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Streamline the Path Between App Performance and User Experience

Justin Collier
SmartBear

Traditional observability has driven key insights into app performance via APM solutions. Teams leverage metrics, logs, and traces, providing them with insights into performance behavior, enabling them to detect and resolve issues. This approach helps ensure that applications run smoothly and efficiently, meeting user expectations, and in turn, business objectives.

On the user side, developers rely on digital experience monitoring solutions to decipher data into user's experiences. However, the link between frontend and backend tools and teams and the impact on each other is not always clear. In a complex microservices-based architecture, it can get muddy — fast.

With so many systems potentially impacting applications performance, it is critical to find ways to separate insights from data that is often white noise. When cross-functional teams have clear alignment on what Key Performance Indicators (KPIs) matter to them and their users' experiences, they can implement tools and processes that best support them. In the end, there must be collective ownership.

Bridging the Gap: Application Performance and Cross-Functional Alignment

Why are organizations, and more specifically, development teams often misaligned? We build software in such a way that developers, DevOps, and IT operations teams are often not clear on business objectives or success metrics, making the job challenging.

To complicate matters, teams are moving at lightening pace. The integration of AI within DevOps is revolutionizing the way teams operate, leading to increased adoption of automation and dramatically accelerated feedback loops.

Without cross-functional alignment on the objectives, a clearly defined set of success metrics, and visibility across the software stack, teams end up trying to solve problems in a vacuum with little data or collective ownership. They end up in two different boats, maybe seemingly rowing toward the same goal, but ultimately feeling like they are competing against one another. We make rules and build fences around our domains in an effort to protect ourselves. In reality, we're only hurting teams, products, and businesses.

Teams don't have to operate like this.

Aligning early and often is critical to the success of our applications and ensures that our end users get the best digital experience possible. To do this, we need to build cultures that value collective ownership. This means that we have to open the gates in our fences and allow teams to be engaged and "in the business" of other teams. To be clear, this isn't easy and takes immense trust and vulnerability. To start, pull out your org chart and go knock (gently) on your neighbor's fence. Get to know them! You can't build collective ownership if you don't have relationships with the members of other cross-functional teams.

As a cross-functional team, you need to sit down and have an open conversation about your business objectives, how you will measure success, and how the team will have visibility into the metrics. Additionally, it is important to ensure that everyone feels a sense of ownership. Without collective ownership, you will end up right back where you started — closed gates, behind your fence. If you've never heard the term, "Disagree and Commit," the idea is to disagree when you're formulating the plan but then commit once the decision is made.

When you have cross-functional alignment and collective ownership, both teams come together and ensure you measure the digital experience of your end users and see end-to-end what the performance of the application actually looks like.

As a frontend developer, it might be easy to install a performance SDK that captures crash rates, ANRs, and screen loading times, but if you don't have performance SDKs installed on your backend systems, you are only getting a partial picture with limited visibility into why the end users experience isn't what it should be. With collective ownership, your DevOps or IT operations teams will instrument the appropriate SDKs that can give the entire cross-functional team the information needed. You must have the end-to-end visibility required to quickly assess and fix issues seen by your end users.

Why Should Organizations Separate Insights from Data?

Separating insights from data ensures that actionable information is clearly identified and prioritized, enabling better decision-making. Companies can focus on strategic improvements rather than getting lost in overwhelming volumes of information. This separation also allows for more effective communication across teams, as insights provide a concise summary of what the data reveals about performance and user experiences.

Further, leveraging AI-powered analytics is helping teams to proactively identify performance bottlenecks, predict potential issues before they arise, and automate remediation processes, enhancing efficiency and reliability throughout the software development lifecycle. This integration of AI reinforces the importance of collective ownership and cross-functional alignment, as teams collaborate to harness the full potential of these innovative technologies.

Conclusion

The journey toward optimizing app performance and enhancing user experience requires a multifaceted approach. Traditional observability, along with cross-functional alignment and collective ownership, forms the foundation for success in today's dynamic software landscape. Determining what KPIs are important to you and your users is paramount. As teams navigate the complexities, the integration of AI within DevOps is emerging as a game-changer in facilitating automation and accelerating feedback loops to unprecedented levels. This union of human collaboration and technological innovation underscores the importance of organizations to adapt, evolve, and embrace a culture that fosters synergy between teams and empowers them to unlock the potential of their customers.

Justin Collier is Senior Director of Product Management at SmartBear

Hot Topics

The Latest

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...

Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...

In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ... 

Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...

Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...

