Introducing the Performance Analytics and Decision Support (PADS) Framework - Part One

Gabriel Lowy

A new Performance Analytics and Decision Support (PADS) framework linking advanced performance management and big data analytics technologies will emerge in 2014. The PADS framework enables organizations to gain deep and real-time visibility into, and predictive intelligence from, increasingly complex IT systems across the entire application delivery chain.

PADS establishes best practices for assuring user experience, reducing risk and improving operational decision making in a more efficient, secure and timely fashion. A more holistic approach that breaks down data silos across different IT teams and departments is the path to assuring service delivery, gaining deeper systems and customer insights, and improving operational efficiency.

The Big Data Challenge

As IT groups acquired discrete tools that focused on a particular hardware, network or software issue, many organizations have ended up with a patchwork quilt of point solutions that do not work well together. And while each tool might be indicating that performance of a particular segment or component is “normal”, outages persist and the actual user experience continues to disappoint.

New distributed computing architectures and approaches to agile application development have made computing far more scalable and dynamic than ever before. But cloud, mobile and social megatrends have also resulted in unprecedented levels of complexity. As a result, more components of the application delivery chain are obscured from IT and line of business owners.

Despite the wealth of data and content available today, most business users continue to struggle to access information they need to gain deeper insights into the business for better and faster decision making. Traditional performance monitoring solutions for application, network, infrastructure and business transactions have become overwhelmed by the scale of data required to comprehensively manage application performance.

The proliferation of server virtualization and the tools needed to monitor and manage virtualized dynamic infrastructures and highly distributed application architectures only expand the data points and metrics that need to be analyzed.

Consequently, vital information is often overlooked, resulting in missed opportunities to uncover hidden patterns, relationships and dependencies. Additionally, whatever data is gathered is not normalized or time synchronized, making analysis and rapid problem resolution impossible. Yet pouring more data into obsolete analytics tools only compounds the problem.

Making Performance a Priority

Performance visibility and greater operational intelligence should be paramount to all organizations amid rising systems complexity and unabated data growth. Numerous surveys have shown high availability of applications as the top priority of business users, customers and CIOs. But the more business processes come to depend on multiple applications and the underlying infrastructure, the more susceptible they are to performance degradation.

The common components of ROI – reduced operating costs, enhanced business productivity, and incremental revenue generation – are closely associated with application performance. Service outages can be quite costly. Depending on the industry sector, slow responsiveness or complete outage (brownouts or downtime) of a company's most business critical application can cost between $100,000 and $1 million per hour. The fallout from poor transaction performance can be a loss of customers, regulatory fines and damage to firm reputation.

Nothing shines a light on an IT team's success or failure as application performance and availability. With uptime as their priority, they need to adapt a more holistic approach to performance management and decision analytics. Through best practices, they can help their companies leverage IT investments to discover, interpret and respond to the myriad events that impact their operations, security, compliance and competitiveness.

A New Generation of Performance Analytics Techniques

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.

These new performance analytics techniques must help the enterprise in three ways:

First, enterprises need to understand what levels of performance (i.e. speed and availability) are needed from their increasingly cloud-based and mobile applications in order to deliver fast, reliable and highly satisfying end-user experiences. To better understand the properties of the components and their place in the overall application delivery chain requires a higher-level assessment of the relationships to each other as well as to the wider system and environment. A comprehensive performance analytics platform provides visibility across the entire application delivery chain – from behind the firewall and out to the Web, including third-party cloud providers.

Second, the “point of delivery”, which is where the user accesses a composite application, is the only perspective from which user experience should be addressed. As such, the most relevant metric for any IT organization is not about infrastructure utilization. Instead, it is at what point of utilization the user experience begins to degrade. Enterprises need to measure the true experiences of their most important end-user segments, including those that are remote and mobile.

Third, to provide insights that line of business users can understand and value, IT must establish an effective link between performance management and analytics.

The PADS Framework, for Performance Analytics and Decision Support, represents a more holistic approach to adaptive, proactive and predictive operational data management and analysis. The framework links advanced performance management and big data analytics technologies to enable organizations to gain deep and real-time visibility into, and predictive intelligence from, increasingly complex virtualized and mobile systems across the entire application delivery chain.

