Dynatrace announced expanded digital experience management capabilities, including advanced analytics and segmentation of mobile app user sessions, auto-instrumentation for additional mobile platforms and technologies, and enhancements to its explainable AI engine, Davis, including insights from third-party mobile app components.
With these advances, Dynatrace delivers real-time, precise answers about the health, performance and usage of native mobile apps, enabling organizations to quickly deliver new apps and features as well as troubleshoot and resolve issues before user experience and adoption are impacted.
“The Dynatrace Software Intelligence Platform was designed to enable organizations to optimize user experiences across all of their digital channels, including native mobile apps, websites and devices,” said Steve Tack, SVP of Product Management at Dynatrace. “In the last year, we have witnessed an accelerating uptake of Dynatrace for native mobile apps to provide a complete picture of user experiences across all channels and the full stack. With our explainable, AI-based approach, organizations can go beyond partial monitoring and best guesses and discover precise answers that explain exactly what, where, when and why issues impact mobile app user experiences. This enables these teams to accelerate innovation and problem resolution.”
New enhancements to Dynatrace Digital Experience Management capabilities for native mobile applications include:
- Advanced analytics and segmentation – Dynatrace multi-dimensional analytics now include crash reporting workflow and granular segmentation capabilities spanning health, performance and usage metrics across all app components and user actions. These enhancements eliminate blind spots and streamline the error troubleshooting process to identify the precise root cause and impact of problems wherever they occur, from backend applications and underlying cloud infrastructure and networks, to the mobile app and device.
- Enhanced auto-instrumentation – Dynatrace’s fully automated instrumentation and dependency mapping capabilities have been extended to the most popular mobile frameworks and platforms including React Native and tvOS. These augment existing auto-instrumentation for Android, iOS, Xamarin, Cordova and Ionic, and enable organizations to eliminate manual effort and quickly achieve observability as well as AI-powered answers for native mobile apps.
- Enriched AI-powered answers – Dynatrace’s explainable AI engine, Davis, can now process data from third-party mobile app components, in addition to mobile app crash and error rates and user performance metrics. As a result, Davis delivers even more precise answers in real time to accelerate problem resolution and help ensure optimal experiences for every mobile app user.
The Latest
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
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 ...