Streamline the Path Between App Performance and User Experience

Justin Collier
SmartBear

Traditional observability has driven key insights into app performance via APM solutions. Teams leverage metrics, logs, and traces, providing them with insights into performance behavior, enabling them to detect and resolve issues. This approach helps ensure that applications run smoothly and efficiently, meeting user expectations, and in turn, business objectives.

On the user side, developers rely on digital experience monitoring solutions to decipher data into user's experiences. However, the link between frontend and backend tools and teams and the impact on each other is not always clear. In a complex microservices-based architecture, it can get muddy — fast.

With so many systems potentially impacting applications performance, it is critical to find ways to separate insights from data that is often white noise. When cross-functional teams have clear alignment on what Key Performance Indicators (KPIs) matter to them and their users' experiences, they can implement tools and processes that best support them. In the end, there must be collective ownership.

Bridging the Gap: Application Performance and Cross-Functional Alignment

Why are organizations, and more specifically, development teams often misaligned? We build software in such a way that developers, DevOps, and IT operations teams are often not clear on business objectives or success metrics, making the job challenging.

To complicate matters, teams are moving at lightening pace. The integration of AI within DevOps is revolutionizing the way teams operate, leading to increased adoption of automation and dramatically accelerated feedback loops.

Without cross-functional alignment on the objectives, a clearly defined set of success metrics, and visibility across the software stack, teams end up trying to solve problems in a vacuum with little data or collective ownership. They end up in two different boats, maybe seemingly rowing toward the same goal, but ultimately feeling like they are competing against one another. We make rules and build fences around our domains in an effort to protect ourselves. In reality, we're only hurting teams, products, and businesses.

Teams don't have to operate like this.

Aligning early and often is critical to the success of our applications and ensures that our end users get the best digital experience possible. To do this, we need to build cultures that value collective ownership. This means that we have to open the gates in our fences and allow teams to be engaged and "in the business" of other teams. To be clear, this isn't easy and takes immense trust and vulnerability. To start, pull out your org chart and go knock (gently) on your neighbor's fence. Get to know them! You can't build collective ownership if you don't have relationships with the members of other cross-functional teams.

As a cross-functional team, you need to sit down and have an open conversation about your business objectives, how you will measure success, and how the team will have visibility into the metrics. Additionally, it is important to ensure that everyone feels a sense of ownership. Without collective ownership, you will end up right back where you started — closed gates, behind your fence. If you've never heard the term, "Disagree and Commit," the idea is to disagree when you're formulating the plan but then commit once the decision is made.

When you have cross-functional alignment and collective ownership, both teams come together and ensure you measure the digital experience of your end users and see end-to-end what the performance of the application actually looks like.

As a frontend developer, it might be easy to install a performance SDK that captures crash rates, ANRs, and screen loading times, but if you don't have performance SDKs installed on your backend systems, you are only getting a partial picture with limited visibility into why the end users experience isn't what it should be. With collective ownership, your DevOps or IT operations teams will instrument the appropriate SDKs that can give the entire cross-functional team the information needed. You must have the end-to-end visibility required to quickly assess and fix issues seen by your end users.

Why Should Organizations Separate Insights from Data?

Separating insights from data ensures that actionable information is clearly identified and prioritized, enabling better decision-making. Companies can focus on strategic improvements rather than getting lost in overwhelming volumes of information. This separation also allows for more effective communication across teams, as insights provide a concise summary of what the data reveals about performance and user experiences.

Further, leveraging AI-powered analytics is helping teams to proactively identify performance bottlenecks, predict potential issues before they arise, and automate remediation processes, enhancing efficiency and reliability throughout the software development lifecycle. This integration of AI reinforces the importance of collective ownership and cross-functional alignment, as teams collaborate to harness the full potential of these innovative technologies.

Conclusion

The journey toward optimizing app performance and enhancing user experience requires a multifaceted approach. Traditional observability, along with cross-functional alignment and collective ownership, forms the foundation for success in today's dynamic software landscape. Determining what KPIs are important to you and your users is paramount. As teams navigate the complexities, the integration of AI within DevOps is emerging as a game-changer in facilitating automation and accelerating feedback loops to unprecedented levels. This union of human collaboration and technological innovation underscores the importance of organizations to adapt, evolve, and embrace a culture that fosters synergy between teams and empowers them to unlock the potential of their customers.

Justin Collier is Senior Director of Product Management at SmartBear

Hot Topics

The Latest

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...

Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...

In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ... 

Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...

Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...