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Telecom Business Review | Monday, April 03, 2023
AI and machine learning have a number of advantages in Advanced Analytics for Telecommunications, including fraud detection and prevention through automated systems, a conversational virtual assistant, and enhanced user experiences for telecom company clients through machine learning algorithms.
FREMONT, CA: With the explosion of sensors and executives' increasing comfort level with making data-informed decisions, telcos can now utilize machine learning (ML) and artificial intelligence (AI). In a recent STL report, almost all operators surveyed wanted to leverage these emerging technologies to increase network efficiency; however, operators were at different stages of their data analytics journey.
Telecom operators should, however, focus on developing their advanced analytics capabilities today to establish a strong foundation and base for future AI and ML skills and capabilities in the long run.
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Advanced analytics opportunities are as follows:
In order to assess the value of A3 in a telco's processes, STL Partners identified six kinds of problems that A3 (automation, advanced analytics, and AI) could help with. Sixty percent of the value from A3 will come from the network domain, according to the analysis.
A key challenge telcos face is making sense of complex data, and advanced analytics and machine learning can help identify patterns, diagnose problems, and predict or prescribe solutions.
In this domain, it is reported the largest potential financial benefit (more than $50mn in yearly financial benefit) in two key use cases are:
Management of networks (resources)
Assurance of services: ML and AI can also play a significant role in this area, particularly in the short term. Virtualization and cloudification of networks and network functions are also heavily intertwined with this, meaning that in the next five to ten years, new service assurance products will be needed to support a multivendor, disaggregated environment as well as new edge computing services and maturing IoT applications. That's not to say that advanced analytics is primarily in the "new" stuff; we have found that it is still valuable that can be derived from A3 in service assurance for 3G and 4G LTE, such as by introducing more predictive algorithms, which have not yet been realized. The use of advanced analytics in service assurance will enable more proactive root cause analysis in the future with machine learning.
The next steps: preparing for greater AI and machine learning opportunities
Maintaining clean and unified data: Almost all operators cited that data collection and management is still a challenge. Operators should focus on improving this to ensure that their data is clean, complete, and unified and that their data lakes are accurate and current.
Developing analytics skills and capabilities: In order to fully utilize their data, telecom operators should hire more data scientists or upskill existing employees. In addition to being a capability to leverage internally, for example, within a network, data analytics should also be considered a capability for others to leverage, such as charging customers for insights and outcomes.
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