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

Gabriel Lowy

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.

Start with Part One of Introducing the Performance Analytics and Decision Support (PADS) Framework

The PADS framework connects unified next-generation performance management and operational intelligence technologies into holistic, integrated platforms that consolidate multiple previously discrete functions. These platforms work in concert, as performance data analytics provides physical and logical knowledge of the computing environment to allow for more powerful and granular data queries, discovery and manipulation.

Expect these platforms to evolve further toward operational intelligence by expanding the types of data sources they can collect and correlate. They will also drive deeper into analytics, including predictive capabilities, to allow IT – and eventually, line of business users – to monitor the performance of services more granularly.

The performance analytics platform incorporates network, infrastructure, application and business transaction monitoring (NPM/IPM/APM/BTM), which feeds an advanced correlation and analytics engine. A single unified view of all components that support a service facilitates the management of service delivery and problem resolution.

Within a PADS framework, users can then feed this information about the application delivery chain and user experience upstream into an operational intelligence (OI) platform. The OI platform can then integrate this data with other types of information to improve decision making throughout the organization.

An OI platform not only ingests data from performance analytics platforms, but a far wider variety of machine and streaming data that are in semi-structured or unstructured formats. Consolidating this data to make it readily searchable can reveal previously undetected patterns or unique events. OI platforms provide a more unified view of events, which are often delivered from multiple streams as messages, to enable more efficient correlation and analysis.

The twin missions of the framework are to:

1. Allow IT to be more proactive in anticipating, identifying and resolving performance problems by focusing on user/customer experience.

2. Enable IT to become a strategic provider and orchestrator of internally and externally sourced services to business units that can leverage operational intelligence.

Ultimately, the PADS Framework can help organizations achieve the three return on investment (ROI) objectives:

1. Reducing costs

2. Enhancing productivity

3. Generating incremental revenues

PADS can also be used to secure valuable systems and data, thereby reducing operational risk while ensuring compliance with GRC (governance, regulatory, compliance) mandates.

Analytics: Going Beyond Montitoring

The PADS framework goes beyond real-time monitoring to offer predictive analytics, which is one of the most important market trends. Another is the ability to scale to big data requirements and interface with newer NoSQL databases. In addition to providing pre-emptive warnings of systems failure, the framework assures application availability and user experience as well as flexible scaling.

The performance analytics platform includes real-time analysis of application and service performance across both physical and virtual environments by dynamically tracking, capturing and analyzing complex service delivery transactions across multi-domain IP networks.

Deep-dive analytics allow IT organizations to be more proactive by pinpointing the root cause of problems before users call the help desk and before a visitor departs a website. Correlation and analytics engines must include key performance indicators (KPIs) as guideposts to align with critical business processes. Capabilities should include data visualization to facilitate mapping resource and application dependencies and allow modeling of applications to detect patterns and predict points of failure.

Data mining that entails analysis of data to identify trends, patterns or relationships among the operational data can be used to build predictive models. Today, modeling is being facilitated by tools that automate iterative, labor-intensive processes. Newer technologies require little or no programming and can be implemented quickly with cloud-based solutions. Predictive models can now be developed by line of business users to improve a business function or process.

The key to success for the PADS framework is providing correlation and analytics engines that feed into customizable dashboards. The ability to quickly visualize and interpret a problem or opportunity that results in actionable decisions is how to derive the most value from the platforms that underlie the framework.

Related Links

Hot Topics

The Latest

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 ...

Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

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

Gabriel Lowy

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.

Start with Part One of Introducing the Performance Analytics and Decision Support (PADS) Framework

The PADS framework connects unified next-generation performance management and operational intelligence technologies into holistic, integrated platforms that consolidate multiple previously discrete functions. These platforms work in concert, as performance data analytics provides physical and logical knowledge of the computing environment to allow for more powerful and granular data queries, discovery and manipulation.

Expect these platforms to evolve further toward operational intelligence by expanding the types of data sources they can collect and correlate. They will also drive deeper into analytics, including predictive capabilities, to allow IT – and eventually, line of business users – to monitor the performance of services more granularly.

The performance analytics platform incorporates network, infrastructure, application and business transaction monitoring (NPM/IPM/APM/BTM), which feeds an advanced correlation and analytics engine. A single unified view of all components that support a service facilitates the management of service delivery and problem resolution.

Within a PADS framework, users can then feed this information about the application delivery chain and user experience upstream into an operational intelligence (OI) platform. The OI platform can then integrate this data with other types of information to improve decision making throughout the organization.

An OI platform not only ingests data from performance analytics platforms, but a far wider variety of machine and streaming data that are in semi-structured or unstructured formats. Consolidating this data to make it readily searchable can reveal previously undetected patterns or unique events. OI platforms provide a more unified view of events, which are often delivered from multiple streams as messages, to enable more efficient correlation and analysis.

The twin missions of the framework are to:

1. Allow IT to be more proactive in anticipating, identifying and resolving performance problems by focusing on user/customer experience.

2. Enable IT to become a strategic provider and orchestrator of internally and externally sourced services to business units that can leverage operational intelligence.

Ultimately, the PADS Framework can help organizations achieve the three return on investment (ROI) objectives:

1. Reducing costs

2. Enhancing productivity

3. Generating incremental revenues

PADS can also be used to secure valuable systems and data, thereby reducing operational risk while ensuring compliance with GRC (governance, regulatory, compliance) mandates.

Analytics: Going Beyond Montitoring

The PADS framework goes beyond real-time monitoring to offer predictive analytics, which is one of the most important market trends. Another is the ability to scale to big data requirements and interface with newer NoSQL databases. In addition to providing pre-emptive warnings of systems failure, the framework assures application availability and user experience as well as flexible scaling.

The performance analytics platform includes real-time analysis of application and service performance across both physical and virtual environments by dynamically tracking, capturing and analyzing complex service delivery transactions across multi-domain IP networks.

Deep-dive analytics allow IT organizations to be more proactive by pinpointing the root cause of problems before users call the help desk and before a visitor departs a website. Correlation and analytics engines must include key performance indicators (KPIs) as guideposts to align with critical business processes. Capabilities should include data visualization to facilitate mapping resource and application dependencies and allow modeling of applications to detect patterns and predict points of failure.

Data mining that entails analysis of data to identify trends, patterns or relationships among the operational data can be used to build predictive models. Today, modeling is being facilitated by tools that automate iterative, labor-intensive processes. Newer technologies require little or no programming and can be implemented quickly with cloud-based solutions. Predictive models can now be developed by line of business users to improve a business function or process.

The key to success for the PADS framework is providing correlation and analytics engines that feed into customizable dashboards. The ability to quickly visualize and interpret a problem or opportunity that results in actionable decisions is how to derive the most value from the platforms that underlie the framework.

Related Links

Hot Topics

The Latest

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 ...

Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

Keys to Building a Partner Ecosystem That Scales

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 ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...