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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

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Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

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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

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...