Operators set to ramp up machine learning spend to avoid “irrelevance”

Big data, machine learning

Mobile operators will devote more than $50 billion to big data and machine learning analytics over the next five years to avoid becoming irrelevant, according to new research.

Spending on the Hadoop ecosystem, SQL/NoSQL data management and orchestration platforms will continue, ABI Research has forecast, but the biggest growth will come from predictive analytics to improve business performance.

Legacy, rule-based analytics solutions are no longer able to keep pace with a number of areas, meaning the appetite for predictive analytics based on machine learning is expected to grow at nearly 50 percent CAGR to reach $12 billion in 2021.

Machine learning “excels” at spotting trending anomalies, ABI said.

It cited financially oriented applications, including fraud mitigation and revenue assurance, as key use cases.

Network performance optimisation, real-time management, sales, marketing, and customer experience teams are all set to benefit in the future, it added.

[Read more: Big data to shift focus to predictive analytics as IoT takes hold]

Joe Hoffman, Managing Director and Vice President at ABI Research, said: “Machine learning-based predictive analytics are applicable to all aspects of the telecom business.

“It is important that operators master and internalise these technologies and [do] not rely solely on their vendors’ expertise. Executives that overlook big data and machine learning risk irrelevance.”

He added: “These are exciting times for mobile broadband as we see the convergence of IT and telecoms, virtualisation with software-defined networking, the adoption of artificial intelligence, machine learning, and the ubiquitous coverage of all-IP 4G and 5G networks.

“With the rise of commercial cloud infrastructure and machine learning services, every mobile operator can be a big data company.

“In just a few years, we will see the mobile networks of tomorrow manifest into giant, distributed supercomputers, with radios attached, continuously re-engineered by machine learning.”

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