Observability is a concept in software and computer networks whereby organizations can gain an in-depth understanding of complex systems by analyzing their external outputs and behaviors—typically with the goal of analyzing significant events and uncovering unforeseen issues.
Often, the more powerful and capable a system, the more complex it becomes. Unfortunately, with this increased complexity comes increased unpredictability; failures, performance bottlenecks, bugs, etc. occur, and determining the root cause of these occurrences isn’t always a simple matter. With complex modern systems, not only does the likelihood of unexpected failure increase, but so does the number of possible failure modes. To counter this trend, IT, development, and operations teams began to implement monitoring tools capable of seeing into the systems themselves.
But progress moves forward, and the complexity of today’s systems is outpacing traditional monitoring capabilities. Today, the proven strategy for protecting systems against unknown failures isn’t monitoring; it’s making the system more monitorable, with observability.
Issue identification
Monitoring compares the current state of components against an established baseline. Anything that deviates from that baseline—such as drops in performance, unusual patterns in network traffic, unauthorized user behavior, or other anomalies—is flagged for further investigation.Alerting and notification
When anomalous activity is identified, monitoring systems alert incident response teams, providing them with real-time details about the issue that requires their attention.Historical analysis
Even after issues are resolved, monitoring continues to capture vital information. Historical data gathered during incidents, anomalous events, and times when the systems are operating within normal conditions can be analyzed to provide reliable insights. This empowers teams with the direction they need to chart evolving trends, review and dissect past incidents, and make improvements.
Comprehensive insights
As previously stated, observability offers a holistic understanding of complex systems by examining their external outputs, providing a more comprehensive perspective. In contrast, monitoring focuses on specific predefined metrics and data points, offering a narrower view of system health.Root-cause exploration
Because observability goes beyond monitoring's predefined metrics, it is more effective at discovering the root causes of issues. It emphasizes context-rich insights, enabling teams to understand the relationships between different system components so that they may discover the origins of problems. This differentiates it from monitoring, which tends to focus on known issues without delving as far into their underlying causes.Discovering the unknown
Going hand in hand with root-cause exploration, observability has the capacity to move beyond predefined criteria, allowing it to excel at uncovering unforeseen issues or patterns in the data. Observability makes it possible to ask questions that monitoring solutions cannot. This is helpful in identifying the unknowns that often exist within IT environments, ensuring more proactive performance optimization.Adaptability and optimization
Observability is particularly useful in dynamic IT environments (such as microservices and container-based systems), helping organizations adapt to changes in system architecture and infrastructure by providing insights into their impact on overall system behavior. Furthermore, observability facilitates the identification of inefficiencies and underutilized resources, supporting targeted performance optimization. Monitoring, on the other hand, primarily focuses on tracking predefined metrics and may not be as adaptable or exploratory.
Software is growing more and more complex with each passing day. There is a combination of patterns in infrastructure, like microservices, polyglot persistence, and containers that continue to decompose larger containers into complex, smaller systems.
At the same time, the quantity of products is growing, and there are many platforms and ways to allow organizations to do new, innovative things. Environments are also becoming increasingly complex, and not every organization is addressing the increased number of issues that are arising. Without an observable system, the cause of problems is unknown, and there isn't a standard starting point.
Observability is typically divided into three pillars: logs, metrics, and traces.
This is the record of an event that occurred on a system. Logs are automatically generated, timestamped, and written into a file that cannot be modified. They offer a complete record of events, including metadata about the state of a system and when the event happened. They may be written in plaintext or structured in a specific format.
Metrics are numerical representations of data measured over time. While event logs gather information about specific events, metrics are measured values derived from overall system performance. They usually provide information about application SLIs (Service Level Indicators).
As transactions flow through a system, they interact with various components. Traces capture data about these interactions (user requests, API calls, service invocations, etc.), helping engineers better understand the path the transaction takes. This is invaluable as it can identify possible bottlenecks or other areas of the network in need of improvement.
