Network transformation is the catalyst to open up a multitude of rewarding opportunities for communication service providers (CSPs) to energize the customer experience. With the advanced capabilities and collaboration enabled by network transformation, CSPs can deliver individualized service combinations to create unique, desirable offerings that increase customer engagement, driving greater satisfaction, loyalty, and ultimately, revenue.
Successful digitization empowers revenue growth strategies that create real network value by mining usage metrics and analytics deep and wide across the network stack. As a result, the network itself becomes the most important value-creating link in the customer value chain. Before beginning this transformation process, however, it’s important to gain a better understanding of the tools that are essential to unlocking real competitive advantage and business value.
True transformation requires network solutions that span the entire data pipeline across multi-domain multi-vendor architectures — which cannot be achieved without fully embracing three key technologies: analytics, artificial intelligence (AI) and automation.
Building a framework for network transformation requires a detailed analysis of the business environment, systems and architectures, as well as operational goals and desired outcomes. Thinking through a mix of products and customized services informs the business and technology decisions that define the digital infrastructure roadmap, identifying the gaps and technologies that might fill them.
An important aspect of this planning involves the ability to analyze data across all systems of record — as well as methods for data ingestion and processing, machine learning (ML) and closed-loop automation enabled by AI. This approach provides evidence-based analysis and responses to improve business forecasts. New datasets provide continuous learning and feedback, delivering insights into more granular customer preferences to create personalized products and services, aligning network value directly to revenue.
With valuable insights gained from unified network data, CSPs can optimize speed and throughput, manage high-traffic user impact, partition traffic, or establish pricing tiers for different traffic types. Segmentation and cluster analysis of end-to-end network data also lays the groundwork for more realistic Quality of Service (QoS) and Quality of Experience (QoE) predictions.
To successfully gather, parse and analyze distributed datasets from today’s complex hybrid networks, AI and ML technologies are no longer optional. Historically, the process of sifting through disconnected data buried in myriad customer reports was time-consuming and error-prone, requiring days, weeks or months. But with network intelligence informed by ML and AI-enabled functions, CSPs can constantly monitor and evaluate customer engagement, service performance and the health of the digital network infrastructure.
Network operations can apply AI and ML to detect, correlate and reliably predict anomalies with real-time comparison between field data and centralized data benchmarks to isolate not-so-obvious patterns. This can mean revealing underutilized assets and demand-side mismatches to proactively address issues before they can impact QoS or stopping cyber-attacks as soon as anomalies are detected rather than after a breach is discovered.
Applying the power of AI also enables audience measurement techniques to be continuously optimized for enhanced digital service delivery, changing the way that CSPs compete. With the help of global network topologies, improved decision making, and well-understood dependencies between physical and virtual network elements, network operations can become an engine for revenue growth.
Moreover, the activation of network intelligence creates the foundation for autonomous networking, such as using a digital twin of the network to aid planning, change verification and security analysis for end-to-end service assurance and root-cause analysis. Yet, to fully manage multiple network functions and domains across physical connections, virtual network functions and cloud environments, the introduction of a unified end-to-end automation layer is required.
Advanced Network Automation
Arguably, automation is the fundamental cornerstone on which network transformation is built. Without advanced automation, the network cannot support intelligence techniques that parse usage data, deliver new services and measure key performance indicators (KPIs). Developing and exploiting digital infrastructure exponentially increases operational complexities that only autonomous networking can solve.
To allow digital infrastructure to work as effectively as possible, the network needs to be self-managing with automated policies, AI capabilities, predictive analytics and real-time performance management. This requires transitioning manual processes to software applications that can automate those tasks reliably and repeatedly, leveraging the DevOps practice of Continuous Integration and Continuous Delivery (CI/CD).
Yet, with millions of lines of code, it can be impossible to index and reuse code without categorizing it into functional groups. Discrete, containerized microservices overcome that operational overhead with a structured architecture that makes it easier to debug and keep track of functional tasks. Containerized designs are more conducive to CI/CD practices, allowing developers to reuse proven microservices to withstand the rigors of production.
These modular, containerized microservices can be used to group traditional microapplications from multiple domains all the way to the edge of the network. By moving compute proximity closer to the edge, CSPs can better respond to customer expectations and traffic spikes for seamless connectivity that meets QoS expectations. Furthermore, containers create streamlined environments that include dependencies needed by the application, such as specific programming language runtimes and other software libraries.
Transformational Network Technologies and Services
The modern network operations center (NOC) needs to control, orchestrate and effectively manage complex digital infrastructure at scale, requiring increased service availability and quality. By automating repetitive tasks and designing self-healing systems that can quickly recover from unexpected events, NOCs can deliver more secure applications and solve problems faster with greater operational transparency, preventing outages rather than reacting to them.
Successful network transformation initiatives enable CSPs to continuously enhance and evolve their existing business and ecosystems, driving down operational costs while boosting revenue to achieve peak profitability. As the pace of technology disruption increases, it’s vital to engage partners who understand the evolution of your business and the technologies that enable innovative digital experiences to meet customer expectations. Fujitsu network experts have the experience and know-how to skillfully support your network transformation journey with reliable solutions for hardware, software and services. Our deep network expertise can help you create digital experience roadmaps, develop integration strategies and build networks with best-of-breed technologies for any transformation goal.
To learn how to use analytics, artificial intelligence and automation to power your network transformation and achieve more success with fewer resources, read our white paper: