The ultimate football match

Matti Aksela
Comptel

Few people need to be told just how popular the games in Brazil were. Thousands watched the football tournament in person, and millions watched from the virtual sidelines. During the final game, 88 million people had more than 280 million social interactions on Facebook, while on Twitter, there were more than 600,000 tweets per minute. 

The people who felt the effects of the virtual games the most were network operations managers around the world. For example, Akamai Technologies reported that about 3.5 million viewers tuned into the Germany – US and Portugal – Ghana matches by streaming videos on the internet, and a quarter watched from mobile devices.

During the matches, these network operations managers were playing their own version of football – constantly running back and forth, trying to find out where networks were slowing down because of bandwidth issues. More than two-thirds (67%) of IT admins even reported having network management problems that were directly related to employees streaming the matches from their computers.

That problem travels all the way up. A sudden spike in bandwidth demand can put a lot of stress on operators’ networks. Network maintenance and service management can become a headache, especially in the event of a site failure during a football match watched around the world. Yet, telcos should take events such as this as an opportunity to consider fundamental questions about network and service management. When bandwidth overwhelms a network, what’s the next step? How can service issues be prioritised in terms of usage and value? Is there any way to know when a failure is going to occur before it actually does?

Mobile subscribers today demand consistently high QoS. A slow or spotty network or data connection is a top reason for customer churn, but the cost pressure on operators has made most businesses perform more reactive, versus preventative, maintenance.

But what if operators could predict network faults and correct them preventively, prioritizing where site failures could have the most impact? By applying machine learning and predictive analytics to network operations, that’s finally possible.

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