SAS and DigitalRoute, two leading AI and data management companies, have joined forces to deliver a 5G Network Data Analytics Function (NWDAF). The combined solution is an alternative to black-box or hard-coded solutions from network equipment providers (NEPs). It will enable Communication Service Providers (CSPs) to utilize NWDAF to go beyond solely addressing defined use cases that focus primarily on network operations and eliminate the network vendor silo trap.
Data-driven networks present a new opportunity for CSPs to bridge network services to business operations leading the industry into a new digital chapter for B2B services, with new revenue streams. However, modern networks have become so complex that it is hard to scale efficient operations, which dictates that they leverage AI and automation, and enable new monetization opportunities, such as Analytics-as-a-Service.
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CSP data holds value beyond network operations, meaning that NWDAF can be part of the broader analytics and data democratization strategy. Now, everybody in an organization can start working with data and AI tools comfortably, irrespective of their technical know-how. This can drive data-informed decisions across the business and help to build customer experiences powered by data.
“It’s important that the NWDAF, in addition to aligning with 3GPP specifications, comes with a set of common and reusable platform services such as APIs and AI platform components that enable continuous lifecycle management of all AI models running within it. These components can be consumed by additional use cases, beyond those defined and standardized by 3GPP,” explained Adaora Okeleke, Principal Analyst Data, AI and Development Tools Cloud and Platform Services at Analysys Mason. “Several 5G network monetization opportunities lie in the B2B domain and accessing them will require the NWDAF becoming a part of a CSP’s broader advanced analytics ecosystem. It provides key data and intelligent insights that will enable discovery and management of new 5G enterprise use cases.”
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“SAS and DigitalRoute are frontrunners and experts in our respective areas, with extensive experience in building revenue and operational solutions for telecoms that now include NWDAF and automated operations,” said Sasa Crnojevic, Network AI & Machine Learning Business Principal, at SAS. “We understand the value and difference a flexible, vendor-agnostic, and easily configured low code / no code solution provides to the bottom line and how it supports democratization of data and AI across the enterprise.”
The NWDAF architecture supports this approach because of two major components, data management and analytics services, which when disaggregated, mean the NWDAF can coexist with a company’s end-to-end data analytics architecture. Therefore, CSPs can benefit in many ways, including:
- Reduced total cost of ownership and time taken to integrate with the NWDAF, through reuse of pre-existing analytics components.
- Faster innovation cycles and time-to-market through less dependency on costly vendor change requests or complex product upgrades.
- Enabling service agility through co-existence with 2G/3G/4G ML Network Operations.
- Improved insights through data silo elimination (by separating the data collection layer, the same information can be used to support other analytics cases or applications).
- Increased B2B opportunities by combining with analytics functions beyond the network (e.g. banking sector analytics, healthcare, manufacturing, IoT, and edge computing).
“Networks have always carried tremendous amounts of valuable data, and not until now, due to advances in machine learning and data science, was it possible for telecommunication providers to use this data to improve their business offerings,” said DigitalRoute CTO Demed L’Her. “DigitalRoute has always been the gold standard in extracting and processing data at scale. Similarly, SAS has set the standard for deriving intelligence from oceans of data. This combination is a perfect match of two technology giants, each expert at their craft; both focused on NWDAF and its possibilities.”
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