The explosion in connected IoT devices, combined with telecom networks’ transition to 5G, has created new opportunities and challenges for telecom operators and service providers. Meeting those challenges requires solutions that are inherently intelligent and that operate much closer to the assets within telecom networks and the industries they serve.
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Device certification and provisioning processes are impeded by the lack of process visibility and control required to achieve high speed and reliability. Elongated test and certification routines delay the release of new products to the market.
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Lack of embedded intelligence makes it difficult for operators and device OEMs to acquire the real-time performance insights that would improve quality of service (QoS) performance to their customers.
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The absence of multidimensional behavioral algorithms to produce the recursive analysis, learning, training, and processing required to enable predictive modeling on asset performance, customer trends, etc.
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Lack of real-time insights, unscheduled downtimes, and static maintenance routines all combine to create network hardware infrastructures that perform below their optimum capability.
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Legacy network security applications that do not provide the AI-enabled, Zero-Trust capabilities required to fully protect networks, devices, and customers.
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Several technological and operational limitations need to be overcome for telecom operators, MVNOs, OEMs, and CSPs to create new streams of revenue that capitalize on advancements in AI, ML, 5g, and IoT.
Take a look at how Plasma’s Solution manages 40+ million devices, and their data analytics, for one of the largest global telecom operators.
By moving intelligence enablement and training much closer to broadband and wireless networks, their assets, and their users, Plasma’s solutions provide a more transparent, and more interconnected, approach to managing today’s complex telecom infrastructures.
Intelligent network optimization that gives operators the ability to optimize network efficiencies, prevent failure, improve device to network visibility, and predict future bandwidth requirements.
Ability to innovate new offerings based on predictive insights into future trends in IoT development, cyber threats, fraud types, and customer demands.
Self-learning security protocols embedded at the network and asset levels that deliver state-of-the-art security to operators, OEMs, and customers.
The ability to visualize and analyze data from networks, devices, customer interactions, and service trends accelerates the development of new products and services and improves customer retention.
Cost reduction across the entire telecom ecosystem via the introduction of higher levels of intelligence, visibility, automation, control, speed, agility, and security.
