If you were planning a cross-country road trip, would you spend numerous hours manually reviewing paper maps to find the best route and calculate how long the trip will take? Or would you use a GPS navigation system that can suggest the best route and accurately calculate the travel time within seconds? Moving from traditional IT monitoring to machine learning-based anomaly detection technology is similar to moving from paper maps to GPS.
Traditional IT monitoring requires you to write extensive rules and thresholds for about 1% of your meaningful data and then manually data mining the rest when you have a problem you have to troubleshoot. It is an incredibly labor-intensive process, particularly as IT environments get larger and more complex. What if you could turn this around and have machine learning anomaly detection software monitor all your relevant data so you can reduce the time you spend troubleshooting by 60-70%? Would that be of interest?