APM and ITOA: Clearing Up the Confusion

Guy Warren
Learn more about ITRS Group

I was reading a discussion on a social media site about Application Performance Management, and realized that there is a lot of confusion about what is Application Performance Monitoring, Application Performance Management (APM) and IT Operational Analytics (ITOA).

Just looking at the words used, you would believe that Application Performance Monitoring is focused on watching data and monitoring it for a particular condition or state. Application Performance Management would lead you to believe that this is a wider field which includes a range of techniques to certainly monitor the application, but also to manage other aspects of the IT estate. The degree to which complex analytics are used is unclear, but potentially IT Operational Analytics could be seen as a subset of Application Performance Management, although the focus on application might make it more limited in its scope than ITOA.

To help clarify this rather muddy set of terms, we use two models which we find are much clearer and logical, and have less ambiguity than the APM and ITOA definitions.

The Monitoring Maturity Model

The first model we call the Monitoring Maturity Model, because it is a layered model where generally the higher levels are based on data collected from the lower levels. The model is:

1. Infrastructure Monitoring: Collection data on the servers, operating systems, network and storage and setting rule based alerts to catch potential problems.

2. Basic Application Monitoring: From interrogating the Operating System, capture and alert on data about the processes running on the servers. This would include CPU & memory utilization, disk I/O, network I/O etc.

3. Advanced Application Monitoring: Installing a tailored agent on the server which is capturing data specific to the application it is monitoring. This can be "inside the app" data or "outside the app" which is useful for Off the Shelf software products and middleware.

4. Flow Monitoring: This is capturing data about the information passing between applications and monitoring/reporting on data flows. This would include volumes/second, volumes per counterparty, latency etc.

5. Business and IT Analysis: This is the analysis of both business data and "machine" data from levels 1 and 2 to understand the business activity and the behavior of the IT estate.

Monitoring vs Analytics

The second model is separating monitoring from analytics. There is no hard definition which separates them so we break the types of analysis into three types:

1. Detect: This is a rule based detection of an alert condition. This is generally what people mean when they talk about Monitoring.

2. Analyze: This is the collection of lots of data, even data which did not trigger a rule in Detect, and analyzing it to discover more insight. This may be as simple as trends, or as complex as Machine Learning and time series pattern based Anomaly Detection. This would also include techniques like Bayesian Network Causal Analysis.

3. Predict: This uses current and historic data to try and predict future or “what if” scenarios. Again, this can be as simple as extrapolation, or as complex as comparison of current state to empirically derived behavioral data, the likes of which you might have gathered in a performance lab when stress testing an application.

Whichever way you model your IT estate and the behavior of your applications, it is necessary to have a clear language so that people are talking about the same thing.

Guy Warren is CEO of ITRS Group.

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APM and ITOA: Clearing Up the Confusion

Guy Warren
Learn more about ITRS Group

I was reading a discussion on a social media site about Application Performance Management, and realized that there is a lot of confusion about what is Application Performance Monitoring, Application Performance Management (APM) and IT Operational Analytics (ITOA).

Just looking at the words used, you would believe that Application Performance Monitoring is focused on watching data and monitoring it for a particular condition or state. Application Performance Management would lead you to believe that this is a wider field which includes a range of techniques to certainly monitor the application, but also to manage other aspects of the IT estate. The degree to which complex analytics are used is unclear, but potentially IT Operational Analytics could be seen as a subset of Application Performance Management, although the focus on application might make it more limited in its scope than ITOA.

To help clarify this rather muddy set of terms, we use two models which we find are much clearer and logical, and have less ambiguity than the APM and ITOA definitions.

The Monitoring Maturity Model

The first model we call the Monitoring Maturity Model, because it is a layered model where generally the higher levels are based on data collected from the lower levels. The model is:

1. Infrastructure Monitoring: Collection data on the servers, operating systems, network and storage and setting rule based alerts to catch potential problems.

2. Basic Application Monitoring: From interrogating the Operating System, capture and alert on data about the processes running on the servers. This would include CPU & memory utilization, disk I/O, network I/O etc.

3. Advanced Application Monitoring: Installing a tailored agent on the server which is capturing data specific to the application it is monitoring. This can be "inside the app" data or "outside the app" which is useful for Off the Shelf software products and middleware.

4. Flow Monitoring: This is capturing data about the information passing between applications and monitoring/reporting on data flows. This would include volumes/second, volumes per counterparty, latency etc.

5. Business and IT Analysis: This is the analysis of both business data and "machine" data from levels 1 and 2 to understand the business activity and the behavior of the IT estate.

Monitoring vs Analytics

The second model is separating monitoring from analytics. There is no hard definition which separates them so we break the types of analysis into three types:

1. Detect: This is a rule based detection of an alert condition. This is generally what people mean when they talk about Monitoring.

2. Analyze: This is the collection of lots of data, even data which did not trigger a rule in Detect, and analyzing it to discover more insight. This may be as simple as trends, or as complex as Machine Learning and time series pattern based Anomaly Detection. This would also include techniques like Bayesian Network Causal Analysis.

3. Predict: This uses current and historic data to try and predict future or “what if” scenarios. Again, this can be as simple as extrapolation, or as complex as comparison of current state to empirically derived behavioral data, the likes of which you might have gathered in a performance lab when stress testing an application.

Whichever way you model your IT estate and the behavior of your applications, it is necessary to have a clear language so that people are talking about the same thing.

Guy Warren is CEO of ITRS Group.

Related Links

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Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

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The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

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Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

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