NWDAF through a new lens
5G presents a new paradigm for communication networks and is expected to lead the telecommunications industry into a new digital chapter. This shift in network communication systems is driven by CSP network virtualization, cloudification, and the industrial and societal demand for faster, more efficient, flexible, and secure networks.
5G will unlock a new era of possibilities for businesses. The new products and services we're expecting to see will be automatically provisioned on-demand, on virtual network resources. To achieve this high automation of lifecycle management, CSPs need to manage the performance of the network, analyze problems, and remediate them in real-time through closed-loop orchestration and Artificial Intelligence (AI). In addition, there is a growing need for B2B end customers to visualize network and service data on SLA-monitoring portals. These will include dashboards that update in real-time and produce custom correlations between different datasets, to track the service performance and predict possible anomalies.
At the same time, contemporary networks have become so complex that it is hard to scale efficient operations. The main reason for this is that there is no standard framework for OSS interoperability, and the absence of an effective AI-driven MLOps network analytics solution.
The 5G Network Data Analytics Function (NWDAF) is a standalone core network feature that was designed to solve the challenges that exist in today's networks. It acts as the layer responsible for data analytics and network learning, in a 5G system architecture. 3GPP in its latest rel.17 specification has defined a dozen use cases spanning across the categories of user equipment (UE-related) analytics, service experience analytics, and load and performance analytics.
It's obvious that to preserve their holistic ownership and incumbency in CSP networks, many of the leading network equipment providers offer NWDAF as a closed or ‘black-box’ feature. But with its data holding value beyond network operations, it’s important to consider a broader perspective for use of the Network Data Analytics Function as part of CSPs’“*Analytics and Data Democratization” strategy.
The capabilities of NWDAF are often associated with closed loop service assurance or network automation . However, the most exciting and game-changing possibilities are around user experience, enterprise digital transformation, new Analytics-as-a-Service monetization opportunities, and for NWDAF to be a key component in supporting company-wide AIOps initiatives.
The architecture of the NWDAF supports this approach because its key components can be disaggregated into two logical functions: data management and analytics services. This disaggregation offers multiple benefits that include:
In a recent research perspective, Analysys Mason Principal Analyst is quoted as saying "CSPs must see the NWDAF as a crucial component of their corporate vision for analytics."
The disaggregation of the NWDAF provides several additional benefits to CSPs:
* Analytics and Data Democratization is the ongoing process of enabling everybody in an organization, irrespective of their technical know-how, to work with data & AI tools comfortably, to feel confident talking about it, and, as a result, make data-informed decisions and build customer experiences powered by data.
We believe that a best-of-breed approach will provide the best opportunity for CSPs to fully leverage the disaggregated architecture of the NWDAF and optimize outcomes such as increased customer experience, new monetization opportunities, and the possibility to enable analytics from any network generation as well as supporting broader company analytics initiatives.
At the same time, a flexible, vendor-agnostic, and easily configured low code / no code solution helps improve both the bottom line and the democratization of data & AI.
For example, when it comes to new services, CSPs can look to bind network data with enterprise-specific data and thus provide valuable insights to their enterprise customers and, eventually, benefit from new monetization opportunities like Analytics-as-a-Service.
TM Forum certified professional and M.E.E. graduate with over 20 years of professional experience in major global technology companies, hereunder 15 years in senior sales, management, consulting and presales positions, around CEE and EMEA.
Starting the professional career in Ericsson R&D and followed by tech solution selling and consultative background, with 20 years of experience in Service Providers business. Before joining SAS, running for 6 years OSS/BSS transformation projects around EMEA, as part of HP Solution Consulting Service and OSS leader for Central and Easter Europe. Focused on digital transformation and its applicability on improvement of Communication Service Providers CXO KPI’s and overall digital business strategy.
Now working in SAS EMEA Telco industry team to help companies in the Communications Sector to: Turn Network Big Data into competitive advantage by using AI & MLOps for “real time closed loop network automation” and “intelligent network planning”, to transform existing Networks and prepare for virtualization and cloudifications of Network functions and 5G.
Patrik is an experienced leader on the DigitalRoute team. As Lead Product Manager, he leverages DigitalRoute technology to support customers and partners globally. He previously headed the company’s solution architecture and technical sales teams.
He has years of hands-on experience across telco OSS and BSS, and he also supports enterprises across different industries in adopting new usage-based business models. Patrik focuses on product strategy and helping customers unlock the full potential of their systems and usage data.