As a product manager, you’re responsible for understanding how well your product performs at a given moment. Sometimes you can look towards KPIs, but often performance tracking can lead to an information overload. Shifting through multiple systems and metrics requires time and energy that could be spent somewhere else.
If any of this sounds familiar, you should consider observability. Observability helps remove some of the headaches associated with gauging performance by guiding you towards actionable insights.
In this article, you will learn what observability is, its associated benefits, and the best practices for implementing it within your product team.
Table of contents
What is observability?
Observability provides insights into the performance of your product based on the logs, traces, and metrics generated by your systems. Observability originated from the modern control theory that led to the implementation of self regulating machines. Gradually this was applied to the IT systems to monitor performance, which gave rise to the term observability.
Enterprises adopted cloud-native applications and services across the last few years. The increased complexity of integrated systems makes it difficult to track problems that may occur. With complex and distributed systems, teams need to analyze and troubleshoot applications to diagnose the root cause of errors.
Without observability in place, the conventional monitoring systems can only trace the known unknowns. Observability, on the other hand, provides the ability to understand patterns that haven’t occurred yet. Although the concept of observability is relatively new, organizations are rushing to adopt its immense benefits.
4 benefits of observability
Observability helps you understand and gain insights into internal states and behaviors of your systems. There are four major benefits:
Observability allows you to detect potential issues before they impact a system. You can set up an intelligent alerting system based on performance data. This can help in preventing any potential issues while maintaining ideal system performance.
From observability, your developers gain knowledge on the system’s behavior that allows them to understand who owns each particular component. Increased visibility into the system helps identify root causes quickly. By looking at the logs, metrics, and traces, it’s easier to pinpoint the problematic areas and avoid any unnecessary time spent on diagnosing the issue.
Optimized end user experience
You can use observability to identify issues before they even occur. This prevents any impact on the end users. You can also identify any potential improvements to continuously shift the product towards the needs of your users.
Observability leads to easier root cause analyses and diagnoses of issues. Without observability, developers need to spend time finding out who’s responsible for a particular service or application before they can fix a bug. Instead, the engineering team will have this information readily available.
By adopting observability, organizations can launch products faster, improve products quicker, and also increase their revenue by providing a stable software environment.
Why is observability relevant for product managers?
Observability is helpful for your role as a PM in four key ways:
Feature adoption and usage
Observability allows for valuable insights regarding feature adoption, usage, and customer feedback. Logs, metrics, and traces from observability help you analyze the adoption rate, the impact of new features, and user behavior. You can evaluate the success of your product features by tracking key performance indicators (KPIs).
By analyzing the real time data provided from observability, you can validate your hypothesis and receive insights into users’ interaction with your product. You will see an overview of the bottlenecks in the application and the pain point of users. The identified pain points or existing issues can then be prioritized on the roadmap based on the data from observability.
You can monitor the data post release to identify any production issues that might occur. By analyzing the logs, error rates, and impacted users, you can prioritize issues. User behavior and logs data availability makes it easier for you to collaborate with development and operations teams to resolve issues quickly.
Resource utilization like memory and network usage is critical for capacity planning. With this knowledge, you can scale your product to meet growing user demands. Analyzing observability data helps in identifying resource constraints and making data-driven decisions for infrastructure and capacity.
Observability empowers product managers to monitor product success, optimize user experience by introducing enhancements, prioritize production issues, and plan for scalability.
