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AppDynamics Introduces Application Analytics

AppDynamics announced AppDynamics Application Analytics, a powerful new solution that provides real-time IT operations and business operations intelligence from across distributed application environments.

Application Analytics automatically collects meaningful data in its business context from the entire application stack, no matter how complex or distributed. This data with business context enables IT not only to manage IT operations, but also to provide business units with vital metrics they can use to optimize user engagement, conversion, and ultimately, revenue. It is a true big data analytics platform that enables stakeholders to instantly analyze transactions, manage performance, and determine current and potential impact across the business in real time.

Application Analytics captures raw business data from every line of code in all layers of an application and across every single node, without requiring code changes or additional infrastructure. There is no need for coding, as is required to collect data from log files. And unlike data warehousing, which can only provide historical snapshots of completed transactions, Application Analytics provides an up-to-the-minute, real-time view into transactions that are in flight.

The solution is easy to deploy and massively scalable, capable of handling a trillion metrics daily.

“Application Analytics addresses the biggest data challenges enterprises face today — volume, velocity, and variety,” said Jyoti Bansal, founder and CEO of AppDynamics. “Huge volumes of data are being generated at blinding speed, and it’s coming from every direction — the cloud, different devices, and complex application infrastructures. But it’s just so much noise if it can’t be efficiently harvested and parsed. That’s what Application Analytics does. With the lens of business context, no matter how fast the data comes at you, it quickly makes sense, and gives a basis for smart, insightful decision-making.”

With a solution that is easy to deploy and massively scalable, and capable of handling a trillion metrics daily, Application Analytics enables IT operations to gain intelligence faster than ever before, seeing errors, slowdowns, and outages in real time. Armed with this data, business and IT can collaborate and act quickly to minimize business impact. At the same time, the data it captures can be used to engage those users who have been negatively impacted to mitigate any negative brand impact.

“The challenge to date has been the abundance of solutions for unstructured data analysis, which still require the application to log the correct and relevant data from within the application. This requires a specific type of maturity within an organization, such as developers logging relevant metrics as defined by the business stakeholders or product management professionals,” said Jonah Kowall, Research VP at Gartner. “Alternate approaches have begun to emerge from leading APM providers, which leverage the solution's placement (and viewpoint from) within the application; hence they are able to automatically extract business logic demonstrated in transactional execution. The coupling of this context with detailed information about end-user experience and user interactions with applications themselves, provides a rich set of data that requires no development resources as long as the applications are built on modern technology.”

Application Analytics Use Cases

- Business impact analysis. Quantify the dollars-and-cents impact of stalls and outages, and identify and communicate with impacted customers to recover their business and preserve a positive brand perception.

- Business operations monitoring. Pinpoint where in the process a transaction is stuck, and know how to resolve it.

- Performance analytics. Identify poor-performing transactions and understand the cause in order to prioritize IT investments.

- Customer analytics. Understand user behavior and usage trends to optimize user engagement and conversions.

- SLA management. Real-time reporting on service level agreement performance.

AppDynamics Application Analytics, which completed its beta as “Transaction Analytics,” will be available with the Fall 2014 Release.

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AppDynamics Introduces Application Analytics

AppDynamics announced AppDynamics Application Analytics, a powerful new solution that provides real-time IT operations and business operations intelligence from across distributed application environments.

Application Analytics automatically collects meaningful data in its business context from the entire application stack, no matter how complex or distributed. This data with business context enables IT not only to manage IT operations, but also to provide business units with vital metrics they can use to optimize user engagement, conversion, and ultimately, revenue. It is a true big data analytics platform that enables stakeholders to instantly analyze transactions, manage performance, and determine current and potential impact across the business in real time.

Application Analytics captures raw business data from every line of code in all layers of an application and across every single node, without requiring code changes or additional infrastructure. There is no need for coding, as is required to collect data from log files. And unlike data warehousing, which can only provide historical snapshots of completed transactions, Application Analytics provides an up-to-the-minute, real-time view into transactions that are in flight.

The solution is easy to deploy and massively scalable, capable of handling a trillion metrics daily.

“Application Analytics addresses the biggest data challenges enterprises face today — volume, velocity, and variety,” said Jyoti Bansal, founder and CEO of AppDynamics. “Huge volumes of data are being generated at blinding speed, and it’s coming from every direction — the cloud, different devices, and complex application infrastructures. But it’s just so much noise if it can’t be efficiently harvested and parsed. That’s what Application Analytics does. With the lens of business context, no matter how fast the data comes at you, it quickly makes sense, and gives a basis for smart, insightful decision-making.”

With a solution that is easy to deploy and massively scalable, and capable of handling a trillion metrics daily, Application Analytics enables IT operations to gain intelligence faster than ever before, seeing errors, slowdowns, and outages in real time. Armed with this data, business and IT can collaborate and act quickly to minimize business impact. At the same time, the data it captures can be used to engage those users who have been negatively impacted to mitigate any negative brand impact.

“The challenge to date has been the abundance of solutions for unstructured data analysis, which still require the application to log the correct and relevant data from within the application. This requires a specific type of maturity within an organization, such as developers logging relevant metrics as defined by the business stakeholders or product management professionals,” said Jonah Kowall, Research VP at Gartner. “Alternate approaches have begun to emerge from leading APM providers, which leverage the solution's placement (and viewpoint from) within the application; hence they are able to automatically extract business logic demonstrated in transactional execution. The coupling of this context with detailed information about end-user experience and user interactions with applications themselves, provides a rich set of data that requires no development resources as long as the applications are built on modern technology.”

Application Analytics Use Cases

- Business impact analysis. Quantify the dollars-and-cents impact of stalls and outages, and identify and communicate with impacted customers to recover their business and preserve a positive brand perception.

- Business operations monitoring. Pinpoint where in the process a transaction is stuck, and know how to resolve it.

- Performance analytics. Identify poor-performing transactions and understand the cause in order to prioritize IT investments.

- Customer analytics. Understand user behavior and usage trends to optimize user engagement and conversions.

- SLA management. Real-time reporting on service level agreement performance.

AppDynamics Application Analytics, which completed its beta as “Transaction Analytics,” will be available with the Fall 2014 Release.

The Latest

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 ...

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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.