“Data is very dirty, right? You know, you have to make do with whatever is available in a lot of cases,” Kothandaraman said. “You’ll have to augment a lot of it with your own data, and nobody is willing to share data with each other.”
Another challenge that emerged was dealing with the limitations and potential of different forms of AI. “AI and ML are inherently probabilistic, whereas operational teams do not want any probabilistic answers. It has to be very deterministic,” he said.
A deterministic response is one that is accurate and consistent time after time, and when dealing with operations, it’s absolutely essential to success. Kothandaraman noted that for network operations teams, when they get trouble tickets and alerts, the information has to be actionable. “We can’t send them on a probabilistic wild goose chase,” he said.
Solving the network noise problem
A key benefit of the Selector AI platform is its ability to reduce the overwhelming amount of alerts that plague network operations teams.
“Some customers have like 30-40 different tools that they are using, each of them starts to complain whenever there is an issue,” Kothandaraman noted. “The number one criteria that we are judged on is noise reduction.”
The platform achieves this through sophisticated data integration and relationship mapping. Kothandaraman explained that Selector AI uses both operational and relationship data. That data is what is fed into the machine learning engine to help identity issues.
