Beyond the scalability of ServiceNav, the research work with the LIG has aimed to add Artificial Intelligence to supervision in order to meet the following uses:
- Reduction of false positives and false negatives
The market reports a number of false positives ranging from 30 to 80% for a conventional monitoring solution.
For example: for an average of 50 incidents per day, 25 of them are really not incidents. This is due to incorrectly configured thresholds: either too high or too low.
This means that operators spend half of their time on alerts that are not incidents or that are not a priority: time spent on unnecessary actions, loss of confidence in the supervision solution ...
When a critical problem occurs, the operating crews are in "firefighter" mode, focused on that emergency. Whether during the day or during on-call duty, the stress generated is enormous and the pressure from management or users is complicated to manage.
Predicting incidents with enough anticipation is therefore a solution for an organized and serene way of working, allowing at the same time to obtain a better quality of service and therefore higher business application availability rates.
- Identify the source of a problem as quickly as possible - Root Cause Analysis
Getting to the source of a complex problem takes time. The objective is to reduce this time to a minimum through analysis and reasoning assisted by Artificial Intelligence.