What is Telemetry?

Telemetry is the process of collecting, transmitting, and analyzing data from remote or inaccessible sources to monitor and control systems. It involves measuring physical or digital parameters (like temperature, speed, voltage, or system performance) and sending that data to a central location for analysis, typically in real-time.

Telemetry has a number of key components including sensors or instruments that collect data, transmission systems to send the data, a receiver to decode data, and a data processing and analysis function that will make the collected data meaningful. Telemetry can be found in a wide variety of industries from aerospace to healthcare, automotive, IT/software and utilities.

What is telemetry data in software and IT?

In software and IT, telemetry data refers to the automated collection and transmission of performance metrics, usage statistics, errors, and logs from software applications, services, or infrastructure to a centralized system for monitoring, analysis, and decision-making.

Typically software/IT telemetry data will include performance metrics, usage data, error and exception logs, health checks and custom events. For software/IT teams, telemetry data is exceptionally useful because it provides insights into user experience, performance optimization, and security threats.

Telemetry and monitoring: What’s the difference?

Telemetry and monitoring are closely related in software and IT, but they serve different purposes in the observability ecosystem.

Telemetry is the process of collecting and transmitting data from systems or applications. Telemetry is an instrumented mechanism for collecting raw data, including metrics, logs, traces and events that is then pushed to a central location like a monitoring or observability platform.

Monitoring is the process of analyzing telemetry data to understand system health, performance, and availability. Monitoring uses telemetry data to create dashboards, alerts, and reports and enables humans to act on insights.

To put it another way, telemetry is like a nervous system—gathering signals from all parts of the body and monitoring is similar to the brain—interpreting those signals and deciding what to do.

Telemetry feeds monitoring. Without telemetry, there's nothing to monitor. Without monitoring, telemetry data is just noise.

What are the different types of telemetry data?

Telemetry data in software and IT generally falls into four main types, often referred to as the "pillars of observability":

Metrics are numerical data points that measure system performance over time. Examples include CPU and memory usage, request latency, error rate, disk I/O and number of active users.

Logs are text-based records of events that occur within a system. Logs could include error messages, debugging output, system events or application traces.

Traces are detailed records of a single request's path through a distributed system. A trace could show how a request moves through microservices, or the duration of each step in a transaction.

Events are discrete, significant occurrences within a system. Examples of events are a deployment started/finished, a feature flag toggle, a security breach detected or a user signed up.

User telemetry data

User telemetry data refers to information collected about how users interact with a software application, service, or digital product. It helps developers and product teams understand user behavior, improve usability, enhance features, and detect issues.

User telemetry data typically covers usage patterns, user interactions, session data, user errors or failures, and environment context.

Network telemetry data

Network telemetry data refers to the real-time or near-real-time collection and transmission of data about the performance, behavior, and state of network infrastructure. This data is crucial for monitoring, troubleshooting, optimizing, and securing networks.

Network telemetry data generally includes performance metrics, traffic data, anomalies and events, and device and interface metrics.

Application infrastructure telemetry data

Application infrastructure telemetry data refers to information collected from the underlying components that support software applications, such as servers, containers, orchestration platforms, databases, and cloud services. This data provides insight into the health, performance, and usage of the infrastructure that applications depend on.

App infrastructure telemetry data can contain compute resources, container and orchestration data, cloud infrastructure metrics, database and storage metrics, and network and load balancer statistics.

Telemetry data in cloud environments

Telemetry data in cloud environments refers to the continuous collection and transmission of data that reflects the behavior, performance, and health of cloud-based systems, services, and applications. It enables visibility, monitoring, and automation in highly dynamic and distributed cloud infrastructures.

Telemetry data in the cloud spans across multiple layers including infrastructure, platform and service, application-level, security, and billing/usage telemetry.

Why is telemetry data useful?

Telemetry data is useful because it provides real-time, actionable insights into the behavior, performance, and health of systems, applications, networks, and users. Without it, organizations would be operating blind—unable to detect problems, understand usage, or make informed decisions.

There are a number of reasons why telemetry data is useful.

First, telemetry improves reliability so teams can detect failures or anomalies before users are impacted and monitor uptime, latency, error rates, and service health.

It’s also possible to use telemetry data for faster troubleshooting and root cause analysis so it’s simple to quickly identify where and why a problem occurred.

Telemetry makes it easier to optimize performance by identifying bottlenecks or inefficient resource usage. Applications can be fine tuned based on actual workload patterns.

For teams wanting better user experience, telemetry data can get them there by showing how users interact with products. And telemetry can even guide UX design and product development.

