How AIOps helps Nextel Brazil predict and prevent network outages

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Mobile smartphones are playing a significant role in the lives and productivity of people around the world. Consider these statistics about smartphone usage from TechJury.

  • Internet users worldwide who visit the Web on a mobile device: 67%
  • Percent of emails read on mobile devices: 49.1%
  • Smartphone users are addicted to their phones: 66%

Clearly, many people today don’t want to (or can’t) be without their smartphones. And like all telecommunications companies, Nextel Brazil is trying to be as customer centric as possible. We strive to make customer service part of the DNA of the company and treat customers as our primary asset, because they are.

I have a great team of 75 people working with me. There are three shifts working morning, noon and night. We do the best we can to satisfy our customer needs because we know our subscribers depend on their mobile phones to work and live their lives. Every second that there’s a network outage and customers don’t have service, we have to be there for them. That’s what being customer centric means to us, especially in operations.

Reducing network outages and meantime to repair with IBM Netcool

Mean time to repair (MTTR) is the key performance indicator for us. We started our partnership with IBM when we began using IBM Netcool Operations Insight software to correlate alarms and get to the root cause of problems faster. We have more than 25,000 established network elements and multiple management systems being monitored by Netcool. The solution has helped us reduce the MTTR to receive an alarm and solve a problem in the field or with some configuration from 30 minutes to less than five minutes.

Still people don’t even want to wait one minute, never mind five minutes, to get their services recovered. As our services and network increase in complexity, so has the amount of data generated.

After approximately three years of maturing this solution, we started to say, “Hey, we can do better. We can be more proactive to treat the problem. Let’s start looking at the data. Let’s start with some analytics into the data.”

We have to be able to predict and to be prepared for network problems, because we know that they will happen. This is our day to day. We wanted to be better prepared for incidents and be able to make adjustments to avoid a network outage.

Moving from reactive to predictive with AIOps

We began working with IBM Watson technology to implement artificial intelligence for IT operations (AIOps). Watson helps us to categorize all the incidents, so we have a better understanding of what is happening in the network, such as if the outage is due to a utilities problem. More than just knowing we have a problem, Watson tells us why we have the problem. Now we can group incidents together and focus on fixing things at the source.

We’re also working with The Weather Company, an IBM Business to predict weather-related incidents and prevent them from impacting service. Our Network Operations team has a high dependence on utility companies because our cell towers are based on electric power. We will have a problem when they have a problem, and they are very dependent on the weather.

With the Weather Company data, we can correlate and look into historical data and know every time that we have a certain threshold of rain, of wind speed, of soil moisture, or whatever set of parameters, that we will have a problem with cell towers in this region.

If we know that one of these conditions is going to happen in the next 72 hours, we can be more prepared to act. As a result, we might send a small generator or extra batteries to the site to keep it up longer. By better knowing the probability and duration of the fault, we can prepare such that we can help avoid an outage for our customers in that region.

AIOps with Watson and The Weather Company data has helped us complete the journey to being predictive in network operations. It’s a great feeling to know that we don’t just have to wait for something terrible to happen and then react to it. We can actually do something about it before it happens. And this means that our customers who depend on their mobile phone are less likely to be without service.

Watch the related video.

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