Network capacity planning aims to ensure that sufficient bandwidth is provisioned, allowing network SLA targets such as delay, jitter, loss, and availability to be reliably met. It's a complex, error-prone task with serious financial implications. Until recently, the network data necessary for insightful capacity planning was generally only available via static, historical, after-the-fact reports. This situation is now rapidly changing.

 

“By pairing advanced data science and cognitive technology such as AI and machine learning, IT can drive new and smarter predictive insights to improve network capacity-planning accuracy,” says Ashish Verma, Deloitte Consulting Managing Director. “This helps organizations unleash data to make more agile decisions, improve operational wisdom, avoid downtime and create a better user experience.”