APIs and CD: Rekindling Interest in APM - Part 2

Julie Craig

Start with APIs and CD: Rekindling Interest in APM - Part 1

The findings outlined in Part 1 of this blog point to a need for "smart" Application Performance Management (APM) solutions supporting automation of change monitoring, performance and availability management, and production troubleshooting functions. With such capabilities in place, Dev and Ops resources could be freed up to deliver the new software products that have become the lifeblood of the agile business.

Taken together, these findings make a strong case for APM investments. They also make a strong case for refocusing on APM as a research topic during 2016. I am currently in the process of developing a survey supporting a new research report to be delivered in mid-to-late June. Thanks to Apica, BMC and Riverbed for sponsoring the upcoming 2016 EMA APM research study entitled APM in the Digital Economy: What's Hot, What's Not and What's On the Horizon?

The study will investigate the types of capabilities IT organizations are seeking in a new generation of APM solutions as mobile applications, API-driven applications, containers, and streaming technologies hit mainstream. One important factor for IT organizations to keep in mind as they consider APM investments is the distinction between the monitoring and management functions, i.e. "Application Performance Monitoring" versus "Application Performance Management." The two differ primarily in two functional areas: depth of coverage and the sophistication of the analytics and other features supporting autonomy in problem detection and resolution.

The role of application monitoring is to quantify, correlate, store, and report granular metrics underlying end-to-end application/transaction execution. The management function goes a step further and begins to take over the expertise-driven analytical tasks traditionally done by human IT practitioners. Advanced APM solutions — in which the "M" stands for "Management" — add the analytics and deep-dive transaction visibility necessary to actually identify, at high levels of certainty, the actual root cause of performance or availability issues.

Features supporting the management function in today's leading-edge APM solutions include the ability to "learn" from environmental factors and use this learning to detect departures from normal behavior. Other features include predictive analytics supporting notification of impending issues/failures, along with autonomic capabilities supporting automated resolution (if a company chooses to take advantage of this functionality). However, perhaps the most important feature associated with these solutions is automation of the process of formulating insights from data and using those insights to draw accurate conclusions relating to "how do we fix it?"

While human experts have historically performed this function, automated products can do so far faster and more efficiently than their human counterparts.

From this perspective, investments in quality APM solutions can eliminate many of the production support tasks that are consuming the bandwidth of Dev and Ops teams. This, in turn, frees up these teams to deliver new software products and services at the fast pace necessary to achieve software-related business objectives.

Stay tuned for the research report, which is scheduled to be published in mid-June, and for the related webinar, currently scheduled for July 12.

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APIs and CD: Rekindling Interest in APM - Part 2

Julie Craig

Start with APIs and CD: Rekindling Interest in APM - Part 1

The findings outlined in Part 1 of this blog point to a need for "smart" Application Performance Management (APM) solutions supporting automation of change monitoring, performance and availability management, and production troubleshooting functions. With such capabilities in place, Dev and Ops resources could be freed up to deliver the new software products that have become the lifeblood of the agile business.

Taken together, these findings make a strong case for APM investments. They also make a strong case for refocusing on APM as a research topic during 2016. I am currently in the process of developing a survey supporting a new research report to be delivered in mid-to-late June. Thanks to Apica, BMC and Riverbed for sponsoring the upcoming 2016 EMA APM research study entitled APM in the Digital Economy: What's Hot, What's Not and What's On the Horizon?

The study will investigate the types of capabilities IT organizations are seeking in a new generation of APM solutions as mobile applications, API-driven applications, containers, and streaming technologies hit mainstream. One important factor for IT organizations to keep in mind as they consider APM investments is the distinction between the monitoring and management functions, i.e. "Application Performance Monitoring" versus "Application Performance Management." The two differ primarily in two functional areas: depth of coverage and the sophistication of the analytics and other features supporting autonomy in problem detection and resolution.

The role of application monitoring is to quantify, correlate, store, and report granular metrics underlying end-to-end application/transaction execution. The management function goes a step further and begins to take over the expertise-driven analytical tasks traditionally done by human IT practitioners. Advanced APM solutions — in which the "M" stands for "Management" — add the analytics and deep-dive transaction visibility necessary to actually identify, at high levels of certainty, the actual root cause of performance or availability issues.

Features supporting the management function in today's leading-edge APM solutions include the ability to "learn" from environmental factors and use this learning to detect departures from normal behavior. Other features include predictive analytics supporting notification of impending issues/failures, along with autonomic capabilities supporting automated resolution (if a company chooses to take advantage of this functionality). However, perhaps the most important feature associated with these solutions is automation of the process of formulating insights from data and using those insights to draw accurate conclusions relating to "how do we fix it?"

While human experts have historically performed this function, automated products can do so far faster and more efficiently than their human counterparts.

From this perspective, investments in quality APM solutions can eliminate many of the production support tasks that are consuming the bandwidth of Dev and Ops teams. This, in turn, frees up these teams to deliver new software products and services at the fast pace necessary to achieve software-related business objectives.

Stay tuned for the research report, which is scheduled to be published in mid-June, and for the related webinar, currently scheduled for July 12.

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 ...

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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 ...

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 ...