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The Need for the PADS Framework Emerges

Gabriel Lowy

This post is an excerpt of the new Tech-Tonics Advisors report: The PADS Framework for Application Performance and User Experience.

The PADS (Performance Analytics and Decision Support) Framework is a more strategic approach to linking next-generation performance management and big data analytics technologies. It establishes best practices for assuring user experience, reducing risk and improving decision making. The Framework provides real-time intelligence that enables companies to build customer satisfaction and loyalty, and improve operational efficiency.

The PADS Framework cuts through increased complexity to better understand the properties of system components and their place in the overall application delivery chain. It does this through a higher-level assessment of their relationships to each other, as well as to the wider system and environment.

The PADS Framework promotes DevOps practices for improved application governance by breaking down IT and business unit data silos. It facilitates collaboration and communication in a more productive and cost-efficient environment by consolidating multiple functions often performed separately.

Holistically integrated platforms work in concert, as Application Performance Management (APM) data and operational analytics provides physical and logical knowledge of the computing environment to allow for more powerful and granular data queries, discovery and manipulation. By correlating real-time streams of machine data and other types of big data with the historical data contained in legacy systems, the platform allows users to gain a more complete perspective. Modeling and mapping capabilities enable faster drill-down and mean time to resolution.

A New Approach to User Experience for New Computing Architectures

New distributed computing architectures and approaches to agile application development have made computing far more scalable and dynamic than ever before. They leverage shared services and cloud infrastructure to create loosely coupled and asynchronous applications.

DevOps practices promise to drive meaningful ROI for organizations consolidating infrastructure, migrating to cloud-based services or developing Web and mobile applications. Yet the more business processes come to depend on multiple applications and the underlying infrastructure, the more susceptible they are to performance degradation.

Performance has historically been measured at the individual component or system level, such as a network device or connection, a firewall or load balancer, a database or a web application server. As environments become more complex, the sum-of-the-parts approach does not accurately reflect true user experience.

Analyzing or mitigating risk in only one component of the system does not prevent disastrous events or failures. In fact, they can be amplified, as one component affects another and then another, spreading risk throughout the system.

More enterprises have recognized the need for a new generation of performance analytics techniques that go beyond the scope of traditional monitoring tools, which were designed for smaller and more static environments. Widespread adoption of virtualization technologies and associated virtual machine migration, balancing between public, hybrid and private cloud environments, and the traffic explosion of latency-sensitive applications such as market data, streaming video and voice-over-IP necessitates a new approach.

Leveraging gains in processing power and storage capacity, IT teams can extract and analyze more performance-related data points across the application delivery chain to gain deeper intelligence. They can understand what levels of performance (i.e. speed and availability) are needed from their cloud and mobile applications in order to deliver fast, reliable and highly satisfying end-user experiences.

Aiming for Better Application Governance

Understanding key fundamental business drivers and working in concert with application owners – and each other – IT teams can meet end-user performance expectations to enable strategic initiatives and positively impact financial results. Optimizing performance allows IT to evolve toward a process-oriented service delivery philosophy. In doing so, IT also aligns more closely with strategic initiatives of an increasingly data-driven enterprise. This is all the more important as big data sources and applications become integral to decision-making.

Through a unified approach, IT can help their companies leverage technology investments to discover, interpret and respond to the myriad events that impact their operations, security, compliance and competitiveness. Teams that have adopted a unified approach use 30% fewer tools yet experience far fewer service interruptions, discover performance problems proactively and typically spend a fraction of the time on problem resolution than most of their peers who either have too many tools or none at all.

A clear linkage has emerged with how improvements in user experiences are driving financial benefits. But in order to realize the benefits of engaged employees and satisfied customers, application performance must be stellar – consistently.

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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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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 ...

The Need for the PADS Framework Emerges

Gabriel Lowy

This post is an excerpt of the new Tech-Tonics Advisors report: The PADS Framework for Application Performance and User Experience.

The PADS (Performance Analytics and Decision Support) Framework is a more strategic approach to linking next-generation performance management and big data analytics technologies. It establishes best practices for assuring user experience, reducing risk and improving decision making. The Framework provides real-time intelligence that enables companies to build customer satisfaction and loyalty, and improve operational efficiency.

The PADS Framework cuts through increased complexity to better understand the properties of system components and their place in the overall application delivery chain. It does this through a higher-level assessment of their relationships to each other, as well as to the wider system and environment.

The PADS Framework promotes DevOps practices for improved application governance by breaking down IT and business unit data silos. It facilitates collaboration and communication in a more productive and cost-efficient environment by consolidating multiple functions often performed separately.

Holistically integrated platforms work in concert, as Application Performance Management (APM) data and operational analytics provides physical and logical knowledge of the computing environment to allow for more powerful and granular data queries, discovery and manipulation. By correlating real-time streams of machine data and other types of big data with the historical data contained in legacy systems, the platform allows users to gain a more complete perspective. Modeling and mapping capabilities enable faster drill-down and mean time to resolution.

A New Approach to User Experience for New Computing Architectures

New distributed computing architectures and approaches to agile application development have made computing far more scalable and dynamic than ever before. They leverage shared services and cloud infrastructure to create loosely coupled and asynchronous applications.

DevOps practices promise to drive meaningful ROI for organizations consolidating infrastructure, migrating to cloud-based services or developing Web and mobile applications. Yet the more business processes come to depend on multiple applications and the underlying infrastructure, the more susceptible they are to performance degradation.

Performance has historically been measured at the individual component or system level, such as a network device or connection, a firewall or load balancer, a database or a web application server. As environments become more complex, the sum-of-the-parts approach does not accurately reflect true user experience.

Analyzing or mitigating risk in only one component of the system does not prevent disastrous events or failures. In fact, they can be amplified, as one component affects another and then another, spreading risk throughout the system.

More enterprises have recognized the need for a new generation of performance analytics techniques that go beyond the scope of traditional monitoring tools, which were designed for smaller and more static environments. Widespread adoption of virtualization technologies and associated virtual machine migration, balancing between public, hybrid and private cloud environments, and the traffic explosion of latency-sensitive applications such as market data, streaming video and voice-over-IP necessitates a new approach.

Leveraging gains in processing power and storage capacity, IT teams can extract and analyze more performance-related data points across the application delivery chain to gain deeper intelligence. They can understand what levels of performance (i.e. speed and availability) are needed from their cloud and mobile applications in order to deliver fast, reliable and highly satisfying end-user experiences.

Aiming for Better Application Governance

Understanding key fundamental business drivers and working in concert with application owners – and each other – IT teams can meet end-user performance expectations to enable strategic initiatives and positively impact financial results. Optimizing performance allows IT to evolve toward a process-oriented service delivery philosophy. In doing so, IT also aligns more closely with strategic initiatives of an increasingly data-driven enterprise. This is all the more important as big data sources and applications become integral to decision-making.

Through a unified approach, IT can help their companies leverage technology investments to discover, interpret and respond to the myriad events that impact their operations, security, compliance and competitiveness. Teams that have adopted a unified approach use 30% fewer tools yet experience far fewer service interruptions, discover performance problems proactively and typically spend a fraction of the time on problem resolution than most of their peers who either have too many tools or none at all.

A clear linkage has emerged with how improvements in user experiences are driving financial benefits. But in order to realize the benefits of engaged employees and satisfied customers, application performance must be stellar – consistently.

Hot Topics

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 ...