Understanding system performance is critical for gaining a competitive advantage. Telemetry provides deeper insights into the system, helping business owners make better decisions.
This article take a comprehensive look at the topic of telemetry. We’ll look at its functionality and telemetry types. We’ll also look at all the things telemetry data can help you with — plus the challenges companies with telemetry systems might face.
What is telemetry?
Let’s start with a definition: Telemetry collects and analyzes data from remote sources to gain insights about a system’s performance — so you can pinpoint areas to improve.
Widely used in many industries, telemetry supports and can be critical in software and IT, agriculture, healthcare, weather forecasting and various research fields. A particularly important example: telemetry monitors critical medical patient metrics, such as blood pressure and heart rate.
Telemetry in IT
In the technology and software industries, which is the focus of this article, telemetry is the process that automatically collects data from various deployments of software products. It helps you get deeper insights about your product so that you can improve the product with better decision-making. F
or example, many software systems use telemetry to track how well users engage with your products. In this example, you might track metrics like:
- Page views
- User journey inside the application
- Events and errors
- User devices and operating systems
Monitoring vs. telemetry
The terms monitoring and telemetry are often used interchangeably. The processes do overlap but they have slight differences:
- Monitoring has a narrower scope: its main objective is to detect potential issues and take proper action to avoid any customer incidents or escalations. Monitoring typically measures metrics such as application resource usage and network activity.
- Telemetry collects and analyses data for a wide range of purposes, from troubleshooting issues to understanding user behavior and system performance. Additionally, it uses broader performance metrics than monitoring.
We can say, therefore, monitoring is a subset of telemetry. It provides deeper monitoring capabilities and a comprehensive understanding of the system.
Enterprises collect and monitor different types of Telemetry data depending on their requirements.
Telemetry data from IT infrastructures
Examples for telemetry in IT infrastructure include transaction and error rates, response times, CPU and memory usage, disk I/O, and network throughput.
User telemetry data
Collecting data when users engage with product features. Examples include when the user clicks on a button, logs into the system, views a specific page, or encounters a specific error page.
Network telemetry data
Specific metrics like bandwidth capacity monitoring, specific network ports, and storage solutions are used for networks. Additionally, network telemetry data can include the health of network devices, such as CPU and memory utilization of routers or switches, device uptime, and temperature.
Application infrastructure telemetry data
Applications generate various telemetry data that users can monitor and collect. Examples include latency, transactions per second, database access, database queries, errors generated in the application, and application deployment-specific activities such as deployment and deployment topology.
Furthermore, stakeholders in an application can get insights such as the most used operating systems, browser type/version, and device details.
Telemetry data in cloud environments
Enterprises can also measure cloud-specific telemetry data such as routing decisions, configuration changes, security group modifications, and data related to cloud usage.
Uses of telemetry
Telemetry can empower you do to all sorts of things, as long as you know how to do that. Here’s some ideas.
Prioritize feature development
Telemetry data can reveal the most engaged and least-used features by users. That information will help product teams prioritize feature enhancements — and opt out of developing features that users are not interested in.
Identify issues in the product
Telemetry data helps enterprises reveal areas or features where users frequently encounter errors or slowdowns in their software or platform. These revelations allow companies to focus on problem areas and fix them before they become serious issues.
Telemetry data can indicate performance bottlenecks of the product, such as slow-loading web pages and components. Using that data, developers can improve areas to enhance performance.
Validating changes or enhancements
When a certain feature is changed or enhanced with additional functionality, telemetry data helps validate if those changes lead to:
- Better user engagement
- Reduced error rates
- Increased feature usage
Telemetry data can reveal suspicious activities and usage patterns. Security teams can understand security incidents and possible causes by examining past telemetry data. Plus, telemetry can easily reveal outdated software versions so that security patches can be applied promptly.
How telemetry works: getting value from telemetry data
Getting value from your telemetry data is not as simple as collecting data. You do have to do some work—I describe in five steps how to get value from your telemetry data.
Step 1. Identify telemetry requirements.
Initially, identify your telemetry monitoring requirements and the approach for data collection. What question needs to be answered? What questions are you trying to get information for? Additionally, you’ll want to determine:
- The metrics you’ll use
- The requirements for pushing data
For example, defining the schema of the telemetry messages of the target system. The common message formats must be defined if multiple systems are involved.
Step 2. Set up telemetry instrumentation.
In this step, the target system that sends data to the remote system integrates with telemetry. For example, for user or application Telemetry, the application may need to push data according to the defined schema at specific events.
Additionally, the configurations will be set if the system needs to send data through a queue system. Data should be validated properly. Avoid or protect sensitive information, according to the privacy and security policies of your company.
Step 3. Transmit the telemetry.
The third step is transmitting the required telemetry data from the target system to the remote storage in real time or at specified intervals. The transmission can use various protocols and methods based on the system and the data types. For example, specific message queues can be used to send the data to the receiver end.
Furthermore, the target systems may be required to cater to specific needs according to the telemetry setup. For example, using a data sampling method to control the data volume and adjusting the transmission rate.
Step 4. Store the telemetry data.
Telemetry data is accumulated in a central database or data lake. The storage system should be chosen to facilitate a large amount of data, according to the data volumes. You’ll also want it to facilitate real-time and historical analysis, helping teams identify trends, anomalies, or patterns over time
Step 5. Analyze & visualize the data into information.
Once the data is collected in the telemetry storage, it is analyzed using various tools. This data can reveal information that will help identify and fix bugs, improve the user experience, and make informed decisions about feature development.
Visualizing the data and information specific to stakeholder needs’ (no more, no less) so that stakeholders can identify the trends and patterns easily.
Challenges of telemetry
And now we come to hard part: the challenges inherent in telemetry data. Telemetry helps answer critical questions to enhance the performance of the system. However, it also poses many challenges that companies must address to reap its benefits effectively.
Data privacy concerns
Some companies may send sensitive user information such as usernames and IP addresses, which are critical for getting valuable insights. However, they can raise serious privacy concerns.
Companies need to comply with data privacy regulations such as GDPR and CCPA and ensure that no personal or sensitive information. Some users might turn off telemetry features for privacy concerns, leading to incomplete or biased data.
Telemetry can generate a large volume of data in the telemetry processing system. The data can be huge, especially if it integrates with multiple products or systems or data generated at peak usage times. Storing such data and scaling to increasing data volumes can be challenging and costly. Therefore, scalable, reliable, and cost-effective solutions must be employed.
Latency & bandwidth issues
Network latency can affect real-time data analysis. Additionally, transmitting large amounts of telemetry data can consume significant bandwidth and increase operational costs.
Data integrity & interoperability issues
If the telemetry system integrates with multiple clients or systems, data can be inconsistent due to device malfunctions, software bugs, or transmission errors. These integrity issues can lead to inaccurate data. There can also be different systems and technology stacks, making it a challenge to ensure these systems can communicate and share data seamlessly with the telemetry system.
Challenges in data analysis
Data analysis with a large volume of data can be time-consuming and challenging. Hence, efficient tools and techniques are required to process, analyze, and extract meaningful insights from this data.
Nowadays, telemetry systems are vital for any business to improve its performance and offer the best user experience. As we discussed in this article, telemetry provides deeper insights into the systems than typical monitoring tasks. Current Telemetry systems track different types of data.
Telemetry has several advantages, such as prioritizing features, improving security, and validating the enhancements. As the article describes, Telemetry also has many challenges that companies must address to get the most from it.
What is Splunk?
This posting does not necessarily represent Splunk's position, strategies or opinion.