Four Ways Telcos Can Realize Data-Driven Transformation

Telecommunications companies are currently executing on ambitious digital transformation, network transformation, and AI-driven automation efforts.

While navigating so many simultaneous data-dependent transformations, they must balance the need to level up their data management practices—accelerating the rate at which they ingest, manage, prepare, and analyze data—with that of governing this data.

To do this, telcos must reimagine their approach to data architecture: transitioning from legacy, siloed data architectures to a modern data architecture—anchored by a data platform able to integrate data across on-premises and cloud environments, and the network edge.

The Opportunity of 5G

For telcos, the shift to 5G poses a set of related challenges and opportunities.

Large 5G networks will host tens of millions of connected devices (somewhere in the 1,000x capacity compared to 4G), each instrumented to generate telemetry data, giving telcos the ability to model and simulate operations at a level of detail previously impossible. 

This exponential growth in connected devices will force telcos to up their game, first by provisioning the capacity they need to scale and maintain next-gen 5G data networks, and later by improving the effectiveness of their data management and governance practices.

But predictive modeling and machine learning (ML) will enable rapid extraction of meaningful insights from this data, gleaning information about customer preferences, behavioral patterns, and needs, making it possible to transform business operations and services, radically personalize the customer experience, and develop new products and services that just weren’t feasible with 4G.

The consolidation wave

Another consideration is a likely wave of consolidation driven by the desire among larger telcos to distribute the burden of technological investment, leverage economies of scale, gain competitive advantages in existing markets, or expand into new markets.

Consolidation presents perhaps the biggest overall challenge, not only with respect to the complexity of integrating dissimilar IT systems and data platforms, but also that of merging and reconciling business processes and operations. Add to this, too, the difficulty of integrating potentially dissimilar compliance frameworks: for example, separate telcos might be operating under different regulatory guidelines, appropriate to specific jurisdictions or business practices, requiring the merged entity to formalize a single, unified framework for compliance.

These transformations require a major rethinking of data architecture

The onus is on telcos to revamp their data architectures so they can collect, process, and analyze data at or close to real time—i.e., at the network edge—to accommodate the lower-latencies and larger volumes of data in the 5G era and beyond, as well as to make it easier to integrate systems, data, and processes in the event of merger and acquisition (M&A) scenarios. This has a few implications for next-gen data platform architecture:

First, streaming data presents a novel set of data management and governance challenges, requiring a data architecture that’s suitable for low-latency, high-velocity data processing.

Second, telcos must be able to “push out” data processing so it takes place closer to the connected devices that generate telemetry data, reducing data latency and minimizing traffic. This means devising ways to process data at the network edge, as well as making decisions about which data to persist for historical analysis—and which to discard.

Third, telcos must adopt a hybrid data platform capable of spanning the cloud, on-premises, and edge environments. They will need the elastic capacity of the cloud to accommodate the continuous, high-volume data flows generated by 5G devices; the massive volumes of historical data used to feed operational analytics and support long-term planning; and the large, multivariate data sets used to train ML models.

Fourth, by unifying control and visibility across the on-premises, cloud, and edge environments, a hybrid data platform makes it easier for telcos to navigate disruptive changes, like M&A scenarios. By automating data management tasks and supporting a wide variety of access protocols, it accelerates the work of integrating dissimilar systems and processes. And by building in identity and access management (IAM), role-based access control (RBAC), and data governance capabilities, it helps simplify M&A consolidation projects.

Integrating these capabilities into a data platform gives telcos the flexibility to navigate changing conditions while enforcing data security, compliance with regulations, and delivering novel products.

Scaling data engineering

In the telco world, the scale of data engineering has always been constrained by factors like the shortage of skilled data engineers and the limitations of legacy platforms and tools.

A hybrid data platform breaks down this barrier, integrating ML- and AI-based tools that make it easier to manage, integrate, and analyze data, as well as monitor governance and compliance.

It also makes data professionals more productive, providing a rich set of ease-of-use features and exposing a variety of interfaces—like RESTful APIs, query interfaces, and language-specific bindings—they can invoke using their preferred tools. It incorporates features that make it easier to build, test, and deploy data pipelines, as well as schedule and monitor them in production. In addition, it automatically manages dependencies between tasks, keeping track of a task’s progress and ensuring that it completes successfully before triggering any dependent tasks. For data engineers, data scientists, and other experts, a hybrid data platform simplifies access to distributed data, enabling them to design reliable, idempotent, low-latency data pipelines that integrate real-time data from the network edge to feed operational analytics, or ML-powered, AI-automated applications and services. 

A hybrid data platform that’s as close to turnkey As possible

No combination of point solutions or open-source software (OSS) adds up to a turnkey hybrid data platform, especially when taking into account the challenge of integrating new OSS technologies with legacy telco systems.

However, Cloudera Data Platform (CDP) is a best-in-class platform that is 100 percent compliant with upstream OSS projects. CDP is the foundation of Cloudera’s Universal Data Distribution (UDD) vision, which describes a data architecture capable of spanning the on-premises, cloud, and edge environments that breaks down legacy silos and enables transparency and interoperability across distributed environments.

Cloudera DataFlow, one of CDP’s integral components, handles both batch and streaming data, ensuring reliable, “right-time” access to information. CDP includes built-in support for advanced security features like IAM and RBAC, which facilitate secure access to data while safeguarding privacy. CDP automatically enforces compliance policies, continuously monitoring and reporting on data access, changes, and movements. And by automating compliance enforcement, telcos reduce the risk of human error and adhere to regulatory requirements while minimizing manual effort.

And by selecting a best-in-class platform like CDP, they effectively outsource the daunting task of building and maintaining a bespoke hybrid data platform from scratch.

Download the e-book A Hybrid Data Cloud for Accelerated Insight and learn more about the benefits of a hybrid data platform.

Anthony Behan
Global Managing Director, Communications, Media & Entertainment at Cloudera
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