Observability is a hot term in the industry, but don’t let it fool you: having visibility into your organization's apps and services only gives you partial clarity into a system’s overall performance. To get a full understanding of your monitoring data, you need to apply contextual intelligence.
Context matters in observability just as it matters in daily human interaction. A wink or a peace sign can mean different things depending on a number of variables: location, time of day and environment, just to name a few. Context works similarly in IT systems.
While observability uses telemetry data to garner insights into the system’s internal state, contextual intelligence provides valuable background information. It transcends traditional rules-based IT systems by taking into account the dynamic environment in which the data was collected. How was the app performing yesterday? What was the performance level while doing something different? Is the server unique?
By enriching your data with context, your
Unearth deeper meaning with metadata
While legacy monitoring systems offer DevOps and SRE teams rigid time-series data, next-generation observability platforms provide continuous context through metadata. Metadata gives teams the ability to find meaning behind observability data and get a clear and accurate view of a system’s performance.
For example, imagine that an online bike class just went live on a digital exercise platform, and the platform’s monitoring solution is indicating that users are low, one of the system’s KPIs. DevOps and SRE teams could manually inspect large volumes of data to determine if the alert is an actual problem, but using metadata is much more efficient. The metadata indicates that the slump in users occurred on a Monday morning, a peak exercise time. With this context, teams know there’s a credible problem and can allocate resources to fix the issue accordingly.
Applying this context through metadata drastically increases the value of a company’s observability data. DevOps and SRE teams can see KPIs in the context of typical use times, enabling teams to act swiftly, root out the problem, fix the issue and avoid a service interruption that could put the company’s neck on the line.
Focus on what’s important by clustering and correlating
Clustering and correlation add meaning to data and metrics by taking a wide view of the environment, identifying relationships between data points and making connections between all of the data in a technology stack. While clustering flags potential incidents by grouping similar data, correlation takes into account variables like text, time and topology. This helps SREs ensure the system is running smoothly. And, in the case of an incident, teams have a broad view of the system failure and can move quickly, resulting in a faster mean time to detect (MTTD) and mean time to resolve (MTTR).
Let’s go back to our bikers. We’ve determined that the number of participants is particularly low for a regular Monday morning. The IT team suspects that the authentication process failed, but it needs more information to act. This is where the monitoring solution shines. It clusters the data with similar incidents, like authentication system failures, and runs correlation algorithms to sift through alerts and find patterns. With this information in hand, teams can tackle the service-affecting incident before angry attendees take their complaints to Twitter.
The customer’s digital experience is enhanced by contextual intelligence and observability in less direct ways too. Because monitoring solutions consolidate incident alerts and notifications — sometimes reducing noise by a whopping 99% — SREs are freed from combing through massive amounts of data. Teams can instead work on forward-thinking solutions that
Contextual intelligence and observability: better together
Remember our bikers? Fixing the service disruption and getting these customers back online is essential to retaining their loyalty, which helps the company increase revenue and spurs long-term growth. But this is hard to do efficiently if data is merely observable. Teams need context.
By looking at both the observability data and the context of the situation surrounding it, SRE teams can make accelerated, informed decisions that give customers the on-demand, no-downtime software services they demand. And, by reducing the time spent fixing incidents, teams can focus their time and attention on providing the latest and greatest technology that ultimately delivers more value to both customers and companies.
Moogsoft is the AI-driven observability leader that provides intelligent monitoring solutions for smart DevOps. Moogsoft delivers the most advanced cloud-native, self-service platform for software engineers, developers and operators to instantly see everything, know what’s wrong and fix things faster.