Enterprise data systems are like busy family households. You see a constant flow of activity to varying degrees from room to room. This activity includes people wandering, opening and closing doors. And then there are other streams constantly flowing through the household- electricity, water, Wi-Fi networks and more. In modern enterprises, the data deluge is a critical issue. While we take the complexity for granted in a household, such is not allowed in a connected enterprise.

According to Gartner, enterprise usage of AIOps is set to surge from a mere 5% in 2018 to a whopping 30% in 2023. To survive in an increasingly competitive market, MSPs must not only respond well to customer expectations but anticipate them. Another Gartner report states that by 2025, over 80% of public cloud managed and professional services deals will require both hybrid and multi-cloud capabilities from the provider, up from below 50% in 2020.

AIOps or Artificial Intelligence for IT Operations refers to a set of technologies that augment human decisions with autonomous decisions driven by AI and machine learning that learn patterns, relationships from data. AIOps is the term originally coined by Gartner, and pictorially illustrated in the following way.

One of the core value propositions of AIOps platforms is to increase IT efficiency & productivity by applying AI & ML techniques to perform Alert Noise Reduction. This in turn translates to direct cost reduction due to savings in IT man-hours. In this approach, the AIOps platform kind of becomes like a gatekeeper for all the IT alerts/events, and it can help effectively, reduce and correlate such events, so as to send meaningful incidents to NOC or Service Desk.

The security stack is a crucial part of any company’s IT infrastructure. However, Security teams increasingly report that traditional SIEM solution approaches are “costly, complex, and resource-consuming,” according to a recent ESG survey. Fortunately, there has been significant innovation in how firms approach cybersecurity with new cloud-native technologies stack and breaking free from vendor lock-in and giving themselves more flexibility, cost advantage, and future-proofing.

Observability has gained a lot of momentum in the past year, be it full stack observability or data observability. Modern complex IT systems using clouds, microservices and serverless are easy to develop and deploy but extremely difficult to observe. These systems generate tremendous amounts of data and need an automated way of handling the volume. The next era of delivering customer experience is underpinned by the full stack observability capability.

“Have you tried shutting it off and turning it back on?” While AIOps won’t likely remove this query from our vocabulary any time soon, technology is certainly here to take on a bulk of the heavy lifting. For all-sized companies, service calls are still going to continue to pour in. And, there’s no sign of any of the world’s CompTIA certs going to waste in the near future. Still, thanks to AIOps, many jobs within the world of IT will become more streamlined.

CloudFabrix solution provides a holistic approach for enterprises to implement proactive operations with the objective of eliminating/reducing critical incidents and improving customer satisfaction. The solution primarily relies on applying regression/forecasting models on any time-series data to detect and forecast anomalies. One of the unique features of the solution is the ability to convert unstructured data such as logs/incidents/alerts into time-series data to be used for running prediction models.

This blog post covers 4 data masking techniques and data obfuscation techniques that you can implement with Robotic Data Automation (RDA) to mask or hide sensitive data or personally identifiable information (PII) like social security numbers (SSNs) that may have crept unintentionally in logs or events.

In this highly dynamic environment, organizations are looking for ways to innovate and manage resources efficiently. In the first part of the two-part blog series, we saw how organizations handled incidents without an AIOps solution and how long it took to resolve that incident — a scenario representing different steps to resolve an incident. In the second part of the two-part blog series, we look at how organizations were able to handle incidents after deploying AIOps.