As organizations evolve and fully embrace digital transformation, the speed at which business is done increases. This also increases the pressure to do more in less time, with a goal of zero downtime and rapid problem resolution.
Real costs to the business are at stake. For instance, a 2021 ITIC report found that a single hour of server downtime costs at least $300,000 for 91% of mid-sized and large enterprises – and 44% of companies said hourly outage costs exceed $1 million to over $5 million.
The key to avoiding downtime is to get ahead of issues and slowdowns before they even happen. Thankfully, there’s a reliable recipe for how to achieve this. Let’s examine the power that comes with combining AIOps together with observability to minimize downtime and the negative business consequences that come with it.
The Power of AIOps
To really grasp the combined power of AIOps with observability, it’s important to first understand the capabilities of each of these technologies and what they mean. Let’s start with AIOps and the crucial role automation and AI play in supporting enterprises struggling with the inherent challenge of scale and stability.
A typical enterprise IT system may generate thousands of “events” per second. These events can be anything anomalous to the regular operations of multiple systems – storage, cloud, network equipment, etc. This makes it impossible to keep up with events manually, let alone parse out and prioritize which events will have major business impacts from the ones whose impact might be negligible.
AIOps allows you to put AI to work in separating the signal from the noise, to surface the issues that cause most damage and apply intelligent automation to resolve these autonomously. It’s a value proposition that more and more companies are understanding and investing in. Indeed, analysts have found the AIOps market has already surpassed $13 billion and will likely top $40 billion by 2026.
The Value of Full Stack Observability
Organizations can reap further value from AIOps when these capabilities are combined with observability, which is the ability to measure the inner state of applications based on the data generated by them, such as logs and key metrics. By looking at multiple indicators to get a full understanding of incidents and components within a system, a strong observability framework in the enterprise can help identify not just what went wrong, but the context for why it went wrong and how to fix it and prevent future occurrences.
One popular approach for comprehensive, full-stack observability is what’s known as a MELT (Metrics, Events, Logs, and Traces) framework of capabilities. Metrics indicate “what” is wrong with a system; understanding Events can help isolate the alerts that matter; Logs help pinpoint “why” a problem is occurring; and Traces of transaction paths can identify “where” the problem is happening.
Although observability and AIOps can work alone, they complement each other when combined to form a holistic incident management solution. Blending observability with AIOps enhances speed and accuracy in leveraging applications data for proactive identification and auto-resolution of problems and anomalies – even to the point of heading off issues before they arise.
This proactive optimization of systems can drastically reduce risk and downtime for the enterprise – with AIOps and observability serving as a powerful combination of capabilities that positively advances the roles of numerous stakeholders, from the doers of the work to the handlers of the exceptions.
Combining AIOps and Observability: A Case Study
An example comes to mind of a private investment company based in Canada – one of the largest institutional investors globally. They struggled to manually coordinate 15 decentralized monitoring tools, resulting in massive system noise and delays finding the root cause of issues. To solve these challenges, they implemented a combination of AIOps and observability tools that helped conduct end-to-end blueprinting of the entire IT ecosystem and then integrate all 15 monitoring tools to capture and prioritize alerts.
The new system now automatically eliminates false positives, generates tickets for real alerts, and then deploys suppression, aggregation, and closed-loop auto-heal capabilities to autonomously resolve most issues. For the remaining unresolved tickets, the system does root cause analysis, logs all the relevant data along with the ticket and then sends it to the manual queue.
As this case study illustrates, pairing observability together with AIOps capabilities allows an organization to link the performance of its applications to its operational results by isolating and resolving errors before they hamper the end user experience. In doing so, enterprises can support closed-loop systems for getting ahead of potential causes of downtime to reduce the number of incidents and – where events do occur – decrease the mean-time-to-detect (MTTD) and mean-time-to-resolution (MTTR).
Conclusion
Clearly, the business benefits that come from combining AIOps and observability together are exponentially greater than the sum of what observability or AIOps could do on their own. These advantages are critically important for organizations looking to minimize both downtime and the steep organizational costs that come with it.