The Many Advantages of Application Performance Data

Shamus McGillicuddy

Enterprise Management Associates (EMA) has discovered that application performance data is extremely valuable when enterprises apply big data analytics to IT monitoring data, and it might be helping in the area where you least expect – Infrastructure capacity planning.

Last year EMA research found that 39% of enterprises were exporting data from network monitoring and management systems into Big Data projects. Naturally, we were curious to know why they were doing this and whether they were exporting any other kinds of monitoring data. So this year, EMA launched a broad study on the subject, Big Data Impacts on IT Infrastructure and Management. We set out to discover exactly what kinds of IT monitoring data enterprises are exporting into big data environments and how they are using it.

The research revealed that application performance data is more relevant and valuable to advanced analytics of monitoring data than any other. Among enterprises that are exporting IT monitoring data into big data environments, 59% of them are exporting application performance data. In contrast, only 41% of these enterprises were exporting log entries and 30% were exporting raw network packets.

We wanted to know about value as well as frequency, so we also asked these enterprises to identify the three most important types of IT data they export into big data environments. Application performance data again came out on top at 44%.

Our research did not ask enterprises why application performance data is so valuable in these projects, but there are numerous reasons why it could be the case. Enterprises may gather Application Performance Management (APM) data more frequently than other data types. For example, EMA has found that only about a third of enterprises use Network Performance Management (NPM) products for continuous monitoring. Instead, troubleshooting is a more popular use case. APM technologies, on the other hand, are essential to understanding end user experience in an application context, which makes continuous monitoring more likely.

Further research will be needed to explore all the variables that go into this outcome. For instance, are APM vendors more supportive than other management tool vendors to exporting their metadata into third party environments like Splunk, Hadoop, Cassandra or MongoDB? It will be important to understand how expensive it is to perform these exports, since some vendors require specialized licensing. We also need to understand how easy it is to export this data. Not all APIs are created equal. Some management vendors offer open, well-documented APIs. Others do not. All of these conditions could influence how popular a data type is.

Use cases also determine the value of data. In this research, EMA asked research participants to identify which types are important to big data analytics for IT planning and engineering, technical performance monitoring, and troubleshooting. It will surprise no one to learn that 63% of the enterprises said application performance data was valuable to performance monitoring via big data analytics. No other data type garnered a majority here. At 56%, application performance data was also the only type of data valuable to a majority of enterprises that are troubleshooting infrastructure via big data analytics. Application performance data can be a good indicator of the root cause of a problem, so again this is no surprise.

But some people may be surprised to learn that 51% of these enterprises are applying application performance data to IT planning and engineering via big data analytics. In this case, it was tied with transaction records for most popular data type. We asked these enterprises to identify the IT planning, monitoring and troubleshooting tasks they perform via big data analytics. Fifty-seven percent of them use these advanced analytics tools for network capacity planning, 66% use it for server capacity planning and 70% use it for storage capacity planning. Clearly the numbers show that application performance data is essential to all three of these tasks.

Other data that one would expect to be valuable to capacity planning lag behind application performance data. For instance, flow records (34%) interpreted packet flow (36%) clearly have value to network capacity planning. But neither is as valued as application performance data.

We’ve established that application performance data is popular and valuable to a broad range of use cases for big data analysis of infrastructure monitoring data. Other sources of data have their uses, too, but clearly an APM platform is a core tool for any organization interested in adopting advanced IT analytics. If an enterprise does choose to move in that direction, they will have to make sure their vendor supports such an initiative. Do they offer open APIs or custom integration with NoSQL databases? Do they charge for such integration? These will be just some of the questions you should ask as you consider advanced analytics.

Shamus McGillicuddy is Senior Analyst, Network Management at Enterprise Management Associates (EMA).

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The Many Advantages of Application Performance Data

Shamus McGillicuddy

Enterprise Management Associates (EMA) has discovered that application performance data is extremely valuable when enterprises apply big data analytics to IT monitoring data, and it might be helping in the area where you least expect – Infrastructure capacity planning.

Last year EMA research found that 39% of enterprises were exporting data from network monitoring and management systems into Big Data projects. Naturally, we were curious to know why they were doing this and whether they were exporting any other kinds of monitoring data. So this year, EMA launched a broad study on the subject, Big Data Impacts on IT Infrastructure and Management. We set out to discover exactly what kinds of IT monitoring data enterprises are exporting into big data environments and how they are using it.

The research revealed that application performance data is more relevant and valuable to advanced analytics of monitoring data than any other. Among enterprises that are exporting IT monitoring data into big data environments, 59% of them are exporting application performance data. In contrast, only 41% of these enterprises were exporting log entries and 30% were exporting raw network packets.

We wanted to know about value as well as frequency, so we also asked these enterprises to identify the three most important types of IT data they export into big data environments. Application performance data again came out on top at 44%.

Our research did not ask enterprises why application performance data is so valuable in these projects, but there are numerous reasons why it could be the case. Enterprises may gather Application Performance Management (APM) data more frequently than other data types. For example, EMA has found that only about a third of enterprises use Network Performance Management (NPM) products for continuous monitoring. Instead, troubleshooting is a more popular use case. APM technologies, on the other hand, are essential to understanding end user experience in an application context, which makes continuous monitoring more likely.

Further research will be needed to explore all the variables that go into this outcome. For instance, are APM vendors more supportive than other management tool vendors to exporting their metadata into third party environments like Splunk, Hadoop, Cassandra or MongoDB? It will be important to understand how expensive it is to perform these exports, since some vendors require specialized licensing. We also need to understand how easy it is to export this data. Not all APIs are created equal. Some management vendors offer open, well-documented APIs. Others do not. All of these conditions could influence how popular a data type is.

Use cases also determine the value of data. In this research, EMA asked research participants to identify which types are important to big data analytics for IT planning and engineering, technical performance monitoring, and troubleshooting. It will surprise no one to learn that 63% of the enterprises said application performance data was valuable to performance monitoring via big data analytics. No other data type garnered a majority here. At 56%, application performance data was also the only type of data valuable to a majority of enterprises that are troubleshooting infrastructure via big data analytics. Application performance data can be a good indicator of the root cause of a problem, so again this is no surprise.

But some people may be surprised to learn that 51% of these enterprises are applying application performance data to IT planning and engineering via big data analytics. In this case, it was tied with transaction records for most popular data type. We asked these enterprises to identify the IT planning, monitoring and troubleshooting tasks they perform via big data analytics. Fifty-seven percent of them use these advanced analytics tools for network capacity planning, 66% use it for server capacity planning and 70% use it for storage capacity planning. Clearly the numbers show that application performance data is essential to all three of these tasks.

Other data that one would expect to be valuable to capacity planning lag behind application performance data. For instance, flow records (34%) interpreted packet flow (36%) clearly have value to network capacity planning. But neither is as valued as application performance data.

We’ve established that application performance data is popular and valuable to a broad range of use cases for big data analysis of infrastructure monitoring data. Other sources of data have their uses, too, but clearly an APM platform is a core tool for any organization interested in adopting advanced IT analytics. If an enterprise does choose to move in that direction, they will have to make sure their vendor supports such an initiative. Do they offer open APIs or custom integration with NoSQL databases? Do they charge for such integration? These will be just some of the questions you should ask as you consider advanced analytics.

Shamus McGillicuddy is Senior Analyst, Network Management at Enterprise Management Associates (EMA).

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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 ...

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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 ...

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