What are Metrics & What are the Most Important Metrics to Monitor?

What does it mean to monitor metrics?

When you monitor metrics, you collect and analyze numerical data from your technology stack and business processes. Through this practice, you can run historical analysis to optimize and make data-driven decisions about the future while also responding to issues in real time.

The importance of monitoring metrics

Metrics provide comprehensive context and insight into what is happening in your environment. They help identify anomalies, outliers, and trends so that you can act on issues before the issues impact your business. By monitoring metrics, you can:

1. Meet and exceed performance goals: Metrics are a quantifiable and objective way of measuring performance over time. They are useful for establishing benchmarks and targets and for measuring progress against those targets. Because metrics can be aggregated and filtered easily, you can summarize data to understand overall performance or focus on only a subset of data. By monitoring metrics, you can also spot spikes and dips in performance in real time so that you can proactively take action. You can apply machine learning algorithms to metrics to forecast whether you’re on track to hit targets.

2. Make data-driven decisions: By analyzing historical metrics, you can identify long-term trends and patterns to make decisions based on fact rather than intuition. You can also correlate metrics and contextualize them with events to help you replicate successful strategies and avoid past mistakes.

3. Enhance alignment and accountability: Metrics are the basis for a consistent set of key performance indicators that define success in an organization. They facilitate a shared understanding of business priorities and provide tangible objectives to pursue. When teams have a shared view of metrics, they can understand how their work impacts the work of others. This understanding improves collaboration and coordination. Metrics also provide a consistent and clear way of reporting results, which enhances accountability and transparency.

How does an organization monitor metrics?

To monitor metrics, you begin by collecting data from a wide variety of cloud and on-premises components. Ideally, you should gather all metrics into a single platform that has access controls. This way, you can readily access and analyze the metrics while protecting the data. To streamline the collection of metrics and enable rapid onboarding, monitoring tools often offer integrations. These integrations can be installed with an agent. Alternatively, you can set up authentication-based integrations in which an API uses a crawler to pull in metrics after credentials are provided. You can also derive metrics from other types of telemetry, such as logs and traces.

From raw data to actionable insights

To achieve an effective monitoring strategy, you should transform raw metrics into actionable visualizations, alerts, and service level objectives (SLOs). Visualizations translate datapoints into a consumable format for analysis and interpretation by graphically representing them. Because metrics are stored as datapoints with values and timestamps, they’re typically represented in a timeseries graph to depict change over time. Metrics are also commonly visualized as pie charts, scatter plots, or tree maps.

Metrics often form the basis for monitoring software because they’re quantitative. You can easily use them to distinguish between typical behavior and anomalous behavior. The monitoring software can trigger alerts when the overall value or change in the value of the metric passes a threshold. Some monitoring platforms use machine learning algorithms to trigger alerts for anomalies and outliers. You can also use metrics to track performance SLOs by calculating the proportion of anomalous events to total events or by measuring the uptime of monitors.

What are the top metrics to monitor?

Metrics can capture information about almost anything, including infrastructure, applications, digital experience, software delivery, and business outcomes. You need to identify the right metrics to monitor so that you can gain insights into various aspects of your operations and optimize performance.

Infrastructure metrics cover the health and performance of backend components, such as servers, virtual machines, containers, and databases. By collecting infrastructure metrics, you can troubleshoot performance issues, optimize infrastructure use, and forecast requirements. Common infrastructure metrics include:

1. CPU usage: Percentage of processing capacity that a host is using to handle computing costs
2. Memory usage: Number of objects or bytes that a host has in short-term storage to run a program
3. Storage usage: Amount of disk space that a host is using to store files, images, and other content

Application metrics monitor the health and performance of applications and services. These metrics are useful for preventing downtime and improving performance and reliability. Common application metrics include:

1. Rate: Number of requests per second
2. Errors: Number of failed requests
3. Duration: Amount of time that requests take

