Building a Data Platform for 5G Networks

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By Don Alusha | 1Q 2021 | IN-6096

With continued adoption of 5G networks, the industry will see an explosive growth of data. That is in part because 5G provides the foundation for new value creation coming from new vertical industry applications. Use cases like automatic network management, network fault prediction and solution, and personalized user experience remain significant scenarios. But a data platform for 5G networks is needed to build a service indication system for end vertical industry use cases (e.g., augmented reality/virtual reality, smart homes, Industrial Internet of Things [IIoT], Industry 4.0, etc.) and to provide relevant analysis and management capabilities.

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Data Management Challenges

NEWS


With continued adoption of 5G networks, the industry will see an explosive growth of data. That is in part because 5G provides the foundation for new value creation coming from new vertical industry applications. Use cases like automatic network management, network fault prediction and solution, and personalized user experience remain significant scenarios. But a data platform for 5G networks is needed to build a service indication system for end vertical industry use cases (e.g., augmented reality/virtual reality, smart homes, Industrial Internet of Things [IIoT], Industry 4.0, etc.) and to provide relevant analysis and management capabilities.

Further, Communication Service Providers (CSPs) increasingly seek to use data in business processes and decision making for sustainable productivity growth as a source of competitive advantage. Consequently, the foundation of data management is evolving to include new data formats, new analytics, and Artificial Intelligence (AI)/Machine Learning (ML) techniques. This brings about three major challenges:

  1. Insufficient capacity where storage scalability and total cost of ownership become pressing issues as massive data needs to be stored
  2. Isolated data coming from service siloes—this results in dispersion and data isolation and, therefore, low analytics efficiency of multiple data formats
  3. Complex management—massive, diversified data warrants a simple and more automated data life-cycle management.

5G Data Platform Key Attributes

IMPACT


With 5G, IIoT, and AI/ML adoption, CSPs will experience an acceleration of processing and storage requirements of massive data at the edge. Traditional data management platforms saw CSPs purchase from multiple vendors to support different kinds of devices, processes, and cellular generations. That, along with a significant involvement of on-site service engineers, often leads to complex Operations and Maintenance (O&M) and high operational costs. With 5G, CSPs seek one-site solution and delivery, simpler O&M, and unattended operations. Therefore, simple storage that encompasses diversified data access and integrated analytics is bound to be a key foundational pillar of a 5G data platform. So is unified management of several thousand edge data centers and cell sites. For example, AT&T and Telefonica are two operators that have data storage capabilities and a unified data platform strategy based on their network, consumers, and IIoT data sources to enable cost efficiencies and drive innovation.

In addition to having the right tools in place, the success of CSPs will come from how those tools are used. Using a 5G data platform to create new services can be done in myriad ways. But if operators are to be effective at using a data platform, they may have to pursue a path that aligns with their unique circumstances. That is their business, their strategy, and their customers. It will not be so much about a 5G data platform product as it will be about establishing the right operational context; for example, a fitting culture where virtually everyone in the company seeks ways for big data and analytics to enhance business operations. Every CSP and vendor will have a different foundation to build from and a different strategy. However, a common denominator among them all is to invest in data governance capabilities and to increase knowledge of open-source tools (e.g., Apache Spark and Flint for data streaming, Hive for data warehousing, and Hadoop for distributed storage).

A data governance system serves as a one-stop shop facility for data management—from data collection to applying data insights to business decisions. Further, it enables CSPs to adopt industry-wide data processing methodologies and techniques that can be applied universally. Finally, in collaboration with vendors, CSPs can utilize data governance capabilities to build dashboards and visual capabilities that help to improve data development efficiency. To that end, vendors such as Enea Openwave, Ericsson, Huawei, Nokia, and ZTE have enhanced their data platform offerings into full-fledged solutions. In other words, in addition to technology, these vendors are increasingly providing an all-encompassing organizational blueprint that guides CSPs toward the right processes and cultures in their journey to become data-driven organizations.

Becoming a Data-Driven Organization

RECOMMENDATIONS


Data is a source of competitive advantage in an increasingly digital world. Data-driven organizations consistently outperform competitors along key metrics, such as margin profitability, productivity, and sustained growth. Therefore, the shift to data-centric frameworks on a wholesale basis must be at the forefront of every CSPs’ transformation agenda. To that end, many CSPs are already on a journey to unlock value form analytics and data infrastructures. AT&T, Telefonica, and Vodafone are examples of some companies that have internal data strategies in place to enable cost efficiencies and to drive innovation. For the CSP community to compete effectively with a data management platform, they must get three strands right:

  • Institute a data infrastructure that leverages a common data layer for data and traffic management across all technology generations (e.g., 3G, 4G, and 5G) and internal operations. Further, they should seamlessly tie in the business logic at the service layer with data repositories that come from such heterogeneous network deployments.
  • Engineer the data infrastructure so that the platform supports multi-vendor deployments and multi-tenant and multicloud software and extends to many users, such as developers, information technology, and business data scientists. This will enable CSPs to bridge application programming interface–centric and service-oriented data with call data records and granular network key performance indicators that characterize telco networks.
  • Establish the right governance to get the best out of an intelligent 5G data platform. CSPs will then be able to drive a unified data strategy; in other words, to have a diverse set of processes tap into a single data repository for service provisioning via standard interfaces.

With which vendors should CSPs work to build a data platform for 5G networks? There are some key strands to consider on that front. One is the experience that any given provider of a 5G data platform has with the wider ecosystem of suppliers and their respective offerings. A vendor’s willingness to work with other suppliers that are oftentimes competitors to achieve CSPs’ overall network modernization goals is imperative. So is the vendors’ ability to bring some of the learnings from other domains; for example, the relevance of public cloud platforms and how they enhance CSPs’ big data capabilities.

At present, no vendor commands the 5G data platform market. Capturing growth is contingent on vendor positioning, the size of the opportunity at stake, and CSPs’ ongoing appetite for data infrastructure solutions. Ultimately, the end goal behind the adoption of a 5G data platform is to aid CSPs in streamlining their operations, pursuing new revenue streams, and competing as effectively as possible with non-telco players.

 

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