SCOTTSDALE, Ariz., Oct. 11, 2016 /PRNewswire/ -- Mobile broadband operators are ramping up spend for big data and machine learning as they transform into digital service providers. With a long history of handling huge datasets, and with their path now blazed by the IT ecosystem, mobile operators will devote more than $50 billion to big data and machine learning analytics through 2021, forecasts ABI Research. Machine learning technologies will lead operators to profoundly change how they manage the telecom business.
"Machine learning-based predictive analytics are applicable to all aspects of the telecom business," says Joe Hoffman, Managing Director and Vice President at ABI Research. "It is important that operators master and internalize these technologies and not rely solely on their vendors' expertise. Executives that overlook big data and machine learning risk irrelevance."
Machine learning can deliver benefits across operators' telecom operations with financially-oriented applications, including fraud mitigation and revenue assurance, which currently make the most compelling cases. Legacy analytics are rule-based solutions that cannot keep pace with the criminal element, but machine learning excels at spotting trending anomalies. Predictive machine learning applications for network performance optimization and real-time management will introduce more automation and efficient resource utilization. Even sales, marketing, and customer experience teams will benefit as machine learning helps to innovate and reengineer business processes.
Telecom big data solutions include the commercial IT kit; the open source, Java-based Hadoop ecosystem, SQL/NoSQL data management, and orchestration platforms. Spending on this infrastructure will exceed $7 billion in 2021. But the biggest growth and most value comes from using predictive analytics to improve telecom business performance, with machine-learning-based predictive analytics to grow at nearly 50% CAGR and reach $12 billion through 2021.
Leading infrastructure vendors—Ericsson, Huawei, Nokia and ZTE—are delivering big data and machine learning solutions oriented toward network operations. Even Hadoop/NoSQL startups like Argyle Data, and chip vendors, led by Intel and Qualcomm, are delivering solutions pertinent to the telecom operator.
"These are exciting times for mobile broadband as we see the convergence of IT and telecom, virtualization with software-defined networking, or SDN, and network function virtualization, or NFV, the adoption of artificial intelligence machine learning, and the ubiquitous coverage of all-IP 4G and 5G networks," concludes Hoffman. "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 reengineered by machine learning."
These findings are from ABI Research's Big Data & Machine Learning in the Telecom Network (https://www.abiresearch.com/market-research/product/1025057-big-data-machine-learning-in-the-telecom-n/) and Telco Big Data, Analytics and Machine Learning market data (https://www.abiresearch.com/market-research/product/1026044-telco-big-data-analytics-and-machine-learn/). The reports are part of the company's Future Networks sector (https://www.abiresearch.com/market-research/practice/next-generation-network-services/), which includes research, data, and analyst insights.
About ABI Research
ABI Research stands at the forefront of technology market research, providing business leaders with comprehensive research and consulting services to help them implement informed, transformative technology decisions. Founded more than 25 years ago, the company's global team of senior and long-tenured analysts delivers deep market data forecasts, analyses, and teardown services. ABI Research is an industry pioneer, proactively uncovering ground-breaking business cycles and publishing research 18 to 36 months in advance of other organizations. For more information, visit www.abiresearch.com.
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SOURCE ABI Research