Digital experience metrics focus on frontend performance to provide insight into the end-user experience. Through monitoring these metrics, you can optimize user experiences by tracking the performance of browser and mobile applications, troubleshooting errors, and understanding who is interacting with your applications and how. Common digital experience metrics include:

1. Largest contentful paint: Loading time when the largest Document Object Model in the viewport is rendered
2. First input delay: Time elapsed between a user’s first interaction with a page and the browser’s response
3. Cumulative layout shift: Sum of all layout shift scores for unexpected layout shifts (when a visible element changes its position between rendered sessions) that might cause visual instability

Software delivery metrics measure the velocity and stability of software development. These metrics are useful for improving the speed and quality of software delivery. Common software delivery metrics include:

1. Deployment frequency: How often an organization successfully releases to production
2. Lead time for changes: Amount of time it takes for a commit to get into production
3. Change failure rate: Percentage of deployments that cause a failure in production
4. Time to restore service: Amount of time it takes to recover from a failure in production

Business metrics vary by industry but are typically related to key business processes and outcomes. You can track business metrics alongside technology metrics to help you understand and report the downstream impact of technology decisions. As a result, you get a more comprehensive view of performance. Common business metrics include:

1. Revenue: Monetary value of sales
2. Transactions: Number of sales
3. Active users: Number of individuals who recently interacted with a product
4. Conversion rate: Percentage of users who completed a desired outcome

As you incorporate metrics monitoring, you will probably find that some metrics are more valuable to you than other metrics are. In this case, you will want to manage which metrics you collect because cost is often associated with volume. If you want to reduce the volume of your metrics, start by trimming any metrics that aren’t being used.

What are the implementation challenges of monitoring metrics?

The implementation process to monitor metrics can entail the following challenges:

1. Limited and lagging visibility: To collect metrics, you need to set up integrations with your existing technologies. This integration can be time-consuming to build and maintain if you do it internally. Monitoring each of these technologies also requires specialized knowledge because you must decide which metrics are important. This required knowledge can slow down onboarding and create a lag between adopting and monitoring new technologies. Complex technology stacks can also cause different teams to use different tools to monitor each component, which can lead to inconsistent and siloed views. This approach limits visibility, making it difficult to properly correlate metrics with other types of telemetry and troubleshoot incidents with context.

2. Cost implications: As the amount of collected data increases, storage costs also increase. This fact can force you to make trade-offs between visibility and cost. You might opt for short retention periods for data, even though this decision can lead to a loss of valuable historical context. Alternatively, you might compromise on granularity or opt to pre-aggregate data, but this decision decreases how precisely and accurately you can respond to incidents. You might also choose a more aggressive strategy by reducing the number of metrics that you monitor. If you make these decisions without knowing if and how the metrics are actively used, you might make changes that disrupt your monitoring.

3. Ineffective and inefficient monitoring: Setting up effective monitors can be a laborious and time-consuming process because it requires knowledge of what specific criteria are worth alerting on. If false alarms or redundant alerts are created, they can add noise rather than value, contributing to alert fatigue. If a monitor doesn’t properly alert on signals of problems, it can delay detection and resolution times. Uneven adoption across an organization and the learning curve required to figure out how to use a tool’s query language and build dashboards also can exacerbate monitoring challenges.

Industry shifts and trends in monitoring metrics

Rapid growth in the amount of data and variety of data sources has created complexity in how metrics are collected and analyzed. Nearly everything is capable of emitting metrics. Infrastructure, applications, microservices, databases, network devices, and more can produce billions of metrics a minute for a single organization. With this proliferation of data sources, more organizations are using open source solutions such as Prometheus and OpenTelemetry to standardize telemetry. These solutions provide a vendor-agnostic approach to collecting metrics. Metrics can also be captured in real time now rather than processed in batches.

