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OpsDataStore Announces Full-Stack Service Level Dashboards

OpsDataStore announced the availability of cross-stack service level dashboards – providing business constituents and application owners of applications monitored by AppDynamics, Dynatrace and ExtraHop a visually appealing and easy to understand view of how their key transactions are performing mapped against the performance of the VMware virtual and physical infrastructure supporting these transactions.

OpsDataStore collects performance (response time and latency), throughput (calls per second and transaction per second), error, capacity, utilization (CPU and memory utilization) and contention (CPU Ready, Memory Swapping) metrics from APM tools like AppDynamics and Dynatrace, Wire Data tools like ExtraHop, and data center virtualization platforms like VMware vSphere. OpsDataStore then calculates the end-to-end and cross-stack relationships between all of these metrics, calculates baselines for each metric and determines anomalies from the baselines. Knowing the metrics across the stack, the anomalies for all of the metrics and the relationships across the stack makes cross-stack service level dashboards possible. This gives business constituents and application owners a 360 degree view of the performance of their business critical transactions overlaid against the performance of the virtual and physical infrastructure supporting and running these transactions.

Each of the below dashboards works in the same way. You configure the top graph for the application level KPI (transaction response time) that you want, and then you select the type of infrastructure that you want for the next 3 graphs. The dashboard then automatically finds the virtual and physical infrastructure where the transaction is running using the relationships in OpsDataStore. All of the dashboards shown below are the exact same dashboard, they are just configured differently.

These dashboards provide the following unique benefits:

- Business constituents and application owners can easily see the performance of business critical transactions and services

- When problems occur, IT can get out of “guilty until proven innocent mode” as the relationships between transaction performance and infrastructure performance are clearly visible

- IT can safely “sweat” the infrastructure while maintaining real time visibility into the performance of the transactions that the business cares about.

OpsDataStore has delivered a capability in the form of easy to use and easy to customize service level dashboards developed in Tableau – the market leading BI and data analytics solution. This gives the business owners of applications the ability to easily see how their business critical transactions and services are performing and allows IT and the business to effectively collaborate around service levels and the costs to support these applications and transactions.

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OpsDataStore Announces Full-Stack Service Level Dashboards

OpsDataStore announced the availability of cross-stack service level dashboards – providing business constituents and application owners of applications monitored by AppDynamics, Dynatrace and ExtraHop a visually appealing and easy to understand view of how their key transactions are performing mapped against the performance of the VMware virtual and physical infrastructure supporting these transactions.

OpsDataStore collects performance (response time and latency), throughput (calls per second and transaction per second), error, capacity, utilization (CPU and memory utilization) and contention (CPU Ready, Memory Swapping) metrics from APM tools like AppDynamics and Dynatrace, Wire Data tools like ExtraHop, and data center virtualization platforms like VMware vSphere. OpsDataStore then calculates the end-to-end and cross-stack relationships between all of these metrics, calculates baselines for each metric and determines anomalies from the baselines. Knowing the metrics across the stack, the anomalies for all of the metrics and the relationships across the stack makes cross-stack service level dashboards possible. This gives business constituents and application owners a 360 degree view of the performance of their business critical transactions overlaid against the performance of the virtual and physical infrastructure supporting and running these transactions.

Each of the below dashboards works in the same way. You configure the top graph for the application level KPI (transaction response time) that you want, and then you select the type of infrastructure that you want for the next 3 graphs. The dashboard then automatically finds the virtual and physical infrastructure where the transaction is running using the relationships in OpsDataStore. All of the dashboards shown below are the exact same dashboard, they are just configured differently.

These dashboards provide the following unique benefits:

- Business constituents and application owners can easily see the performance of business critical transactions and services

- When problems occur, IT can get out of “guilty until proven innocent mode” as the relationships between transaction performance and infrastructure performance are clearly visible

- IT can safely “sweat” the infrastructure while maintaining real time visibility into the performance of the transactions that the business cares about.

OpsDataStore has delivered a capability in the form of easy to use and easy to customize service level dashboards developed in Tableau – the market leading BI and data analytics solution. This gives the business owners of applications the ability to easily see how their business critical transactions and services are performing and allows IT and the business to effectively collaborate around service levels and the costs to support these applications and transactions.

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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 ...