5 Tips for Effective Mobile Monitoring

Eran Kinsbruner

Here are some important guidelines to remember when you start to plan your mobile monitoring strategy ...

1. Real device monitoring is a "must"

Only real devices let you capture the true mobile end user experience in terms of application performance and availability. Be sure to use non-jailbroken/rooted devices, production version apps (non-modified), run complex user scenarios using complex client logic on real devices, and measure client-side performance impact on the overall user experience. Other monitoring solutions, such as browser emulation, fail to reflect the true mobile user experience.

2. Leverage both RUM and Synthetic monitoring techniques

Real User Monitoring is the best way to know what is happening on a user's device, as it is based on an agent within the application that collects data and communicates it to the monitoring server. Synthetic monitoring allows organizations to track application behavior against real networks globally in a “clean-room” environment, and provides an excellent solution for debugging problems identified in the field.

3. Extend your existing monitoring solution to mobile

There is no need to re-invent the wheel. Your operations center has accumulated valuable experience in monitoring and triaging incidents. There is no need to create new processes, re-train personnel or buy completely new solutions. Rather, it is advisable to expand those processes to cover your mobile initiative as well by making it mobile-relevant.

4. Select your synthetic monitoring coverage wisely

Synthetic monitoring, by definition, provides sampled coverage of the audience, devices, carriers and locations, and user scenarios. It is not realistic to provide coverage for everything. Identify those combinations that are relevant to your business objectives and work from there.

5. Ensure the reliability of your monitoring solution to eliminate false alerts

There is nothing most frustrating to your Ops Center staff than false alerts. Your mobile monitoring solution must comply with stringent SLAs to ensure that your engineers do not waste time on alerts caused by problems related to device availability, for example. Device redundancy, together with identification and reduction of alerts driven from known issues, help to ensure proper attention to real issues that are impacting end users.

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5 Tips for Effective Mobile Monitoring

Eran Kinsbruner

Here are some important guidelines to remember when you start to plan your mobile monitoring strategy ...

1. Real device monitoring is a "must"

Only real devices let you capture the true mobile end user experience in terms of application performance and availability. Be sure to use non-jailbroken/rooted devices, production version apps (non-modified), run complex user scenarios using complex client logic on real devices, and measure client-side performance impact on the overall user experience. Other monitoring solutions, such as browser emulation, fail to reflect the true mobile user experience.

2. Leverage both RUM and Synthetic monitoring techniques

Real User Monitoring is the best way to know what is happening on a user's device, as it is based on an agent within the application that collects data and communicates it to the monitoring server. Synthetic monitoring allows organizations to track application behavior against real networks globally in a “clean-room” environment, and provides an excellent solution for debugging problems identified in the field.

3. Extend your existing monitoring solution to mobile

There is no need to re-invent the wheel. Your operations center has accumulated valuable experience in monitoring and triaging incidents. There is no need to create new processes, re-train personnel or buy completely new solutions. Rather, it is advisable to expand those processes to cover your mobile initiative as well by making it mobile-relevant.

4. Select your synthetic monitoring coverage wisely

Synthetic monitoring, by definition, provides sampled coverage of the audience, devices, carriers and locations, and user scenarios. It is not realistic to provide coverage for everything. Identify those combinations that are relevant to your business objectives and work from there.

5. Ensure the reliability of your monitoring solution to eliminate false alerts

There is nothing most frustrating to your Ops Center staff than false alerts. Your mobile monitoring solution must comply with stringent SLAs to ensure that your engineers do not waste time on alerts caused by problems related to device availability, for example. Device redundancy, together with identification and reduction of alerts driven from known issues, help to ensure proper attention to real issues that are impacting end users.

Related Links

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Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Empowering the Human Side of the Autonomous IT Age

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AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...