Organizations today are pressured to keep their IT applications and infrastructure up and running and minimize their downtime. While this has always been a critical goal, it’s become harder to achieve with modern architectures, such as microservices, containerization, edge computing, hybrid-cloud deployments and the newer development methods such as agile DevOps techniques.
The massive volume of operations data generated by a modernized IT infrastructure creates the need for techniques to improve monitoring to understand what’s happening in every system.
How do AIOps and Observability Differ?
In IT operations, identifying potential problems is only the tip of the iceberg. The real key is to resolve these issues as fast as possible and seamlessly before they pose a threat to business services.
Artificial Intelligence for IT Operations or AIOps systems detect anomalies, find patterns in noisy IT data with correlation and deduplication, contextualize data and help in performing root cause analysis to apply fixes.
AIOps platforms are rapidly proliferating industries. According to Gartner, the market is expected to grow at 15% YoY between 2020 and 2025.
For organizations to ensure IT system availability, they need access to metrics, events, logs, and traces from each application and associated infrastructure. Metrics indicate ‘what’ is flawed in a system. Logs show ‘why’ an issue flared up. Traces help locate ‘where’ the problem is. And, events suppress noisy alerts that can be auto resolved so that IT teams are focused on critical and anonymous incidents.
What are the Benefits of Observability Tools Vs. Monitoring Tools?
Monitoring tools often create disparate health status scenarios, making it hard for teams to keep track of issues. Monitoring is the collection of MELTs from sampling systems. The result of monitoring tools is a huge data volume for users to explore and extract insights from.
Observability eliminates sampling to collect MELTs from everywhere, including containerized microservices. Observability tools continuously yield contextualized insights for teams and make monitoring better without diving into monitoring data.
Many businesses already have monitoring tools or logging applications. Consider what you are already using and find an observability solution that can fill the gaps of your monitoring application and work in an integrated fashion.
AIOps tools with an observability pipeline can work together with a present monitoring tool to make it smarter. Observability is the sustainable approach to monitoring. If systems are observable, we can monitor them.
What happens when AIOps comes first?
Enterprises keep battling the data deluge- a vast volume of data captured by monitoring systems of a complex and modernized IT infrastructure. AIOps essentially can help enterprises get deep insights into their systems.
However, several data gaps exist in a hybrid-cloud, distributed, microservices-based IT setup. The data, for one, is not readily available for processing. Even if it is, it’s distributed.
A properly implemented, sophisticated AIOps platform will deduplicate and correlate events, identify patterns in the noise using AI and ML, detect anomalies, flag alerts for only incidents that need attention, identify the location and cause of incidents and suggest fixes.
For AIOps to do what it does, it needs those data gaps plugged in.
If your AIOps tool can help find gaps and discover duplicates, you can rationalize the tools. If you don’t have observability or struggle with monitoring gaps, AIOps tools bundled with observability can fill the void. If not, you need to assess present data gaps, plug them in separately and then go for an AIOps solution.
AIOps without observability can only yield sub-optimal results.
What happens when Observability comes first?
Addressing observability is critical, but you still deal with the data deluge and siloed views if you stop at it. Observability means you have complete visibility into operations data, including the modernized parts of your organization.
If observability is still followed by monitoring, data deluge and siloed views persist as an issue. Observability is all about visibility across systems and tying business KPIs with technical data. Monitoring makes it possible to track and understand if everything is working as it should. And AIOps is about deriving insights and meaning from the data.
Having observability means that you have all the correct data from across the organization. Now, you cannot still derive meaning from this data without manual intervention. You still need AIOps to process and dig insights from the large volume of cross-domain data.
What happens when AIOps and Observability come together?
AIOps and observability don’t make a lot of sense standalone. While they can work without one other, they make up a holistic solution together. When you have complete observability information (MELTs), you can feed the data into an AIOps platform to correlate and surface events without manual labor and intervention.
This is how you derive all the following benefits from AIOps and observability in tandem:
- AI and ML add speed, accuracy and scale to ITOps and DevOps.
- Production, development and testing environments communicate with each other all the time. Any change in one of these systems needs a quick response. AIOps and observability build real-time systems as context-rich data that can traverse the entire application stack, reducing noise and driving automation to minimize MTTR.
- With AIOps and observability working together, an enterprise can automate operations for distributed applications.
- AIOps and Observability close the loop of the cycle of discovery, analysis, detection, prediction and automation, creating an agile organization on the path to self-healing IT.
- Automation and acceleration of data operations is another benefit with low-code or no-code observability pipeline capabilities.
AIOps & Observability- Which Should Enterprises Prioritize?
Without AIOps, observability makes systems observable without a solution to present all the data in the form of insights for decision making. And, AIOps without observability can only present half of the picture. Enterprises still need to plug gaps in data for AIOps to derive insights.
In the ideal case of AIOps and observability coming together, enterprises enjoy streamlined data pipelines, harness more data, process hidden data, gain better visibility into gaps and build a cost-effective solution to the problem of data deluge and IT issues.
