5 Building Blocks of AIOps

AIOps - Artificial Intelligence in IT Operations - promises to transform IT Operations - according to Gartner. What are the Building Blocks of AIOps - and how can you validate the value and feasibility for your organization? Today, we live and work in a digital economy that is becoming more and more vulnerable. Companies have come to rely on increasingly complex IT infrastructure, software applications, and microservices to drive revenue and improve productivity. As the IT ecosystem of companies becomes more agile, complex, and real-time the IT operations challenge increases exponentially. Businesses have been ill-equipped to deal with looming technical issues, which has caused many to lose revenue or incur significant costs. No longer can information technology operation professionals rely on traditional methods. To address problems before the business is impacted, companies must make a shift towards automation. Artificial Intelligence (AI) is the new driver for transforming traditional ITOps into AIOps. Here we give you an introduction to what AIOps is, why it’s needed, and how it can be applied to your own operations. When building a house, we explain how the key capabilities of AIOps ("elements of a house") must be built from the bottom up where the foundation for building (or “floor”) is data. Without the ability to harvest quality data, it's impossible to build the "house" with sound and solid "walls," and a "roof." When it comes to AIOps, what you put in (junk), is what you get out (junk). So, it's imperative that you use quality data as a foundation. quality Junk in - junk out... 1 - Data Harvesting (floor) If you're trying to envision where the five capabilities of AIOps fit into a business, it starts with data harvesting. As the "floor" of the AIOps infrastructure, data harvesting involves collecting data on a continuous basis - time-series performance metrics and text logs - from multiple sources. Structuring and normalization of the data is a key capability together with flexibility in the ingestion of APIs. AIOps is about making sense of data across huge data sets and sources. Hence the ability to flexibly consume, normalize and structure the data for subsequent processing across any sources is fundamental. Learn about AIMS data harvesting Get the algorithms to work for you 2 - Learning & Correlation Engine (walls) If data harvesting is the floor of AIOps, then the learning and correlation engine represents two walls of the building. Using the power of machine learning (ML), AIOps learn the normal behavior patterns of the data and use correlation to identify relationships and make sense out of a vast amount of events. Using anomaly detection, AIOps detects abnormalities that can lead to problems. This helps to get to the root cause of the problem and speed up resolutions. Learn about AIMS Normalisation Engine Understand the context 3 - Topology Discovery (walls) Topology discovery is another crucial capability. Topology refers to the physical structure, relationships, and dependencies of artifacts or assets in an organization’s IT ecosystem. Topology can be represented in many layers and business needs. From technical network diagrams to dependency topologies to higher-level business topologies. The ability to navigate through the topology layers - from/to - technical and business is key to understanding the context, and hence the importance, of any anomaly. The topology includes infrastructure, applications, and services independent of data center, physical, container, or cloud deployment. The ability to get topology context with anomalies allows a drastic reduction in MTTK (Mean Time To Know) and the MTTR (Mean Time To Respond) beyond anything that humans are capable of doing alone. Learn about AIMS Topology Discovery Getting to relevant anomalies is a major milestone 4 - Business Relevant Anomaly Alerts (ceiling) Business relevant alerts are the "ceiling" of the AIOps architecture. What’s different about alerts in an AIOps infrastructure is that they do not rely on pre-configured alerting defined by technical teams - rather they rely on algorithms to identify anomalies. This relies on solid data, a sophisticated and robust anomaly detection engine, and context. The alerts should be prioritized to those that are most relevant to the specific business operation impacted. The alerts that you don’t care about should be suppressed automatically. This contributes to eliminating the common alert fatigue that IT teams struggle with when trying to filter through all the alert noise to get to the most important problems and solve them as quickly as possible. Learn about AIMS Anomaly Detection Don't underestimate the value of smart dashboards Bonus - Actionable Dashboards A bonus capability from all the data and actionable insight are dashboards and reports. In practice, the data is real-time business intelligence. Possibly better than any traditional BI tool, real-time with built-in anomaly detection and context. The ability to leverage this data and insight for reports and dashboards for stakeholders across an organization - from technical to managers and executives will be crucial to the required governance of IT that the profit & loss of businesses rely on. Learn about AIMS AIOps Dashboards Its all about taking early action to prevent 5 - Taking Action (roof) Last, but certainly not least, actually taking action with AIOps forms the roof. Every capability before this one enables you to take action whether that action is manual or automated. Once the data has been harvested, processed by the anomaly detection engine, and analyzed for context-aware anomalies, then the resulting probable root cause can be identified and acted upon quickly. This may be manual action or automated action by triggering auto-healing scripts. Learn about taking actions backed up by AI Eager to implement AIOps? Get started free with AIMS Experience the AI delivering dependency topology from day 2 and anomalies from day 14. With 200+ out-of-the-box integrations, the flexible AIMS API, and agents you can connect all your systems - cloud, hybrid, and on-premise. AIMS automatically normalizes the data with the hyper-scalable time series Normalization Engine. Then AIMS uses machine learning and artificial intelligence to learn the behavior of all your performance metrics across your IT environment, identifies critical correlations and dependencies, and provides you early notification of IT issues that can bring down your business.

5 Building Blocks of AIOps

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