We live in an increasingly digital era where business success depends on the technologies used. Performance problems (peaking, cyber attacks, etc.) lead to financial losses and IT operations need to work more efficiently. Digital businesses and technologies are increasing the volume, speed and variety of data. Correlation and manual analysis of data and alerts is increasingly difficult for the IT operations team, with tools in silos spread across mobile devices, the Cloud, and the mainframe. IT operations suffer from excessive noise because there are many events and many processes and it becomes humanly impossible to keep up with everything that goes on. The solution is to achieve a more efficient management with reduced costs and that is why Gartner created the term “AIOPS: Artificial Intelligence for IT Operations”. This new term aims at a change of culture to obtain more agile processes.
But, after all, what is AIOps?
This new form of IT management allows a move away from isolated operations management and provides intelligent insights that promote automation and collaboration to provide continuous improvement. AIOps uses big data, data analysis and machine learning to provide insights and a greater level of automation. Thus IT operations do not depend so much on human interaction to perform the management tasks required by modern infrastructure and software. In this way, human interaction is reduced in routine tasks and human resources are released to other areas of added value. AIOps solutions consume data from various resources and then store and provide access to them, enabling more advanced analysis.
What are the main uses of AIOps?
- Analysis of the cause of the problems
- Reduction of algorithms and correlation
- Preventing problems through smart alerts
- Intelligent automation
- Predictive Capacity Identification
- Agility between teams and datacenter groups
Challenges of AIOPS
This new way of managing information technologies presents numerous challenges. The first of all is resistance to change. There is still some mistrust regarding artificial intelligence and there is a fear that the automation of tasks will put people’s jobs at risk. Another challenge is data disorganization. Most companies do not have the data in an organized way and Artificial Intelligence works entirely based on reading information to perform its function. When it reads wrong information, it creates wrong standards. Lastly, another challenge in this industry is the lack of planning. Some companies implement IA not to be out of the market or because the competitor deployed and had good results. However, each case is a case and if there is no planning on what the company expects of the machine and if there is insufficient and well structured data, the whole investment falls to the ground. It is necessary to do market research and evaluate the pros and cons of technology and its applicability to the business.