Find out more about PADS: Introducing the Performance Analytics and Decision Support (PADS) Framework- Part Two

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Introducing the Performance Analytics and Decision Support (PADS) Framework - Part One

Gabriel Lowy

A new Performance Analytics and Decision Support (PADS) framework linking advanced performance management and big data analytics technologies will emerge in 2014. The PADS framework enables organizations to gain deep and real-time visibility into, and predictive intelligence from, increasingly complex IT systems across the entire application delivery chain.

PADS establishes best practices for assuring user experience, reducing risk and improving operational decision making in a more efficient, secure and timely fashion. A more holistic approach that breaks down data silos across different IT teams and departments is the path to assuring service delivery, gaining deeper systems and customer insights, and improving operational efficiency.

The Big Data Challenge

As IT groups acquired discrete tools that focused on a particular hardware, network or software issue, many organizations have ended up with a patchwork quilt of point solutions that do not work well together. And while each tool might be indicating that performance of a particular segment or component is “normal”, outages persist and the actual user experience continues to disappoint.

New distributed computing architectures and approaches to agile application development have made computing far more scalable and dynamic than ever before. But cloud, mobile and social megatrends have also resulted in unprecedented levels of complexity. As a result, more components of the application delivery chain are obscured from IT and line of business owners.

Despite the wealth of data and content available today, most business users continue to struggle to access information they need to gain deeper insights into the business for better and faster decision making. Traditional performance monitoring solutions for application, network, infrastructure and business transactions have become overwhelmed by the scale of data required to comprehensively manage application performance.

The proliferation of server virtualization and the tools needed to monitor and manage virtualized dynamic infrastructures and highly distributed application architectures only expand the data points and metrics that need to be analyzed.

Consequently, vital information is often overlooked, resulting in missed opportunities to uncover hidden patterns, relationships and dependencies. Additionally, whatever data is gathered is not normalized or time synchronized, making analysis and rapid problem resolution impossible. Yet pouring more data into obsolete analytics tools only compounds the problem.

Making Performance a Priority

Performance visibility and greater operational intelligence should be paramount to all organizations amid rising systems complexity and unabated data growth. Numerous surveys have shown high availability of applications as the top priority of business users, customers and CIOs. But the more business processes come to depend on multiple applications and the underlying infrastructure, the more susceptible they are to performance degradation.

The common components of ROI – reduced operating costs, enhanced business productivity, and incremental revenue generation – are closely associated with application performance. Service outages can be quite costly. Depending on the industry sector, slow responsiveness or complete outage (brownouts or downtime) of a company's most business critical application can cost between $100,000 and $1 million per hour. The fallout from poor transaction performance can be a loss of customers, regulatory fines and damage to firm reputation.

Nothing shines a light on an IT team's success or failure as application performance and availability. With uptime as their priority, they need to adapt a more holistic approach to performance management and decision analytics. Through best practices, they can help their companies leverage IT investments to discover, interpret and respond to the myriad events that impact their operations, security, compliance and competitiveness.

A New Generation of Performance Analytics Techniques

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.

These new performance analytics techniques must help the enterprise in three ways:

First, enterprises need to understand what levels of performance (i.e. speed and availability) are needed from their increasingly cloud-based and mobile applications in order to deliver fast, reliable and highly satisfying end-user experiences. To better understand the properties of the components and their place in the overall application delivery chain requires a higher-level assessment of the relationships to each other as well as to the wider system and environment. A comprehensive performance analytics platform provides visibility across the entire application delivery chain – from behind the firewall and out to the Web, including third-party cloud providers.

Second, the “point of delivery”, which is where the user accesses a composite application, is the only perspective from which user experience should be addressed. As such, the most relevant metric for any IT organization is not about infrastructure utilization. Instead, it is at what point of utilization the user experience begins to degrade. Enterprises need to measure the true experiences of their most important end-user segments, including those that are remote and mobile.

Third, to provide insights that line of business users can understand and value, IT must establish an effective link between performance management and analytics.

The PADS Framework, for Performance Analytics and Decision Support, represents a more holistic approach to adaptive, proactive and predictive operational data management and analysis. The framework links advanced performance management and big data analytics technologies to enable organizations to gain deep and real-time visibility into, and predictive intelligence from, increasingly complex virtualized and mobile systems across the entire application delivery chain.

Find out more about PADS: Introducing the Performance Analytics and Decision Support (PADS) Framework- Part Two

Related Links

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Observability Is the New Control Plane for Enterprise Transformation

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

When AI Becomes the Corporate OS

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

The $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Breaking Down Agentic AI Fragmentation and Complexity with Governance

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

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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 ...

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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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