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. This holds true across industries but especially in technology, banking and finance, manufacturing, logistics / supply chain, retail and e-commerce, public sector and healthcare. 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. As 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 quality 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.
The use of artificial intelligence is becoming a necessity for IT operations. As businesses go digital their use of more complex technologies like cloud architectures puts them at risk of failing with crucial governance and monitoring requirements. Failure will impact the corporate Profit & Loss through revenue- and productivity loss.
Here are a number of reasons why you need AIOps:
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 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.
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.
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.
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.
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 from 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.
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.
We help organizations to capture the value of Artificial Intelligence in IT Operations - AIOps.
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