eJAmerica’s experience in enabling Artificial Intelligence in IT Operation for multiple Financial Services and Healthcare Customer.
- Operational Analytics, in simple terms, could be hailed as the application of business analytics on operations.
- This means that tools and methods from domains such as data mining are used on data that is extracted from operations in order to extract the insights and optimize decision making.
- AIOps stands for Artificial Intelligence in IT operations. It refers to data science and AI to analyze big data from various IT and business operations tools.
eJ-AIOps Goal is to increase the speed of delivery of the various services to improve IT services’ efficiency by reducing the Level-2 and Level-3 effort to save labor costs.
Other Ancillary gains of the eJ-AIOps include:
- eJ-AIOps will provide a superior user experience.
- AIOps enable themes to move away from siloed operations.
- It enables the generation of insight, which can be communicated to the stakeholders. It can help drive Automation and collaboration within an organization.
So why should an executive or a manager care about eJAIOps:
If your business has a Very large infrastructure that depends on the cloud for its day-to-day operations, then you would understand that the downtime is costly, and the service can get slow often. This would increase the cost even further. Thus, servers and cloud infrastructures need proactive management. However, the complexity of catering to this is too high.
Traditionally one would need to hire a large team that can keep a regular record of the performance and runs a periodical analysis of audit reports to attain desired goals.
While Automation in Monitoring and Event, Correlation, Aggregation Management reduces the Workload from the LEVEL-1 team, AIOPS focuses on Optimizing and reducing the effort from the LEVEL2 and LEVEL3 team. For Example, eJ’s ways of AIOPS will leverage AI for Root-Cause-Analysis, whereby reducing the LEVEL-2 Workload significantly.
eJ-AIOps Breakthrough: The premise of AIOps is that many of the level-1 issues can be solved through Automation. eJ-AIOps will enable faster root-cause analysis, predictive analytics, noise reduction, and intelligent Automation.
Over 70% of the people using AIOPS identified alert correlation and proactive issue detection as the two biggest challenges.
- eJ-AIOps helps reduce noise.
- Level-2 Effort Reduction: eJ-AIOps can help by providing faster and more accurate root-cause analysis
- eJ-AIOps automate the analysis of an event. This is directly related to the first point, and obviously, this can all help reduce alert noise.
- Alert fatigue. This refers to a case where there are so many alerts being generated by the system that humans find it difficult to handle all of them.
- If implemented correctly, we can ensure 30% Operational Cost savings
At the bottom of the stock, we have different data sources. This can be events such as alerts, real metrics that we are using to monitor a server, such as a load of a server. Tickets are operational issues that are being investigated. And logs are logs of activity. Then, on top of this stack of the data sources, we have real-time processing, rules and patterns, and domain algorithms. And these are materialized using machine learning and artificial intelligence. When using machine learning and artificial intelligence to create algorithms, they run on rules and sometimes just on pure machine learning, such as deep neural networks.
All those different data sources can be digested and then automate many of the things IT labor is doing. In this image here, Gartner, produced in 2017, can see a diagram explaining how AIOps is working.
The outer circle encapsulates business value. This is the most important component of AIOps. It creates business value by improving the quality of the service and reducing costs.
The second layer consists of MEM (monitoring & event management), Service Desk, and Automation.
Monitoring could first be the act of observing what is happening 24×7. Service desk refers to the ticket management, giving direction between the team and the platform and the customers. Automation is what eJAIOps, machine learning, is offering. There is another circle in the middle which talks about continuous insights. All these, the monitoring and the engagement and the Automation, and the insights are generated by the core, which is based on machine learning and big data.
In terms of adoption, more and more enterprises will be using AIOps to support two or more major IT operations functions. AIOps is becoming more and more popular.
It reduces costs and improves the quality of the service. It also caters to operational analytics. As a general guide, operational analytics, and AIOps, they do not describe a single use case. AIOps is about using data signs, machine learning, AI, data mining, and data from operations to extract insight into automated processes. This means that many tools can be useful in this effort, including dashboards or various kinds of machine learning models. There are many applications of AIOps. Optimizing the availability of a network, Automatic ticket and problem assignment, anomaly detection for cybersecurity reasons, and improving storage management. Much work in AIOps provides us with another very useful chart, how an organization can excel in AI for IT operations.
There are four phases, the establishment phase, the reactive phase, the proactive phase, and the expansion phase.
It is a very intuitive diagram. The first phase, the establishment phase, is about understanding the challenges related to operations that an organization faces. Then these challenges will be solved in the reactive and proactive phase.
The difference between the two phases is that the reactive phase is simpler from a technical perspective. In contrast, the proactive phase is more advanced because it is based on prediction.
At some point in this whole process, you want to move to prediction. You want to be able to see problems before they come. Once you can do this, you want to expand and automate as many of your operations as possible.
Talking about root cause analysis, that is a very interesting reference. This one is provided here for your consideration that studied the problem of identifying the root of a problem. We have many classification models, and some of those based on logic.
We saw many examples and many use cases of AIOps. We saw how AIOps could reduce the cost and improve the efficiency of various services. Our industry is progressing towards greater adoption of data science and artificial intelligence in IT operations.
Please contact us to know more about eJ-AIOps and Operational Analytics.