Identifying and pinpointing potential network failures and performance issues has long been a matter of educated guesswork, but an emerging generation of predictive analytics tools promises to bring greater accuracy to network reliably forecasts, allowing staff to address and remedy specific issues even before they can even begin affecting network operations.
Predictive analytics is a game-changer, giving CIOs the ability to literally look into the future. "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," says Diomedes Kastanis, head of technology and innovation for Ericsson. "Although we have a lot of automation systems and rules to manage and operate networks, it still it not enough to cope with the intense changing environment and proactively adapt to changing demands."
The shock of the new
Predictive analytics incorporating processes such as machine learning (ML) and artificial intelligence (AI) are relatively new concepts to many CIOs. "It takes time to prove a newer technology to the enterprise market and it is still early in this space," says Brian Soldato, a senior director at cyber security research firm NSS Labs. "Most of the adoption is occurring among security platforms and endpoint technologies that have predictive analytics as a feature."
Predictive analytics has improved significantly over the past few years, thanks to advances in AI and related fields. "Forecasts based on time series data, like network logs, are increasingly accurate and therefore more useful," says Chris Nicholson, CEO of Skymind, an AI developer supporting the open source deep learning framework Deeplearning4j. "The level of accuracy depends on the quality of the data set," he notes. "On some problems, deep learning can achieve a double digit increase in accuracy."
Kastanis says Ericsson is in the process of investigating predictive analytics optimization on its networks. "We are currently experimenting with cutting edge technologies like deep learning, decision theory and semantic reasoning with different technology partners who are best of breed in different components of AI," he notes.
Gianluca Noya, digital network deployment and analytics lead at Accenture, concludes that it's now possible to predict future network behaviors, such as demand and service experience, with more than 95 percent accuracy, requiring a history of only five times the span of the prediction. "In other words, to predict data for the next month, you need five months of historic data," he says.
Advancements in compute power and distributed storage have opened the way to the unfettered use of packet-level network data, yet most network operators have failed to take full advantage of this potentially powerful resource, Noya says. "We see many early trials with investment in technology enablers, but frequently these initiatives are thwarted by the lack of a holistic approach taking into consideration the transformative effect of adopting data-driven operating models," he says.
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