Artificial Intelligence for IT Operations (AIOps) is an advanced analytics and operations management solution that is designed to help organizations address the challenges of monitoring and managing IT operations in the era of digital transformation. AIOps leverages the power of Artificial Intelligence and Machine Learning Technologies to enable continuous insights across IT operations monitoring.

According to one survey, 94% agree that AIOps is “important or very important” to manage network and cloud applications performance. AIOps intends to help customers contextualize humongous data volumes and streamline IT operations with automation. As IT infrastructure grows in complexity, alerts flood IT Ops centers and Ops teams drown in managing the deluge.

The absence of topology can be a key inhibitor for AIOps tools, creating blind spots for AIOps as they only have access to event data. A topology, an IT service model, or a dependency map is a real-time picture of tools and services that are connected and dependent on each other to deliver an IT service. Suppose an application is driven by cloud-native technology, connected with any kind of ephemeral systems (containers and microservices), and relies on storage, database, and a load balancing tool.

There’s plenty of loaded terminology and buzzword bingo when it comes to the latest advances in cloud application delivery. Especially when it comes to multi-cloud – which should merely mean multiple cloud instances when modern cloud applications really leverage multiple hybrid IT operating models, atop both existing business silos and newer microservices application workloads.

A pivotal inhibitor to mitigate these challenges is the Data Value Gap. Data automation and Data Fabric are emerging as key technologies to overcome these challenges. Learn from industry experts about these key technologies and how they create a lasting impact in enterprise IT.

This is the first Feature Byte in the AIOps series. The idea of the Feature Byte series is to talk about key operational tasks and processes in AIOps, and how CloudFabrix Data-Centric AIOps platform features help implement such tasks. Look for more such feature bytes over the next few weeks.

Data laying all around an enterprise’s premises and over the cloud is of no use unless it forms part of a bigger and clearer picture. This is what a data stack does by helping enterprises leverage data to its fullest potential- it turns raw data into insights that can be acted on and lead to business benefits. The complicated modern enterprise of today cannot make do anymore with the obsolete ways of data management.

AI has certainly become the hallmark of digital transformation strategy. According to IDC, global AI spending is forecasted to reach $500 billion in 2024 with a CAGR of 17.5%. Likewise, Gartner predicts low-code application platforms (LCAP), robotic process automation (RPA) and AI are fueling the growth for hyperautomation, and the market will reach $596 billion in 2022, up nearly 24%.

According to Norton’s Cyber Safety Pulse Report, India faced over 18 million cyber threats in only Q1 2022, roughly 200,000 threats every day. Of the bulk, 60,000 were phishing attempts, and 30,000 were tech support scams. For perspective, phishing attempts around the world during the same period counted for approximately 16 million. CERT-In also reported over 2.12 lakh (~0.1 million) cybersecurity incidents until February 2022.

Digital-first businesses are striving for service assurance, which has become the lifeblood for their businesses processes. But they are increasingly getting complex across legacy and cloud-native applications, multi-cloud distributed services, with the rise of edge and when leveraged with Kubernetes and microservices architectures. Service assurance needs full-stack observability; however, customers need an approach to tame the data deluge while enabling actionable insights.