Performance Analytics is Key to PADS Framework

PADS (Performance Analytics and Decision Support) Framework Components
Gabriel Lowy

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

The "point of delivery", which is where users access composite apps, is the only perspective from which user experience should be evaluated. Thus, the most relevant metric for IT teams is not about infrastructure utilization. Instead, it is at what point of utilization the user experience begins to degrade. This means transaction completion. If transactions do not complete, user experience suffers as does business performance.

The PADS (Performance Analytics and Decision Support) Framework is composed of holistically connected next-generation Application Performance Management (APM) and operational intelligence platforms.

Performance Analytics

The performance analytics (PA) platform incorporates network, infrastructure, application and transaction monitoring, 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.

More advanced platforms already perform real-time monitoring. Leading players have evolved their platforms to perform real-time deep-dive packet inspection that can be correlated for root cause diagnostics and trouble-shooting. They can trace transactions from the user’s perspective, creating metadata tags that add business context for when application performance issues arise.

Correlation and analytics engines must include key performance indicators (KPIs) as guideposts to align with key business processes.  A holistic approach lets the level of granularity be adjusted to the person viewing the application performance. For example, a business user’s requirements will differ from an operations manager, which in turn will be different from a network engineer.

Next-generation solutions are all capable of collecting vast amounts of transaction data against which they can run analytics for a variety of use cases, most of which are user experience related. The interactive and measurable nature of Web activity also enables businesses to determine how users – both internal and external – experience their applications, including the time it takes for Web pages to load and transactions to be processed.

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 user experience more granularly.

Operational Intelligence

Within the 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 collects, indexes, correlates and analyzes log and other forms of machine data at massive scale to help IT and business users gain real-time insights from disparate data types and sources.

Complicating the issue of just doing big data analytics on the systems a company has on premises is the fact that more and more applications are running out on the cloud. And growing numbers of users are accessing both internal and cloud-based apps remotely from mobile devices. The platform makes heavy use of timestamps and keywords and then applies special algorithms to correlate data into events.

Big data sets are beyond the scope of conventional relational database management systems utilizing SQL query language. As such, an OI platform complements traditional business intelligence and data warehousing through its ability to incorporate newer forms of unstructured data.

To achieve scale, the platform leverages massively parallel processing and big data analytics capabilities. With distributed search databases, role-based access control, and the ability to rifle through terabytes of log data daily, the OI platform can accommodate the data generation systems of large enterprises.

Analytics Unifies the Platforms

These holistically linked platforms correlate billions of transaction metrics and identify patterns that provide early warning signs of impending trouble. Analytics can help reduce time being spent on correlating information collected by different tools that monitor infrastructure, network, applications and transactions, including real user and synthetic transactions. This should also include tools that are being deployed independent of IT.

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 allows 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.

To provide insights that business users can understand and value, IT must establish an effective link between performance management and analytics. The level of granularity can be adjusted to the person viewing the performance of the service or the network. For example, a line of business user’s requirements will differ from an operations manager, which in turn will be different from a network engineer.

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 comprise the Framework. In addition to assuring application availability and user experience, the PADS Framework provides pre-emptive warnings of systems failure.

For today’s loosely-couple application architectures, the PADS Framework provides enterprises with a strategic approach to ensuring application performance and user experience. Studies have shown that across different industry sectors, companies taking a unified approach outperform their peer groups in achieving ROI and risk management objectives.

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Performance Analytics is Key to PADS Framework

PADS (Performance Analytics and Decision Support) Framework Components
Gabriel Lowy

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

The "point of delivery", which is where users access composite apps, is the only perspective from which user experience should be evaluated. Thus, the most relevant metric for IT teams is not about infrastructure utilization. Instead, it is at what point of utilization the user experience begins to degrade. This means transaction completion. If transactions do not complete, user experience suffers as does business performance.

The PADS (Performance Analytics and Decision Support) Framework is composed of holistically connected next-generation Application Performance Management (APM) and operational intelligence platforms.

Performance Analytics

The performance analytics (PA) platform incorporates network, infrastructure, application and transaction monitoring, 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.

More advanced platforms already perform real-time monitoring. Leading players have evolved their platforms to perform real-time deep-dive packet inspection that can be correlated for root cause diagnostics and trouble-shooting. They can trace transactions from the user’s perspective, creating metadata tags that add business context for when application performance issues arise.

Correlation and analytics engines must include key performance indicators (KPIs) as guideposts to align with key business processes.  A holistic approach lets the level of granularity be adjusted to the person viewing the application performance. For example, a business user’s requirements will differ from an operations manager, which in turn will be different from a network engineer.

Next-generation solutions are all capable of collecting vast amounts of transaction data against which they can run analytics for a variety of use cases, most of which are user experience related. The interactive and measurable nature of Web activity also enables businesses to determine how users – both internal and external – experience their applications, including the time it takes for Web pages to load and transactions to be processed.

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 user experience more granularly.

Operational Intelligence

Within the 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 collects, indexes, correlates and analyzes log and other forms of machine data at massive scale to help IT and business users gain real-time insights from disparate data types and sources.

Complicating the issue of just doing big data analytics on the systems a company has on premises is the fact that more and more applications are running out on the cloud. And growing numbers of users are accessing both internal and cloud-based apps remotely from mobile devices. The platform makes heavy use of timestamps and keywords and then applies special algorithms to correlate data into events.

Big data sets are beyond the scope of conventional relational database management systems utilizing SQL query language. As such, an OI platform complements traditional business intelligence and data warehousing through its ability to incorporate newer forms of unstructured data.

To achieve scale, the platform leverages massively parallel processing and big data analytics capabilities. With distributed search databases, role-based access control, and the ability to rifle through terabytes of log data daily, the OI platform can accommodate the data generation systems of large enterprises.

Analytics Unifies the Platforms

These holistically linked platforms correlate billions of transaction metrics and identify patterns that provide early warning signs of impending trouble. Analytics can help reduce time being spent on correlating information collected by different tools that monitor infrastructure, network, applications and transactions, including real user and synthetic transactions. This should also include tools that are being deployed independent of IT.

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 allows 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.

To provide insights that business users can understand and value, IT must establish an effective link between performance management and analytics. The level of granularity can be adjusted to the person viewing the performance of the service or the network. For example, a line of business user’s requirements will differ from an operations manager, which in turn will be different from a network engineer.

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 comprise the Framework. In addition to assuring application availability and user experience, the PADS Framework provides pre-emptive warnings of systems failure.

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