These three pillars create a solid foundation for observability—working together to provide clear insights into applications, infrastructure, events, and system behavior. But as essential as data collection can be, logs, metrics, and traces alone do not ensure effective observability. To achieve the goals of observability, organizations must be capable of promoting vital business outcomes and enhancing the user experience.
Understanding how observability is distinct from monitoring helps highlight the need for comprehensiveness in tracking and maintaining vital company systems and software. As such, observability exists to fulfill several important objectives, including:
Observability’s primary goal is reliability. An effective IT infrastructure that functions properly and reliably according to customer needs requires a measurement of its performance. Observability tools inform user behavior, system availability, capacity, and network speed to ensure that everything is performing optimally.
Organizations that are subject to compliance must have observability of their computing environments. Full visibility from observability through event logs allows organizations to detect potential intruders, security threats, attempts at brute force, or possible DDoS attacks.
The ability to analyze events yields valuable information about behaviors, and how they are possibly affected by variables like application format, speed, etc. All of this data can be analyzed for actionable insights into network and application optimization in order to generate revenue and attract new customers.
The objectives outlined above may describe what observability is designed to do, but how does that translate into benefits? Here, we identify several clear advantages associated with observability:
Observability excels when facing the unexpected. Unlike traditional monitoring, which primarily focuses on predefined metrics and known issues (called known unknowns), observability is designed to uncover unknown problems (called unknown unknowns). It empowers IT teams to explore beyond the familiar and discover previously unseen issues and patterns within a system. By doing so, it equips organizations to address potential challenges before they escalate into critical problems, ensuring system reliability and resilience.
Observability is extremely valuable during the development phase. By integrating observability into the development pipeline, teams can detect and resolve issues at even the earliest stages. This proactive approach minimizes the likelihood of costly and time-consuming problems arising in production, leading to smoother development cycles and more reliable software.
One of the most significant advantages of observability is its ability to automate remediation—resolving issues without the need for excessive human intervention. This goes beyond merely identifying issues and can be configured to trigger automated responses or remediation actions when certain conditions are met. This reduces manual efforts and helps free up valuable human resources while also ensuring a faster and more precise response to problems, enhancing system reliability and minimizing downtime in the process.
Observability provides a panoramic view of a system's internal state based on its external outputs. This holistic perspective enhances visibility into the system, allowing IT teams to develop a deeper understanding of the relationships and interactions between various components, see exactly what services are running, gain a comprehensive view of application performance, and compare changes between recent deployments. This, in turn, makes it easier to pinpoint the root causes of issues and optimize system performance.
Observability enables the creation of intelligent alerts. These notifications not only inform IT teams that something may be wrong within the system or application; they provide detailed information and deep visibility into what has changed and what that change could indicate, incorporating detailed visualization and intelligent suggestions regarding what actions should be taken. This greatly enhances response teams' capacity to identify and resolve issues quickly, while also helping reduce false positives and ensuring that only the right teams are being alerted (instead of the entire organization).
Observability tools facilitate the creation of customized workflows designed to optimize IT operations. These workflows take into account the entire, end-to-end journey of a request within a system, providing essential contextual data to help streamline investigation and resolution processes. Additionally, by automating routine tasks and responses, observability promotes efficiency and consistency across IT operations.
Developers can use observability data to gain insights into how their code behaves in production. This real-time feedback loop enables them to make informed decisions, identify performance bottlenecks, and fine-tune their applications more effectively, all while eliminating the friction traditionally associated with monitoring and troubleshooting. As a result, development teams can iterate faster, deliver higher-quality software, and respond promptly to user needs.
While observability offers numerous advantages in managing complex systems, it is not without its challenges. Organizations must navigate these obstacles effectively to harness the full potential of observability. Key challenges presented by observability (and the strategies for countering them) include:
Challenge: In complex systems, certain components or dependencies may inadvertently remain invisible due to inadequate instrumentation or gaps in monitoring. Insufficient or incomplete source data can limit the depth of observability, hindering an organization’s capacity to gain comprehensive insights into system behavior.
