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What You Should Be Monitoring to Ensure Digital Performance - Part 4

APMdigest asked experts from across the IT industry for their opinions on what IT departments should be monitoring to ensure digital performance. Part 4 covers the infrastructure, including the cloud and the network.

Start with What You Should Be Monitoring to Ensure Digital Performance - Part 1

Start with What You Should Be Monitoring to Ensure Digital Performance - Part 2

Start with What You Should Be Monitoring to Ensure Digital Performance - Part 3

DATA

Data performance monitoring is the most important aspect to ensure digital performance. Data transformed into content will dictate the end user experience. Data represented as text, images, video or voxels in extended reality requires continual monitoring to ensure quality of experience. IT departments can determine the amount of investment required to modify the network and application components based upon data performance. Data visualization formats can also be modified to function on the status quo infrastructure until the upgrade investments are in place.
Dos Dosanjh
Director, Technical Marketing, Quali

Monitor slow flow data - collected by standard discovery tools to map changes in network, apps, data, location or users. And fast flow data - collected by log analyzers, webcasters, and real time discovery to overlay changes to dependency map.
Jeanne Morain
Author and Strategist, iSpeak Cloud

BIG DATA TECHNOLOGIES

Organizations need to be able to monitor the big data technology that modern applications are increasingly reliant upon. These apps need fast access to technologies such as Hadoop, Kafka, Spark and Hbase, in order to make business critical decisions across all verticals, including financial services, retail, manufacturing, healthcare and telecom. For example, in finance, fraud detection leverages streaming data from systems like Kafka and Spark Streaming to collect and process information in order to detect any irregular patterns and prevent fraudulent transactions. Streaming apps like these have complex distributed architectures and produce massive volumes of data that is constantly changing. This makes them susceptible to performance issues, jeopardizing the important business process they were supporting. It's critical that enterprises monitor these modern data apps with a strong application performance management (APM) platform that has end-to-end observability and AI-driven automation at the core.
Kunal Agarwal
CEO, Unravel Data

MAINFRAME

Ever since servers of all types began processing transactions between businesses and the web/mobile devices, the need for millisecond performance between the end-user and the mainframe back-end data repository has existed. Along this path, there are numerous moving parts that contribute to a delightful or disastrous user experience. Focus must be given to monitoring mainframe performance as most web and mobile applications end with a purchase, a bank account deposit, or some exchange that ultimately takes place on a back-end mainframe.
Kelly Vogt
Performance Consultant, Compuware

MIDDLEWARE

One component that people often miss: the middleware. Whether it be on-premises ESBs, cloud-based iPaaS, or some combination, if the middleware has an issue, it can adversely impact the customer experience.
Jason Bloomberg
President, Intellyx

INTERACTIONS BETWEEN CLOUDS

As organizations increasingly adopt multi-cloud strategies, there's a growing need to monitor not just the performance (speed, reliability) of individual cloud infrastructures themselves, but also the interactions between these platforms. When deploying a multi-cloud environment, fast, reliable interoperability between multiple cloud regions and providers can be the key to strong performance for entire end-to-end componentized applications.
Mehdi Daoudi
CEO and Founder, Catchpoint

NETWORK

Digital performance is a full-stack affair, so it's essential to monitor the network at the wire level all the way to the applications and real user experience.
Jason Bloomberg
President, Intellyx

The most important metric for IT teams to monitor is the performance of the network.
Douglas Roberts
VP and General Manager, Viavi Enterprise and Cloud Business Unit

Digital performance monitoring requires complete network visibility to expose hidden problems and soon-to-be problems.
Keith Bromley
Senior Manager, Solutions Marketing, Ixia Solutions Group a Keysight Technologies business

Networks can be needy: They often require constant attention to ensure continuous uptime and properly defend against cyberattacks. Traditional symptom-based SNMP monitoring isn't enough to ensure (or enhance) digital performance – IT teams need to leverage proactive network monitoring and contextualized visibility. Waiting for a problem to occur and then trying to track down the source with an outdated map or protocols results in more time troubleshooting and increased MTTR, leaving less time and brain space for making strategic updates or otherwise optimizing digital performance. Instead of always operating in crisis mode, network teams need to employ network automation to continuously monitor for underlying faults, identify problems in context to speed recovery and proactively enforce best practices.
Jason Baudreau
Product Specialist, NetBrain

SD-WAN

Ensuring digital performance today can be a proxy for keeping employees productive. When networks are slow, people are slow. The adoption of SD-WAN is enticing for large enterprises seeking to lower costs or increase flexibility for remote locations, but it's not a silver bullet and what's missing from this discussion is end-to-end performance baselining. SD-WAN has no effect on issues outside of the WAN and the application delivery path is constantly changing so monitoring before, during, and after SD-WAN deployment across the entire connection is essential to finding and fixing issues that degrade user experience no matter where the issue occurs.
Sean Armstrong
VP of Product, AppNeta

Read What You Should Be Monitoring to Ensure Digital Performance - Part 5, the final installment, with some recommendations you may not have thought about.

The Latest

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 ...

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 ...

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 ...

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 ...

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 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.

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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...

