The pressure on IT to do more with less is constant and only intensifying. For the past few years, IT leaders have been striving to bring in reliability, scalability, and customer centricity – all while simultaneously lowering total cost of ownership.
AIOps (Artificial Intelligence for IT Operations) could be the best solution to this issue so far. Our clients always have many questions about how to better understand, approach, and implement AIOps. So, in this five-part series, I’m going to demystify AIOPs using the simple framework and with some real-world examples that resonates well with many IT leaders.
Gartner coined the term AIOps in 2016. According to Gartner, “AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.” AIOps is not a single technology, but rather AIOPs is a combination of disparate technologies for data collection, data processing, data analysis, data visualization coupled with automation tools and solutions.
Business leaders have been looking to IT to guide them in leveraging the value that can be derived from troves of data available to them. I consider AIOps as a truly unique opportunity for IT leaders to lead the enterprise by example. With AIOPs, IT departments can further the data decision making process and also be more proactive.
The key to AI application – observability, orchestration, and automation
So, how do you adopt AIOPs (AI and ML) in IT operations and glean actionable insights from data generated by various IT assets and IT systems users? To simplify your AIOps implementation approach, we recommend framing the application of AI across three key pillars – observability, orchestration and automation.
Observability – In the most utopian scenario need to be self-aware and have the ability to self-announce and self-heal when there are issues. Monitoring in the traditional world is highly siloed, so observability is best achieved by bringing logging, tracing, and metrics from network and storage, along with integrating servers from across on-premises or building multi-cloud environments into applications. This full-stack monitoring capability will provide deep IT, service performance, and application performance insights. At Capgemini, we extend these insights to bring awareness and context to improve business-level performance. We’ll take a more in-depth look at observability in part two of this blog series.
Orchestration – Service management is the hub of all human-centric activities in IT operations. With AI and ML, we bring human-in-the-loop (HiL) automation to not only streamline but also optimize incident, problem, and service request management processes. With chatbots and digital voice assistants, automated bots can engage with users and drive self-service to various types of requests, such as password resets or data and report requests . We’ll learn more about this in the third installment of this blog series.
Automation – Automation through scripts is nothing new to IT departments and programmers. With advancements in automation using APIs, data management, and GUI-based robotics, we can easily push the envelope by delegating deterministic procedures to bots and empowering human workers to take up more problem-solving challenges. Additionally, AI takes it further by helping us achieve self-heal-based remedial actions. We’ll look more at this in part four of this blog series.
To summarize, there are clear benefits that can be derived from the untapped potential of data sitting in IT systems. With the right AIOps strategy, organizations have a sure-fire solution to pain points – whether it’s excessive ticket issuances or manual fulfillment of service requests. ADMnext can help you formulate a successful strategy that looks to mining and analyzing data via advancements in Big Data, AI, and ML algorithms and visualization techniques.
In part two of this series, I’ll delve into these advancement and techniques more, along with observability and how you can transform from your current state of monitoring.