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Network monitoring is a challenging job because networks continue to evolve to meet ever-changing client requirements. Businesses today heavily depend on their networks, and even a short outage can lead to penalties and lost profits. This is why your monitoring tool must also transform itself to not only scale as you grow but offer new features that address new challenges posed by the increasing usage demands placed on your network.
Customer expectations aren’t what they used to be. Emerging technology trends have elevated the customer experience by bringing personalization and automation to the forefront. With these strides, companies have increased their customers’ expectations ten-fold. To meet rising customer expectations, the only solution is AI. While we’re only scratching the surface, here are a few ways AI can help improve your customer engagement.
Unlocking the full potential of monitoring through ML integration, anomaly detection, and innovative scoring engines. Machine Learning has been making waves in various industries, but its adoption in the monitoring and observability space has been slower than expected. Many “ML” features remain gimmicky and do not provide actual real world value to users that encourages their further use.
The use of statistics, advanced algorithms and AI/Ml is becoming omnipresent. The benefits are visible in every walk of life, from web searches, to movie and retail recommendations, to auto-completing our emails. Of course, not many anticipated the dramatic entrance of generative AI in the form of ChatGPT for writing college essays and poetry on arcane topics.
A web application or an API breaking is a matter of when, not if. Whether the cause is buggy code making it to production or infrastructure failing to support the software built upon it, incidents of varying severity are the norm rather than the exception, appearing frequently enough that the industry has coined the terms Mean Time To Detect (MTTD) and Mean Time To Recovery (MTTR).