Telemetry data can help get control of costs and resources by monitoring cloud or infrastructure usage in real time. Teams can easily detect underutilized or over-provisioned resources.

Security and compliance are simplified because telemetry data makes it easier to track access logs, failed login attempts, and unusual behavior.

And finally, telemetry data is at the heart of data-driven decision making, so organizations can leverage actual usage patterns to prioritize features.

Feature Development

Telemetry data helps feature development by giving product and engineering teams evidence-based insights into how users interact with current features, where pain points exist, and what improvements will have the greatest impact. It transforms guesswork into data-driven decision-making.

Identify issues in products

Telemetry data helps identify issues in products by continuously collecting real-time information from software, applications, and infrastructure, then analyzing that data to detect patterns, anomalies, or failures. 

Performance optimization

Telemetry data plays a crucial role in performance optimization by providing detailed, real-time insights into how systems, applications, and user interactions behave under actual conditions. 

Validation

Telemetry data helps with validation by providing real-time, data-driven confirmation that a system, feature, or update is working as intended in production. This is especially valuable in complex, distributed systems where traditional QA might miss edge cases. 

Security improvements

Telemetry data is a powerful tool for improving security because it provides continuous, real-time visibility into system behaviors, user activities, and anomalies. By capturing and analyzing telemetry across applications, infrastructure, and networks, security teams can detect threats earlier, respond faster, and harden systems over time. 

How does telemetry work?

Telemetry works by automatically collecting, transmitting, and analyzing data from software, devices, or systems to provide real-time insights into their behavior, performance, and usage. Here’s a breakdown of how telemetry works step by step:

1. Data Collection

Telemetry starts with instruments embedded in software, systems, or hardware that automatically collect data such as:

• Metrics (e.g., CPU usage, response time, memory consumption)
• Logs (e.g., errors, warnings, events)
• Traces (e.g., step-by-step flow of user requests or system processes)
• Events (e.g., user actions, configuration changes)

These are gathered through telemetry libraries, agents, or SDKs (like OpenTelemetry) added to the code or infrastructure.

2. Data Transmission

Once collected, telemetry data is transmitted (often continuously or in batches) to a centralized location:

• Telemetry pipelines send data over secure protocols.
• Data may be streamed or buffered depending on scale and network conditions.
• Edge devices might store and forward data when offline.

3. Data Aggregation and Storage

Telemetry data is ingested into a backend platform, where it is:

• Normalized (converted to a consistent format)
• Stored in databases optimized for time-series, logs, or event data
• Tagged with metadata (e.g., time, host, user ID, service name)

4. Data Analysis and Correlation

The stored data is then processed to:

• Detect trends, anomalies, and patterns
• Correlate data across services (e.g., link a user error to a backend crash)
• Provide real-time dashboards, alerts, or visualizations

This is where observability comes into play - turning raw telemetry into actionable insight.

5. Visualization, Alerting, and Automation

Telemetry data is displayed through:

• Dashboards (e.g., system health, feature usage, response times)
• Alerts that notify teams of issues (e.g., spike in errors or latency)
• Automated actions, like restarting a service or throttling traffic

6. Feedback Loop

Teams use telemetry insights to:

• Improve performance and reliability
• Optimize user experience
• Fix bugs and vulnerabilities
• Validate updates and deployments

This feedback loop makes telemetry essential for continuous delivery, security, and system resilience.

How Do You Use Telemetry Data?

Using telemetry data effectively means turning raw, real-time system and user data into actionable insights that drive better decisions, faster troubleshooting, and improved products. Telemetry data is typically used to monitor system health, troubleshoot and resolve issues, optimize performance, track user behavior, validate releases and fixes, strengthen security, gain business insights and automate workflows.

How Do You Analyse Telemetry Data?

Analyzing telemetry data involves turning vast streams of raw signals into meaningful, actionable insights about how your systems, applications, and users behave. 

Telemetry data analysis begins with collection and aggregation. Data from various sources including applications, infrastructure, networks and user devices, is aggregated into centralized platforms and observability tools. Then, since the data is often in different formats, it must be normalized and sometimes enriched. Data is standardized into common schemas, and contextual metadata is added (e.g., timestamps, user IDs, environments, trace IDs). Relationships between data types (logs, metrics, traces) are established for better correlation.

Once organized, telemetry data is analyzed both in real time and over time. Real-time analysis detects anomalies, errors, or performance spikes as they occur, and historical analysis tracks trends, usage patterns, and regressions.

After that, telemetry is visualized using dashboards and tools that display time series charts, heatmaps or topology maps, funner or flow diagrams or correlation views.