Counter: Organizations should adopt a proactive approach to facilitate complete coverage. Invest in reliable data collection mechanisms and ensure that all relevant data sources are accessible and well-documented. Leverage data pipelines and ingestion tools to centralize and organize this data effectively. Additionally, regularly review and update instrumentation strategies to identify and rectify blind spots. Consider employing automation solutions (such as ServiceNow Terraform Connector) to ensure that components are correctly instrumented—from the moment they are created through the very end of their lifecycle.
Challenge: Data often comes in diverse formats, making it challenging to consolidate and analyze information efficiently.
Counter: Employ data aggregation and transformation tools to normalize data into a consistent format. Use observability platforms that support various data types and provide a unified view, simplifying analysis and correlation. Set and enforce data formatting standards across the organization.
Challenge: Data fragmentation in different teams or departments can lead to isolated observability efforts, limiting the ability to correlate insights across the organization.
Counter: Foster a culture of collaboration and data sharing. Implement centralized observability platforms that allow cross-functional teams to access and collaborate on observability data. Establish clear data governance policies to ensure data consistency and accessibility.
Challenge: Modern systems generate vast amounts of data rapidly. Handling and analyzing this data in real time can be daunting.
Counter: Invest in scalable observability solutions that can handle high data volumes. Utilize distributed data processing and storage technologies to efficiently manage and analyze complex datasets. Employ intelligent sampling techniques to focus on critical data points while reducing noise.
Challenge: Manual instrumentation and configuration can be time-consuming and error-prone, leading to incomplete or inaccurate observability setups.
Counter: Automate the instrumentation process as much as possible. Use infrastructure as code (IaC) and configuration management tools to ensure consistent and reliable instrumentation across the entire system. Implement version control for instrumentation code to keep track of changes and allow earlier versions to be recovered in the event of an error.
Challenge: Inaccurate or incomplete staging environments can limit the ability to test and validate observability setups effectively.
Counter: Invest in staging environments that closely mirror production systems. Ensure that observability configurations are thoroughly tested and validated in these environments before deployment to production. Automate the deployment and enforce the standards of observability instrumentation to maintain consistency between staging and production.
- Challenge: In complex systems involving multiple teams, identifying the root cause of issues can be challenging—especially when dealing with a high volume of observability data or siloed information.
- Counter: Implement intelligent analysis and correlation tools within your observability platform. Leverage AIOps, machine learning, and anomaly detection to pinpoint unusual behavior and potential issues. Invest in training and knowledge sharing to enhance troubleshooting skills among all relevant teams.
Observability is a versatile concept that offers a range of valuable use cases across IT and business operations. Among the essential uses of observability, five stand out most prominently:
With full end-to-end observability, organizations can swiftly identify and resolve performance issues, even in complex cloud-native and microservices environments. Advanced observability solutions go beyond detection and offer automation capabilities, improving efficiency and fostering innovation among Ops and Apps teams. This empowers teams to maintain a high-performing application ecosystem.
Observability makes it possible to combine business data with comprehensive application analytics and performance metrics. This fusion allows for a real-time understanding of the business impact of software operations. It aids in optimizing conversion rates and tracking adherence to internal and external service level agreements (SLAs). Business analytics through observability helps organizations make data-driven decisions that enhance their competitive edge.
DevSecOps and site reliability engineering (SRE) teams may leverage observability data throughout the software delivery lifecycle. This helps them build more secure, resilient, and reliable applications. By incorporating observability from the outset, organizations ensure that their software is secure and continuously optimized for performance and reliability.
Observability enables organizations to proactively detect and resolve issues before end-users ever notice them—addressing issues and making improvements ahead of user requests to enhance customer satisfaction and loyalty.
Observability extends to infrastructure and operations (I&O) teams, offering enhanced context for infrastructure monitoring. I&O teams can improve application uptime and performance, reduce the time required to pinpoint and resolve issues, detect cloud latency problems, optimize cloud resource utilization, and enhance the administration of Kubernetes environments and modern cloud architectures.
Effectively implementing observability in an organization demands a strategic approach supported by tried, tested, and true practices. These best practices will help ensure maximum returns from observability investments:
Integrating monitoring and measurement tools into software applications from the very beginning of their development ensures that applications are designed with the capability to collect data on their performance, usage, and behavior by default, rather than adding such capabilities as an afterthought or only in response to issues. Application instrumentation promotes visibility—a prerequisite to measuring data.
Observability is not merely a tool or technology; it's a cultural shift that fosters collaboration and transparency between development and operations teams. To fully leverage observability, organizations must embrace a culture of observability where teams work together seamlessly throughout the software delivery lifecycle.
This cultural alignment promotes shared responsibilities for observability—from designing applications with observability in mind to monitoring and troubleshooting in production. It encourages the free flow of information and insights, breaking down silos and accelerating problem resolution. Committing to an observability culture ensures that observability becomes an integral part of the development and operational processes, driving efficiency and innovation.
Observability generates massive amounts of data, but that data is useless unless it can be translated into insights that can be acted upon with confidence. To do this, organizations need to establish meaningful reporting practices. This involves setting clear objectives for observability, identifying key performance indicators, and designing dashboards and reports that focus on these metrics. The goal is to provide relevant, real-time information to stakeholders, enabling them to make informed decisions.
Meaningful reporting not only aids in detecting issues but also helps organizations identify trends, optimize performance, and align their IT operations with business goals. This ensures that observability data is not just collected but used effectively to drive improvements.
Observability shines when it goes beyond detection and enables automated responses to issues. Organizations should integrate observability with automated remediation systems to enhance incident response and minimize downtime.
When an observability tool detects anomalies or issues, it should trigger predefined remediation actions automatically. These actions can range from scaling resources to rolling back deployments or even notifying relevant teams. By automating remediation, organizations reduce the mean time to resolution (MTTR), enhance system reliability, and free up IT teams to focus on higher-value tasks. However, it's essential to design and test these automated responses carefully to ensure they align with business objectives and do not introduce new risks.
While adopting observability is a crucial step, ensuring its effectiveness and efficiency requires careful planning and execution. Here are key practices that can help organizations guarantee successful observability:
The first step in ensuring observability is to set clear and relevant goals. Understand what aspects of the systems and applications need to be observed and why. Define the specific benefits that observability should deliver to the business. By establishing well-defined objectives, teams provide a clear direction for the observability initiative.
Optimizing data for observability involves adding context and making it more conducive to analysis. Review the data sources and consider enhancing them to better support observability goals. This might involve adding additional details to logs, aggregating data for trend analysis, or optimizing data collection methods. By optimizing data for observability, organizations can enhance their ability to extract meaningful insights and detect trends that might otherwise remain hidden.
Ensure that the results of the observability efforts are shared with the right recipients at the right time. This involves configuring reporting, alerting, and dashboards to provide meaningful and actionable outputs. Instead of relying on static alerting thresholds, consider configuring alerts based on time parameters that account for normal fluctuations. Also, direct observability outputs to appropriate channels and individuals. Effective sharing of results ensures that observability efforts lead to informed decision-making and direct problem resolution.
The three pillars of observability help bring together data sources that would otherwise be difficult to draw conclusions from alone. This is because, at its heart, observability depends on two things:
High-context telemetry data with a great deal of runtime context.
The ability to interact with that data iteratively to glean new insights without deploying code.
When these two factors are in place, businesses have the raw resources they need to improve systems and application observability.
To achieve effective observability in modern IT environments, organizations rely on a suite of technologies and tools designed to provide insights into system behavior, performance, and security. Essential technologies and tools commonly used in observability include:
APM tools help monitor the performance of applications by tracking metrics and identifying performance bottlenecks, providing insights into application behavior, resource utilization, and response times. Perhaps most essentially, APM solutions provide important data relevant to user experience. They also help discover dependencies and track and measure transactional data.
Distributed tracing enables the tracking of requests as they traverse various services and components in a distributed system. Because a single request may span many different services, identifying problem areas means fully understanding and tracking the flow of requests through microservices. Typically, this information is presented in a visual format, allowing teams to see the interactions at a glaze and quickly discover where issues may be occurring.
RUM (also called end-user monitoring, or EUM) focuses on gathering data about end-user interactions with applications, helping organizations better understand the user experience. By analyzing various UX-relevant metrics, such as access times and the number and frequency of errors, teams gain greater insight into the elements that directly impact their users’ satisfaction.
eBPF is a technology that allows the dynamic tracing and monitoring of the Linux kernel. eBPF empowers organizations to collect observability metrics swiftly and efficiently, significantly outpacing traditional technologies. It also allows application developers to seamlessly enhance the observability of their systems by running eBPF programs within the kernel, ultimately leading to enhanced insights and more effective observability practices in Linux-based environments.
Log management tools collect, store, and analyze log data from various sources, (including applications, infrastructure, and security systems) to create a comprehensive and ongoing record of events. These tools assist organizations in tracking system events, troubleshooting issues, and ensuring compliance with established regulations and policies. The best log management technologies ensure that captured data is presented in a format structured to the organization's needs. They may also include customizable alerts and notifications.
OTel is a pivotal open-source project designed to streamline observability practices. It provides a standardized methodology for collecting comprehensive observability data, encompassing traces, metrics, and logs from various applications and services. One of its significant advantages is the ability to ensure consistency in instrumentation and data collection across a wide spectrum of environments, including cloud-native, hybrid, and on-premises setups. OpenTelemetry simplifies the process of integrating observability into applications, offering developers a unified framework for generating valuable insights into system behavior and performance.
XDR is a cybersecurity technology that extends beyond traditional endpoint detection and response (EDR). By consolidating data from multiple security solutions, including endpoints, networks, and cloud environments, XDR provides security teams with a more holistic and integrated view of threats and incidents. This comprehensive perspective allows for quicker threat detection and response, as security analysts can correlate data from various sources to identify and mitigate security risks effectively. XDR is a crucial component of observability in security operations, enhancing the ability to monitor, analyze, and respond to security events across the entirety of the IT landscape.
Zero Trust is a security framework that challenges the traditional approach of trusting entities within a network perimeter. It emphasizes continuous verification and strict access controls, making it a critical component of observability in security operations. Zero Trust assumes that no entity, whether inside or outside the network, can be inherently trusted, and thus mandates rigorous authentication and authorization mechanisms, ensuring that users and devices are continually validated before accessing resources. This security approach aligns closely with observability, as it demands comprehensive visibility into user and device activities, network traffic, and access patterns.
As systems grow in complexity, the likelihood of unexpected failures, performance bottlenecks, and elusive bugs increases. The need to maintain constant, reliable visibility and pinpoint the root causes of these occurrences is more critical than ever. Observability is the solution, providing organizations with the means to gain valuable insights into the inner workings of intricate systems—well beyond what is capable with traditional monitoring. That said, effective observability takes more than commitment; it requires the right tools, resources, and support. This is why successful businesses trust ServiceNow Cloud Observability.
ServiceNow Cloud Observability is designed to equip organizations with the capabilities they need to thrive in the complex world of IT operations. Gain crucial visibility into application dependencies to anticipate issues before they occur. Reduce resolution times by identifying the root causes of spikes and other issues by analyzing logs, metrics, and traces. Break down organizational silos and foster effective communication across teams. And through it all, apply built-in features—notebooks, OpenTelemetry, The ServiceNow Correlation Engine, unified query language (UQL), cloud-native logging, intelligent alerts, unified dashboards, and service mapping—to ensure that your observability journey aligns with your evolving business needs.
See how the right approach to observability can give you the insights your business depends on. Try ServiceNow Cloud Observability today!