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Holistic Unified User Experience Assurance

Gabriel Lowy

With the proliferation of composite applications for cloud and mobility, monitoring individual components of the application delivery chain is no longer an effective way to assure user experience. IT organizations must evolve toward a holistic, more collaborative methodology based on a service-delivery principle that is more aligned with corporate strategy.

The more business processes come to depend on multiple applications and the underlying infrastructure, the more susceptible they are to performance degradation. Unfortunately, most enterprises still monitor and manage user experience from traditional technology domain silos, such as server, network, application, operating system or security. As computing and processes continue to shift from legacy architecture, this approach only perpetuates an ineffective, costly and politically-charged environment.

Key drivers necessitating change include widespread adoption of virtualization technologies and associated virtual machine (VM) migration, cloud balancing between public, hybrid and private cloud environments, and the traffic explosion of latency-sensitive applications such as streaming video and voice-over-IP (VoIP).

The migration toward IaaS providers such as Amazon, Google and Microsoft underscore the need for holistic user experience assurance across multiple data centers, which are increasingly beyond the corporate firewall.

Moreover, as video joins VoIP as a primary traffic generator competing for bandwidth on enterprise networks, users and upper management will become increasingly intolerant of poor performance.

By having different tools for monitoring data, VoIP and video traffic, enterprise IT silos experience rising cost, complexity and mean time to repair. Traditionally, IT has used delay, jitter and packet loss as proxies for network performance. Legacy network performance management (NPM) tools were augmented with WAN optimization technology to accelerate traffic between data center and branch office user.

A more granular approach is to look at application payload and measuring the quality of voice and video communications. For unified communications (UC), this includes monitoring signaling between the UC components.

Meanwhile, conventional application performance management (APM) tools monitor performance of individual servers rather than across the application delivery chain – from the web front end through business logic processes to the database. While synthetic transactions provide a clearer view into user experience, they tend to add overhead. They also do not experience the same network latencies that are common to branch office networks since they originate in the same data center as the application server. Finally, being synthetic, they are not representative of “live” production transactions.

Service delivery must be unified across the different IT silos to enable visibility across all applications, services, locations and devices. Truly holistic end-to-end user experience assurance must also map resource and application dependencies. It needs to have a single view of all components that support a service.

In order to achieve this, data has to be assimilated from network service providers and cloud service providers in addition to data from within the enterprise. Correlation and analytics engines must include key performance indicators (KPIs) as guideposts to align with critical business processes.

Through a holistic approach, the level of granularity can also be adjusted to the person viewing the performance of the service or the network. For example, a business user’s requirements will differ from an operations manager, which in turn will be different from a network engineer.

A unified platform integrates full visibility from the network’s vantage point, which touches service and cloud providers, with packet-level transaction tracing granularity. The platform includes visualization for mapping resource interdependencies as well as real-time and historical data analytics capabilities.

Taking a holistic unified approach to user experience assurance enables IT to identify service degradation faster, and before the end user does. The result is improved ROI throughout the organization though reduced costs and higher productivity.

Optimizing performance of services and users also 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 swamps the enterprise. It is why I suggested in a recent article that user experience assurance should be big data job number one.

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Holistic Unified User Experience Assurance

Gabriel Lowy

With the proliferation of composite applications for cloud and mobility, monitoring individual components of the application delivery chain is no longer an effective way to assure user experience. IT organizations must evolve toward a holistic, more collaborative methodology based on a service-delivery principle that is more aligned with corporate strategy.

The more business processes come to depend on multiple applications and the underlying infrastructure, the more susceptible they are to performance degradation. Unfortunately, most enterprises still monitor and manage user experience from traditional technology domain silos, such as server, network, application, operating system or security. As computing and processes continue to shift from legacy architecture, this approach only perpetuates an ineffective, costly and politically-charged environment.

Key drivers necessitating change include widespread adoption of virtualization technologies and associated virtual machine (VM) migration, cloud balancing between public, hybrid and private cloud environments, and the traffic explosion of latency-sensitive applications such as streaming video and voice-over-IP (VoIP).

The migration toward IaaS providers such as Amazon, Google and Microsoft underscore the need for holistic user experience assurance across multiple data centers, which are increasingly beyond the corporate firewall.

Moreover, as video joins VoIP as a primary traffic generator competing for bandwidth on enterprise networks, users and upper management will become increasingly intolerant of poor performance.

By having different tools for monitoring data, VoIP and video traffic, enterprise IT silos experience rising cost, complexity and mean time to repair. Traditionally, IT has used delay, jitter and packet loss as proxies for network performance. Legacy network performance management (NPM) tools were augmented with WAN optimization technology to accelerate traffic between data center and branch office user.

A more granular approach is to look at application payload and measuring the quality of voice and video communications. For unified communications (UC), this includes monitoring signaling between the UC components.

Meanwhile, conventional application performance management (APM) tools monitor performance of individual servers rather than across the application delivery chain – from the web front end through business logic processes to the database. While synthetic transactions provide a clearer view into user experience, they tend to add overhead. They also do not experience the same network latencies that are common to branch office networks since they originate in the same data center as the application server. Finally, being synthetic, they are not representative of “live” production transactions.

Service delivery must be unified across the different IT silos to enable visibility across all applications, services, locations and devices. Truly holistic end-to-end user experience assurance must also map resource and application dependencies. It needs to have a single view of all components that support a service.

In order to achieve this, data has to be assimilated from network service providers and cloud service providers in addition to data from within the enterprise. Correlation and analytics engines must include key performance indicators (KPIs) as guideposts to align with critical business processes.

Through a holistic approach, the level of granularity can also be adjusted to the person viewing the performance of the service or the network. For example, a business user’s requirements will differ from an operations manager, which in turn will be different from a network engineer.

A unified platform integrates full visibility from the network’s vantage point, which touches service and cloud providers, with packet-level transaction tracing granularity. The platform includes visualization for mapping resource interdependencies as well as real-time and historical data analytics capabilities.

Taking a holistic unified approach to user experience assurance enables IT to identify service degradation faster, and before the end user does. The result is improved ROI throughout the organization though reduced costs and higher productivity.

Optimizing performance of services and users also 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 swamps the enterprise. It is why I suggested in a recent article that user experience assurance should be big data job number one.

The Latest

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 ...

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 ...

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 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 ...

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? ...

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 ...

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 ...

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 ...

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 ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...