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The Need for Unified User Experience

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 unified approach that promotes collaboration and efficiency to better align with corporate return on investment (ROI) and risk management objectives.

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, the adoption of DevOps practices 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 unifying 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 resolution (MTTR). 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.

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.

Characteristics of a Unified Platform

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. 

A 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 through 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 applications and sources become a larger part of decision-making and data management.

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Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

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The Need for Unified User Experience

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 unified approach that promotes collaboration and efficiency to better align with corporate return on investment (ROI) and risk management objectives.

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, the adoption of DevOps practices 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 unifying 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 resolution (MTTR). 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.

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.

Characteristics of a Unified Platform

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. 

A 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 through 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 applications and sources become a larger part of decision-making and data management.

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IT organizations have historically measured success by how quickly they can respond when something goes wrong. The entire discipline of Incident Management has been optimized around mean time to resolution, first-response SLAs and ticket closure rates. But new research suggests that even though this is a well-executed playbook, it's no longer enough to retain customers ...

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...