5 Ways Predictive Analytics can Prevent Network Failures

The good news—you can put your crystal ball away. Preventing network failure doesn’t have to be a guessing game anymore. Predictive analytics can help you find these performance issues before they happen.

The ability to find and address network problems before they even begin affecting your operations is giving information technology (IT) professionals the freedom to focus on progressive projects as opposed to reactive ones.

“There is a growing need for networks to adapt to dynamic application demands as well as address dynamically to special events, seasonality and so on,” Diomedes Kastanis, head of technology and innovation for Ericsson told CIO.online.

“Although we have a lot of automation systems and rules to manage and operate networks, it still is not enough to cope with the intense changing environment and proactively adapt to changing demands.”

There’s a reason predictive analytics are so effective. We’re pinpointing five ways that they can help your IT department prevent network failures.

1) Incorporating AI in new technologies is paving the way for failure prevention.

Most of the current adoption of predictive analytics comes from updates to technologies that are already being used. Different security platforms, like endpoint technologies, include updates that leverage artificial intelligence (AI) or machine learning (ML).

It’s technologies like these that have led companies like Skymind to start to adopt the practice, but the technology still isn’t yet fully evolved—only 95 percent accuracy for them.

“In other words, to predict data for the next month, you need five months of historic data,” Gianluca Noya, digital network deployment and analytics lead at Accenture told Networks Asia.

Still, the advancements in computing power, security technology and network data are allowing IT departments to start to figure out how to take advantage of this resource—including anticipating capacity requirements.

2) IT departments can stop spending time analyzing capacity data.

To determine to future capacity of their networks, IT professionals spend time determining a benchmark metric, and continuing to measure against it for comparison. This takes time—staff will spend months trying to gather and project data for several month forecasts, only to find they have to start over when those months roll around.

Instead of spending those months analyzing traffic, services, device use and how employees are using them, predictive algorithms can crunch all of that data for you. Not only can it same time, but it can continuously learn as it does this, beyond what is capable with benchmarks that don’t move.

3) Quality of performance is taken into account.

Learning capabilities also come into effect when you’re trying to analyze quality of performance in the future. Based on past events, deep learning technology can be applied to forecast for the future.

“When you have a dataset that includes records of events you want to predict, you can train a deep neural network on that data,”Chris Nicholson, CEO of Skymind, an AI developer supporting the open source deep learning framework Deeplearning4j told CIO.

“When you can predict capacity problems accurately (for example), you can act pre-emptively to rebalance the load on your network and provision the network with more capacity.”

The more data you have, the better the technology can learn. While there are still some stopgaps here—like data that’s not clean or organized properly—when applied correctly, it can proactively secure your organization.

4) As AI technology learns, predictive analytics learn about attackers as well.

As attackers get smarter, supporting intrusion detection gets tougher, and organizations will soon require predictive analytics to stay ahead.

It’s effective because this technology learns about your system in a more complete way than any other human could. This means it knows what your ‘normal’ looks like, and recognizing anomalous behavior is easy.

This will become very important in industries like banking, where risk mitigation and detection of security breaches is so important. The cost of a security breach is immense—the more quickly you detect them, the less impact to your organization.

5) Predictive analytics will cost you less in the long run.

Network pricing structures can be complicated, but coupling your system with predictive analytics can not only help you save in the event of an attack, but can help you forecast for effectively—like network upgrades, new devices and staff.

The key to success with predictive analytics is to have data that the technology can learn from. A historical look at past problems is paramount to predicting security events in the future.

While this is not a ‘quick-fix’ solution, predictive analytics are a tool for CIOs to better prepare for the future and identify behavioral patterns across all of your systems.

Megan has been writing about enterprise technology, data, infosecurity and environmental technology for several years. Tweet her @MeganRoseM, or check out her blog: www.meganmorreale.com.