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Dynatrace Announces Early Access to New Data Center RUM Release With Self-Learning Analytics

Dynatrace (formerly Compuware APM) announced its early access program for Data Center Real User Monitoring (DC RUM) 12.3, delivering powerful self-learning analytics which simplifies the identification and resolution of bottlenecks in production systems, saving organizations hours or days of troubleshooting.

With the addition of automatic discovery and configuration, and dynamic diagnosis, Dynatrace DC RUM cuts down the time it takes to learn why an application is slow, and enables IT professionals to proactively improve on users' satisfaction and SLAs. Existing customers can take advantage of these new innovations immediately by joining the Dynatrace DC RUM v12.3 early access program.

Dynatrace DC RUM is part of the Dynatrace family of products, which delivers a simple to use solution to enable IT professionals to manage the complexity of modern networks and applications. By combining its expertise in both application and network performance analysis, Dynatrace captures the complete set of insights that organizations need to efficiently and effectively manage performance and end user experiences.

"Our continued commitment to delivering innovation in Data Center RUM ensures our customers can manage the network and all applications in their increasingly sophisticated environments," said Steve Tack, VP of Product Management for Dynatrace. "Virtualized infrastructure, increased leverage of SaaS-delivered applications and more mobile and web demand from the edge of the Internet has made managing the entire application delivery chain a necessity. The result is a critical need for app-aware network performance insight that DC RUM uniquely provides to manage technical performance and business results."

Key benefits of the latest version of Dynatrace Data Center RUM include:

- Self-learns application behavior and performance conditions down to individual transactions, assuring performance problems are identified and consumer and employee impact is prevented.

- Dynamically diagnoses performance bottlenecks, and immediately triages to the responsible fault domain; including network quality, third-party performance, client delays and more to assure the responsible team addresses the performance issues.

- Self-Learning FDI Analytics are leveraged across Dynatrace's industry-leading application insight; including SAP, Oracle EBS, HTTP/S, Database, WebSphere MQ, VoIP and more.

- New network performance analysis dashboards and workflows rapidly assess and triage network issues, including packet-level analysis and understanding of deep metrics such as MAC and VLAN data.

- Zero-configuration monitoring automatically discovers and measures all application transactions to simplify performance management and reduce time-to-value.

The Latest

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 ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

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 ...

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 ...

Dynatrace Announces Early Access to New Data Center RUM Release With Self-Learning Analytics

Dynatrace (formerly Compuware APM) announced its early access program for Data Center Real User Monitoring (DC RUM) 12.3, delivering powerful self-learning analytics which simplifies the identification and resolution of bottlenecks in production systems, saving organizations hours or days of troubleshooting.

With the addition of automatic discovery and configuration, and dynamic diagnosis, Dynatrace DC RUM cuts down the time it takes to learn why an application is slow, and enables IT professionals to proactively improve on users' satisfaction and SLAs. Existing customers can take advantage of these new innovations immediately by joining the Dynatrace DC RUM v12.3 early access program.

Dynatrace DC RUM is part of the Dynatrace family of products, which delivers a simple to use solution to enable IT professionals to manage the complexity of modern networks and applications. By combining its expertise in both application and network performance analysis, Dynatrace captures the complete set of insights that organizations need to efficiently and effectively manage performance and end user experiences.

"Our continued commitment to delivering innovation in Data Center RUM ensures our customers can manage the network and all applications in their increasingly sophisticated environments," said Steve Tack, VP of Product Management for Dynatrace. "Virtualized infrastructure, increased leverage of SaaS-delivered applications and more mobile and web demand from the edge of the Internet has made managing the entire application delivery chain a necessity. The result is a critical need for app-aware network performance insight that DC RUM uniquely provides to manage technical performance and business results."

Key benefits of the latest version of Dynatrace Data Center RUM include:

- Self-learns application behavior and performance conditions down to individual transactions, assuring performance problems are identified and consumer and employee impact is prevented.

- Dynamically diagnoses performance bottlenecks, and immediately triages to the responsible fault domain; including network quality, third-party performance, client delays and more to assure the responsible team addresses the performance issues.

- Self-Learning FDI Analytics are leveraged across Dynatrace's industry-leading application insight; including SAP, Oracle EBS, HTTP/S, Database, WebSphere MQ, VoIP and more.

- New network performance analysis dashboards and workflows rapidly assess and triage network issues, including packet-level analysis and understanding of deep metrics such as MAC and VLAN data.

- Zero-configuration monitoring automatically discovers and measures all application transactions to simplify performance management and reduce time-to-value.

The Latest

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 ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

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 ...

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 ...