With that information in hand, teams can use analysis tools to apply rules, baselines, or ML models to trigger alerts when anomalies occur, SLAs or SLOs are violated, and/or unusual user behavior or traffic patterns emerge. The information can also be used to uncover long-term trends in system or service performance, feature adoption and user engagement, and common failure modes or security threats.

It’s also important to remember that insights from telemetry analysis can inform operational improvements, product decisions, and engineering priorities.

How Do You Read Telemetry Data?

Reading telemetry data means interpreting the raw signals coming from software, systems, and users to understand what’s happening—and why. While the format and context may vary (metrics, logs, traces, events), here’s a clear framework to help read and make sense of telemetry data:

Each type of telemetry data - metrics, logs, traces and events - serves a different purpose. Reading telemetry starts with knowing why the process is happening and what is being looked at.

Look for Key Data Fields

Telemetry data often includes:

• Timestamps – When the event or measurement occurred
• Service or component name – Where the data came from
• Severity or status – Info, warning, error
• Message or description – The human-readable insight
• Contextual metadata – User ID, session ID, trace ID, region, environment

These fields help place the data in context.

Use Dashboards or Tools for Visualization

Telemetry is often consumed through observability platforms that present:

• Time-series graphs (metrics over time)
• Log search and filtering (via keywords, fields)
• Trace visualizations (flame graphs, timelines)

Use these tools to filter, zoom in, and correlate data with specific time ranges, users, or components.

Follow Patterns and Outliers

When reading telemetry:

• Look for spikes or dips in metrics (e.g., sudden CPU surge)
• Watch for clusters of errors or warnings in logs
• Use traces to follow slow or failed requests through the system

This helps detect anomalies and understand their causes.

Correlate Across Data Types

True insight often comes from reading multiple telemetry types together:

• A spike in error metrics → Drill into logs for error details → Use traces to find where the request broke

This correlation helps move from “something is wrong” to “here’s what happened and where.”

Read with a Goal in Mind

Interpret telemetry based on an objective:

• Debugging → Focus on logs and traces
• Performance tuning → Watch latency, throughput, resource metrics
• Security review → Look for anomalies, failed authentications, access logs
• User analysis → Check engagement events, feature usage telemetry

Apply Filters and Queries

Most platforms allow users to:

• Search logs with keywords and regular expressions
• Group metrics by label (e.g., host, region)
• Filter traces by latency, user ID, or error

This narrows down the data to what’s relevant and actionable.

What are the challenges of telemetry data?

Telemetry data is powerful, but using it effectively comes with several challenges - especially as systems grow in complexity. 

Data volume

One obvious problem is that modern systems generate massive amounts of telemetry data. Every service, instance, and user can produce logs, metrics, and traces continuously, meaning millions of events per second have to be stored, processed and analyzed.

Data integrity

All that data can also be siloed, or fragmented, meaning it can be hard to correlate issues across systems, difficult to holistically observe, and the context can be lost. Standards are also a problem because teams and vendors can have different ways to name, instrument and format data. The lack of standards can make data integration and analysis inconsistent and hinder automation and interoperability.

Latency and bandwidth issues

Telemetry instrumentation is also complex, requiring developer expertise and ongoing maintenance. In some cases the need for complex instrumentation can lead to over-instrumentation, which can impact performance, or under-instrumentation which can miss key areas of the system. And of course, telemetry can produce too much data so teams may overlook critical signals, suffer from alert fatigue or have too many dashboards.

Data privacy

All that telemetry data is often private, so there is a risk of violating data protection laws (e.g., GDPR, CCPA).

What Kinds Of Companies Use Telemetry Data?

Telemetry data is used by companies across nearly every industry—especially those that rely on digital systems, connected devices, or large-scale infrastructure. Expect to find telemetry data in:

• Technology and software companies
• Cloud and infrastructure providers
• Telecom and network providers
• Financial services and FinTech
• E-Commerce and retail
• Healthcare and medical technology
• Automotive and transportation
• Manufacturing and Industrial IoT (IIoT)
• Gaming and entertainment
• Aerospace and defense
• Energy and utilities

How Mezmo can help you with telemetry data

Mezmo (formerly LogDNA) is a modern observability and telemetry platform designed to help organizations collect, manage, and act on telemetry data efficiently. 

Mezmo’s unified telemetry pipeline reduces complexity and eliminates data silos while offering real-time observability. Advanced log management and analysis makes it faster and easier to troubleshoot and investigate root causes, while smart data routing and enrichment optimizes costs and ensures the right teams see the right data. Stay secure and compliant with built-in features for privacy and compliance standards like GDPR, HIPAA and PCI, all while fitting into your existing toolchain.

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