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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

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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 ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

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 ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...