AiThority Interview with Edmond Baydian, Chief Strategy Officer at Optanix

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Where is Optanix headed next on your AIOps journey?

It’s become evident to us that increased automation is critical. Many of our customers are telling us they have a lot of information about their operating environments, but they have less time to interpret the data itself. They want their monitoring and management solutions to drive more insights and they want to automate more functions.

From a service assurance perspective, that automation can provide enrichment of monitoring data or auto-investigations, or they can drive changes back into the network and systems for corrective actions. We have focused AIOps value in two main areas: first, auto-investigations and workflows for corrective actions; and second, automatically using the data we collect to continuously provide current and near-term insights about the operating environment.

When integrated with our business impact modeling capabilities, we can execute those automation workflows based on what has been learned about the IT environment. That value can be realized directly from the monitoring layer before we present the output to IT service management systems we connect with.

Our value is to reduce the number of incidents people look at and organize the outcomes in the business priority context of each customer. As our AIOps capabilities mature and our customers become more experienced with their implementations, we are able to deliver on the value of having automation at the service assurance layer.

Because we’re integrating events and metrics, we can also leverage additional sources of telemetry that we’re able to collect. We’re able to leverage information coming from what would traditionally be multiple functional silos within a server assurance stack in one single place.

So, where we’re heading next in our journey on AIOps is making investments in new Machine Learning capabilities. It’s a huge area of investment for us.

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What business advantages will Machine Learning bring to MSPs?

We are always looking at ways to intersect algorithmically or Machine Learning capabilities with IT operations, in service providers and their enterprise customers. Machine Learning and increased automation will be catalysts in improving the overall user experience.

So, once we leverage AIOps to look at what’s required for management from a systems perspective, we can extend it to an end-user experience perspective as well. We have several things planned for taking our Machine Learning capabilities and leveraging them to model the end-user experience based on the raw information we’re collecting from the environment. One area of interest is to integrate path-based awareness and monitoring capacities into our overall platform.

We also have other investments with respect to the other types of information that we collect. For example, we’re driving performance analytics value off of NetFlow information as well. We see these investments becoming very important as they can help to measure end-user experience regardless of where in the enterprise the customer places their application workloads.

How will Machine Learning differentiate your automation capabilities?

In the Optanix Platform, our automation is driven off of what we call the decision engine. The decision engine is a powerful, stateful modeling engine coupled to our execution workflow engine. This coupling enables us to represent logic that an engineer would follow in the real-world scenarios and embody it within the system, allowing us to keep the “engineer in the box” concept available 24×7.

So, given this capability, our next set of investments in Machine Learning is aimed at looking at the efficiency of the automation themselves so we can drive insights into how that automation is being used and how effective they are. For example, our Machine Learning is being applied to analyzing the state machines themselves. This will directly result in enhanced automation and reduce the cost of “training the machine.”

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How will your investment in Machine Learning impact your technology?

We are already seeing an impact at two levels:

First, the system will use the data it collects. By expanding the scope of data analyzed, our causality analysis at the network and system layers are becoming more accurate, more actionable and timelier. Our smart analytics are already leveraging Machine Learning in real-time on the metrics we’re collecting. This gives our customers near real-time awareness of their environment’s behavior. So, rather than using performance monitoring data for capacity planning or historical review, which most organizations do, we’re actually operationalizing it through the use of Machine Learning.

Second is the ability to determine business impact. This allows the engineer to observe a problem through the telemetry and then respond and react in a way that prioritizes revenue and business services. We achieve this through our service modeling capabilities: the Machine Learning on the data drives the outcomes in those models, allowing our platform to determine the business context. This will give us a leg up heading into the future.

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Thank you, Edmond! That was fun and hope to see you back on AiThority soon.