What is AIOps: Why, When to use and implement AIOps
Sue Tripathi, Managing Director and Global GTM, Data, AI, Cloud Technology | Accenture
About this talk
What is AIOps and why should we use it? When is it appropriate to use AIOps and how do we implement it? AIOps may be viewed as the application of machine learning analytics- platforms that utilize big data, modern machine learning and other advanced analytics technologies to enhance IT operations. These platforms allow concurrent use of multiple data from varied data sources, providing proactive, personal and dynamic insight. Using and implementing AIOps requires an understanding of the fundamental essence of what AIOps can do in real time.
This presentation covers the core components of AIOps, specific use cases of when to use it, and how you can implement AIOps.
The first section covers key components of AIOps, the “what” question. It includes:
1. Engaging-historical analysis, anamoly detection, performance analysis, predictive insights, correlation and contextualization
2. Automating-automated root cause analysis, optimization suggestions, and task automation
3. Monitoring-historical data, real-time data, and decision-making insights
The second section covers “why” and “when” to deploy AIOps.
AIOps use cases presented in this session cover three core outcomes intrinsic to IT operations:
1. Leveraging AIOps capabilities to detect, diagnose, and resolve incidents faster with a clear outcome to decrease mean time to recovery (MTTR)
2. Proactive performance monitoring, preventing problems with actionable alerts that identify anamolies with a clear outcome to improve operational efficiencies and overall health of the business.
3. Surface insights to drive better and faster decision making, using machine learning technologies.
Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource.
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