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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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.

Related Links

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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