable across an extremely wide variety of devices.
Daniel Gallo
Sales Engineer, Sencha

11. ADHERE TO LAWS OF DATA GRAVITY

IoT is a big contributor to Big Data, generating massive real-time data streams. Therefore, in order to build high-performing IoT applications, it's important to adhere to the laws of data gravity. Data gravity refers to the nature of data and its ability to attract additional applications and services. Developers must bring their applications as close to the (IoT) data as possible, versus the other way around. Cloud and open, extensible platforms are absolutely key to doing this in a quick and cost-effective manner.
Roald Kruit
Co-Founder, Mendix

12. LINK DATA TO BUSINESS GOALS

Organizations that link their IoT sensor data to a specific business process or target ensure that their results will gain visibility with the most important IoT champions in an organization – Operational Teams. These OT groups are focused on the delivery and improvement of the operational activities associated with an organization. For energy companies, this takes the form of efficient and predictable distributed energy production. By using sensor information associated with solar collection and daylight hours, or wind speed and direction associated with turbine performance, an IoT initiative provides information directly to the OT team operating the distributed power production and managing the efficient use of non-renewal energy sources. With this context, IoT initiatives link directly to operation productivity and OT team goals for maximum value.
John L Myers
Managing Research Director, Enterprise Management Associates (EMA)

Read Top Recommendations to Ensure Performance for the IoT - Part 3, covering app design and development.

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Top Recommendations to Ensure Performance for the IoT - Part 2

The Internet of Things (IoT) is in position to become one of the greatest application performance management challenges faced by IT. APMdigest asked experts across the industry – including analysts, consultants and vendors – for their recommendations on how to ensure performance for IoT applications. Part 2 covers data and analytics.

Start with Top Recommendations to Ensure Performance for the IoT - Part 1

7. REAL-TIME DATA

The IoT is still too new and the technologies and protocols too diverse to ensure anything, let alone performance – but that doesn't mean you can't get started. The first step: realizing the IoT operates in real-time. Any performance management for the IoT will have to deal with an ongoing deluge of real-time data.
Jason Bloomberg
President, Intellyx

User engagement is fundamentally changing. The broad scale onset of smart sensors, voice interaction, AR/VR is creating an increasingly connected world where customer engagement spans across digital and physical touch points. To ensure optimal business outcomes, it is imperative for businesses to measure near-live time performance of software across devices, connecting microservices, and the clouds supporting the uniform experience.
Prathap Dendi
GM, Emerging Technologies, AppDynamics

We scaled orders of magnitude when we transformed from Client-Server to Internet. This caused dramatic changes how we built, tested, measured, and maintained our systems. With IoT, it's about to happen again. Sensors and tags aren't clients. They're emitters. IoT will demand capture, analytics, and querying millions of data points an hour, in real time. Anything less would be like claiming data that fits on a laptop is a big data problem.
Eric Proegler
Product Manager, SOASTA

Big Data flowing from IoT-connected devices helps organizations be more responsive, adaptive and competitive in a constantly changing business environment. The ability to analyze massive volumes of data as they are collected allows businesses to predict and respond to trends with superior accuracy and precision. Data becomes more actionable and reliable the closer it is analyzed to real-time, and for this reason, organizations cannot afford bottlenecks anywhere in the IoT data collection and analysis process.
Mehdi Daoudi
CEO and Founder, Catchpoint

8. ADVANCED ANALYTICS

People wrongly assume that connectivity is the biggest challenge facing IoT initiatives, when in fact, this is getting easier everyday. The real challenge isn't accessing data, it's gaining knowledge from the data. The more devices we connect, the more noise we create, and — effectively — the more garbage we churn out. Without establishing an intelligent way to make sense of this information, we're simply going to drown in noise.
Assaf Resnick
CEO, BigPanda

Advanced analytics is not only critical to maintaining IoT performance, it also influences business, technology and investment decisions. The best way to help IT teams learn what is happening with edge computing and IoT — such as what devices are interacting with others, what levels of performance are normal, and what are anomalies—is to gather operational data from these log files, and use advanced analytics to move from reactive to proactive problem solving. Log files are a source of the truth and advanced analytics can be used to identify pattern, decrease mean time to identification and predict potential issues before they happen. By understanding critical usage system trends, proactive decisions can be made that positively influence the business and ensure the best customer experiences.
Ramin Sayar, CEO of Sumo Logic
Ramin Sayar
President & CEO, Sumo Logic

9. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

IoT is going to make Big Data into Giant Data. It's the next level of scale, but what will the impact be? Companies will no longer be able to manage the hundreds of millions of connected devices … you simply can't hire enough people to chase down that many alarms. This is where AI and machine learning becomes a "must have" for IT operations tools. AI learns what is normal and abnormal behavior, then will be able to heal itself before an anomaly causes an incident. AI and machine learning will power the growth of IoT and vice versa.
JF Huard, Ph.D.
Founder and CTO, Perspica

10. DATA BATCHES

One of the biggest challenges of building an IoT application is collating the data from various sources. But when an application makes many repetitive requests to different IoT devices to obtain data, it can slow app performance. As such, the best way to ensure performance of IoT applications is to consolidate data into batches. Data can be pushed to the application at low latency in small chunks, as it becomes available. At the same time, deploying an application through a web browser makes it's usable across an extremely wide variety of devices.
Daniel Gallo
Sales Engineer, Sencha

11. ADHERE TO LAWS OF DATA GRAVITY

IoT is a big contributor to Big Data, generating massive real-time data streams. Therefore, in order to build high-performing IoT applications, it's important to adhere to the laws of data gravity. Data gravity refers to the nature of data and its ability to attract additional applications and services. Developers must bring their applications as close to the (IoT) data as possible, versus the other way around. Cloud and open, extensible platforms are absolutely key to doing this in a quick and cost-effective manner.
Roald Kruit
Co-Founder, Mendix

12. LINK DATA TO BUSINESS GOALS

Organizations that link their IoT sensor data to a specific business process or target ensure that their results will gain visibility with the most important IoT champions in an organization – Operational Teams. These OT groups are focused on the delivery and improvement of the operational activities associated with an organization. For energy companies, this takes the form of efficient and predictable distributed energy production. By using sensor information associated with solar collection and daylight hours, or wind speed and direction associated with turbine performance, an IoT initiative provides information directly to the OT team operating the distributed power production and managing the efficient use of non-renewal energy sources. With this context, IoT initiatives link directly to operation productivity and OT team goals for maximum value.
John L Myers
Managing Research Director, Enterprise Management Associates (EMA)

Read Top Recommendations to Ensure Performance for the IoT - Part 3, covering app design and development.

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 ... 

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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 ...