These advancements coincide with cultural shifts in technology organizations. The adoption of DevSecOps has increased collaboration between development, security, and operations teams and has increased the need to unify monitoring for shared visibility. Additionally, the role of technology organizations is changing in key business strategies and initiatives. Technology organizations are now strategic business enablers, and CIOs and CTOs now have a larger influence within organizations. As a result of these technological and cultural changes, organizations must now consider and monitor a wider range and larger volume of metrics. This scale of data has led organizations to adopt AI and automation to efficiently analyze and act on the data.

What to look for in a metrics monitoring solution

Choosing the right metrics monitoring solution is essential to facilitate comprehensive visibility and efficient incident management. You should look for the following characteristics to find a solution that aligns with your specific needs and operational goals:

1. A unified platform: A unified platform allows you to correlate metrics with each other and related telemetry such as traces, logs, and events. If you start with a comprehensive platform that covers all your current and future needs, you won’t have to integrate an assortment of tools later. By centralizing monitoring, you can better understand relationships in your data and identify downstream and upstream impacts. A unified platform also provides a single source of truth for teams across your organization, so they can operate off the same information and share context.

2. Rapid onboarding and activation: Rapid onboarding enables you to gain visibility into your environment quickly. Instead of you needing to know what dashboards or alerts to create, a metrics monitoring tool should provide a curated catalog of them for you. Templated dashboards and alerts are based on best practices and subject matter expertise, and you can use that collective knowledge and experience. Another key consideration is how intuitive the product is to use. This factor influences how fast you can get value from the product and how many people in your organization can self-serve information.

3. Superior granularity and retention: Granular and long-lived data improves your ability to solve issues. Fine-grained data provides more detailed insight, allowing you to precisely detect and diagnose issues in real time. Meanwhile, long retention periods are useful for identifying long-term trends and using past data to make better decisions about the future. With more granular and historical data, you can improve accuracy by factoring more datapoints into your analysis. This approach is especially useful for machine learning models because it gives the models a larger set of data from which to extrapolate.

4. Tags: Tags provide a flexible way to add metadata and important context to metrics. You can use tags to aggregate, filter, and correlate the metrics. These actions are useful to help you go from a summarized overview of performance to detailed views of specific applications, services, and regions. Tags also provide a consistent framework for tying together metrics and other telemetry by indicating how they’re related to each other.

5. Alerts: Alerts notify you when critical changes occur. You can manually set up an alert by specifying criteria that trigger the alert when a metric passes a threshold. You can also set up machine learning-based monitors to identify anomalies and outliers or make forecasts. To improve the actionability of these alerts, you can assign them to specific individuals or teams, include necessary details, and integrate them with notification channels. As you use alerts, use the noise-to-signal ratio to refine them and eliminate false positives and false negatives.

6. Visualizations: Visualizations graphically represent metrics so that you can interpret the metrics. A monitoring tool should give you templated dashboards to deliver instant visibility. When you need to create your own dashboard or customize an existing dashboard, drag-and-drop widgets can improve ease of use and provide self-service analytics. Additionally, a monitoring tool that includes a large library of visualization types and built-in functions for transforming your data can help you best represent your data.

7. Cost-control tools: The ability to manage your metrics volume is important for creating an effective and cost-efficient monitoring strategy. For this capability, you need visibility into metrics usage and attribution to hold teams accountable and enable them to act proactively. To make informed optimization decisions, you also need insight into how the metrics are used so that you don’t inadvertently stop monitoring useful metrics.

Learn more

Organizations that effectively monitor metrics gain valuable insights and powerful tools for optimizing performance across various aspects of their operations. Despite the challenges in implementation, a robust monitoring solution can significantly enhance visibility and decision-making. Datadog offers a unified metrics monitoring platform that brings together Infrastructure Monitoring, Application Performance Monitoring, and more under a single plane of glass with advanced features to help you effectively track and analyze your data. You can use the resulting insights to establish and meet goals, make optimal decisions, and respond to issues in real time.