What You Should Be Monitoring to Ensure Digital Performance - Part 4

APMdigest asked experts from across the IT industry for their opinions on what IT departments should be monitoring to ensure digital performance. Part 4 covers the infrastructure, including the cloud and the network.

Start with What You Should Be Monitoring to Ensure Digital Performance - Part 1

Start with What You Should Be Monitoring to Ensure Digital Performance - Part 2

Start with What You Should Be Monitoring to Ensure Digital Performance - Part 3

DATA

Data performance monitoring is the most important aspect to ensure digital performance. Data transformed into content will dictate the end user experience. Data represented as text, images, video or voxels in extended reality requires continual monitoring to ensure quality of experience. IT departments can determine the amount of investment required to modify the network and application components based upon data performance. Data visualization formats can also be modified to function on the status quo infrastructure until the upgrade investments are in place.
Dos Dosanjh
Director, Technical Marketing, Quali

Monitor slow flow data - collected by standard discovery tools to map changes in network, apps, data, location or users. And fast flow data - collected by log analyzers, webcasters, and real time discovery to overlay changes to dependency map.
Jeanne Morain
Author and Strategist, iSpeak Cloud

BIG DATA TECHNOLOGIES

Organizations need to be able to monitor the big data technology that modern applications are increasingly reliant upon. These apps need fast access to technologies such as Hadoop, Kafka, Spark and Hbase, in order to make business critical decisions across all verticals, including financial services, retail, manufacturing, healthcare and telecom. For example, in finance, fraud detection leverages streaming data from systems like Kafka and Spark Streaming to collect and process information in order to detect any irregular patterns and prevent fraudulent transactions. Streaming apps like these have complex distributed architectures and produce massive volumes of data that is constantly changing. This makes them susceptible to performance issues, jeopardizing the important business process they were supporting. It's critical that enterprises monitor these modern data apps with a strong application performance management (APM) platform that has end-to-end observability and AI-driven automation at the core.
Kunal Agarwal
CEO, Unravel Data

MAINFRAME

Ever since servers of all types began processing transactions between businesses and the web/mobile devices, the need for millisecond performance between the end-user and the mainframe back-end data repository has existed. Along this path, there are numerous moving parts that contribute to a delightful or disastrous user experience. Focus must be given to monitoring mainframe performance as most web and mobile applications end with a purchase, a bank account deposit, or some exchange that ultimately takes place on a back-end mainframe.
Kelly Vogt
Performance Consultant, Compuware

MIDDLEWARE

One component that people often miss: the middleware. Whether it be on-premises ESBs, cloud-based iPaaS, or some combination, if the middleware has an issue, it can adversely impact the customer experience.
Jason Bloomberg
President, Intellyx

INTERACTIONS BETWEEN CLOUDS

As organizations increasingly adopt multi-cloud strategies, there's a growing need to monitor not just the performance (speed, reliability) of individual cloud infrastructures themselves, but also the interactions between these platforms. When deploying a multi-cloud environment, fast, reliable interoperability between multiple cloud regions and providers can be the key to strong performance for entire end-to-end componentized applications.
Mehdi Daoudi
CEO and Founder, Catchpoint

NETWORK

Digital performance is a full-stack affair, so it's essential to monitor the network at the wire level all the way to the applications and real user experience.
Jason Bloomberg
President, Intellyx

The most important metric for IT teams to monitor is the performance of the network.
Douglas Roberts
VP and General Manager, Viavi Enterprise and Cloud Business Unit

Digital performance monitoring requires complete network visibility to expose hidden problems and soon-to-be problems.
Keith Bromley
Senior Manager, Solutions Marketing, Ixia Solutions Group a Keysight Technologies business

Networks can be needy: They often require constant attention to ensure continuous uptime and properly defend against cyberattacks. Traditional symptom-based SNMP monitoring isn't enough to ensure (or enhance) digital performance – IT teams need to leverage proactive network monitoring and contextualized visibility. Waiting for a problem to occur and then trying to track down the source with an outdated map or protocols results in more time troubleshooting and increased MTTR, leaving less time and brain space for making strategic updates or otherwise optimizing digital performance. Instead of always operating in crisis mode, network teams need to employ network automation to continuously monitor for underlying faults, identify problems in context to speed recovery and proactively enforce best practices.
Jason Baudreau
Product Specialist, NetBrain

SD-WAN

Ensuring digital performance today can be a proxy for keeping employees productive. When networks are slow, people are slow. The adoption of SD-WAN is enticing for large enterprises seeking to lower costs or increase flexibility for remote locations, but it's not a silver bullet and what's missing from this discussion is end-to-end performance baselining. SD-WAN has no effect on issues outside of the WAN and the application delivery path is constantly changing so monitoring before, during, and after SD-WAN deployment across the entire connection is essential to finding and fixing issues that degrade user experience no matter where the issue occurs.
Sean Armstrong
VP of Product, AppNeta

Read What You Should Be Monitoring to Ensure Digital Performance - Part 5, the final installment, with some recommendations you may not have thought about.

The Latest

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 ...

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 ...

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 ...

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 ...

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 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.

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 ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...