Best practices for implementing observability
To take advantage of observability, follow these best practices:
- Identify observability goals — Define the goals and objectives to achieve using observability. The goals and the objectives defined must be aligned with the key stakeholders in the organization
- Formulate instrumentation strategy — Analyze and figure out key areas of your system that need constant monitoring. Next, identify the instrumentation techniques like metrics, libraries, disturbed tracing and logging frameworks to collect relevant data
- Collect the data — A centralized system to collect and store observability data ensures easy access and analysis. Dedicated observability platforms can store the data and generate the required reports
- Define metrics — Define meaningful metrics and KPIs aligned with the observability goals identified earlier. Ensure the metrics are reviewed regularly to align with observability goals
- Structure your logs — Structured logs are easier to interpret, making it easier to search, filter, and analyze the log data. A consistent logging structure throughout the system is essential for consistent interpretation
- Distributed tracing — Distributed tracing can help visualize dependencies and bottlenecks in the system by capturing requests as they go through various components. To facilitate root cause analysis, it’s crucial to include relevant contextual data in tracing information
- Detect anomalies — Set the triggers based on the minimum level of performance expected. These triggers should notify the required teams to take necessary action immediately
- Create dashboards — Customize the dashboards according to the needs of the stakeholders. Accessible dashboards help you monitor and diagnose issues efficiently
Challenges and considerations
Observability comes with its own set of challenges for implementation. Let us take a look at the key challenges:
- Data privacy and security — Data collected with observability can often contain sensitive user information and other system configuration related information. It’s crucial to consider privacy and security measures like access controls, data encryption, and regulations
- Meaningful insights — Incorporating contextual information such as metadata can help to figure out the root cause of issues. This will allow you to derive meaningful insights from the collected data
- Data storage and aggregation — Aggregating data from different sources presents a challenge for visibility. Tools that can store large volumes of data and aggregate the data, correlate, and generate visual dashboards to identify system behavior are crucial
The following types of organizations use observability to improve their end user experience by enhancing the product capabilities:
Internet of things (IoT)
Observability is critical in IoT to monitor and manage connected devices. Analyzing the data in IoT devices enables you to manage your devices while providing insights to efficiently troubleshoot and keep the business running. For example, smart home systems use observability to monitor energy consumption, detect any anomalies, and receive alerts when needed.
Cloud platforms such as AWS, Microsoft Azure, and Google cloud platform monitor and analyze the performance of infrastructure components. These cloud platform tools provide observability features to identify and resolve performance issues or resource constraints.
Observability helps in monitoring and analyzing trading systems, as well as transaction processing and risk management. Through real time monitoring of market data and transaction flows, observability can help detect anomalies or fraudulent activities to ensure regulatory compliance.
Observability plays an important role in monitoring and understanding the behavior of distributed systems. Observability tools collect the data across distributed systems to analyze metrics, logs, and traces to provide meaningful insights into systems behavior. By identifying bottlenecks, troubleshooting issues, and sending alerts before a potential issue happens, observability can considerably reduce downtime and increase revenue for the businesses.
There are many different tools that you can implement within your team to capture the benefits of observability. The following are some of the best:
Dynatrace is a SaaS enterprise tool that offers an AI engine called Davis for root cause analysis and anomaly detection. It provides Infrastructure monitoring, log management, and application performance monitoring (APM). Its application monitoring allows it to monitor the performance and security of cloud applications.
Datadog is an observability tool for cloud-scale applications. It helps in monitoring infrastructure, applications, databases, network performance, as well as a full DevOps stack. Datadog provides an extended toolkit of security tools along with metrics monitoring.
Prometheus is an open source monitoring system that allows the storage, querying and capture metrics. It has a wide range of client libraries, integrations with efficient time series databases and modern alerting approach.
AppDynamics provides monitoring with visibility into all layers of application in real time. AppDynamics can monitor infrastructure, applications, databases and business performance. Additionally, it can also detect security vulnerabilities.
LogRocket monitors and understands user experience by capturing client side logs, performance data, and other information directly from the browser. By doing so, you can identify issues and enhance user experience and application performance.
LogRocket also collects client side logs and code level details along with high-fidelity session replay. The Galileo machine learning module automatically identifies, aggregates, and analyzes issues providing alerts for severe issues.
Observability solutions can help make your systems and applications observable, troubleshoot issues, detect critical issues before occurring, and generate business value. By keeping a tab on the insights provided by the observability tools, product managers can assign priority to issues that arise. The data can also help you make data driven decisions for determining further enhancements for your product.
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