Modern network deployments embrace disaggregated infrastructure, and for good reason. The use of cloud services allows service providers to avoid costly upfront capital investments, while also forgoing the hassle of procurement, configuration, provisioning, deployment, and management tasks. There are also significant advantages in treating infrastructure as an operational expense. However, the preponderance of multi-cloud, hybrid, and on-premises deployments presents challenges. These include unique management schemes pinned to individual cloud service providers and underlying heterogeneous networking architectures.
Analytics and observability have the power to monitor infrastructure gaps, and the resulting problems that materialize with application performance, and poor assurance while proactively addressing weak security posture. These tools facilitate higher network performance, better resilience, and greater management agility. Combining AI and automation can further strengthen these capabilities, allowing network operators to harness data at rest and in motion to adapt and optimize performance without human intervention using digital twins to model changes before deployment.
In this piece, I will discuss my perspective on the value of analytics and observability in addressing the challenges faced by communication service providers, and how Nokia offers significant capabilities to balance connectivity cost and performance through its autonomous networks solutions.
Unlocking Actionable Insights Through Analytics
The power of analytics lies in its ability to facilitate meaningful and actionable insights from structured data. Readying that data for use is a critical consideration, including identifying its sources, applying normalization and extraction techniques through natural language processing, creating data lakes and other repositories, and ensuring proper data governance for access control and privacy protection.
With the proper curation of structured data, deep analytics can empower organizations to make better, more informed decisions, mitigate business risks, and fine-tune operational management. It can also boost efficiency, glean deeper insights into customer behavior and market trends, personalize offers for new products and services, and more. From a networking perspective, analytics have the power to reduce downtime through stronger assurance provisions as well as analyze large amounts of cyberthreat data when the appropriate tools are integrated into network fabrics and operating systems.
The Benefits of Observability
Network observability is defined as the proactive process of gathering and analyzing data through monitoring tools to gain visibility into the complex interdependencies that underpin the health and performance of a given network. It also leverages diverse data sources to provide perspectives into the functionality of underlying infrastructure, as well as to identify weaknesses and security gaps. Subsequently, observability solutions are becoming widely adopted, driven in part by their ability to supercharge AI operations and unlock new business value.
There are many measurable benefits tied to observability. For starters, it can provide CSPs with deeper insights and recommendations to remediate issues and proactively improve performance. Additionally, observability facilitates application performance management, risk assessment and scoring, and drastically improved security posture within disaggregated infrastructure, multi-cloud, and hybrid cloud environments.
Why Nokia
Nokia provides depth in both analytics and network observability. From an analytics perspective, Nokia’s Data Suite weaves together a data studio and intelligent engines that aim to reduce the friction in developing AI and machine learning applications; it also facilitates integration into third-party ecosystems to spur innovation.
Furthermore, Nokia’s autonomous network observability strategy is built on five pillars: AI and machine learning, intent-driven orchestration, digital twinning, open APIs and ecosystem integration, and generative AI functionality. In marrying deep analytics and observability, the company is providing:
- Granular control of 5G networks across diverse use cases, including industrial IoT, private cellular, and network slicing
- Proactive maintenance that identifies potential issues before they materialize, maximizing uptime and network availability
- Insights into customer experience and network usage patterns to tailor service offerings for better monetization and lower subscriber churn
Both analytics and observability play an important role in Nokia’s delivery of autonomous networks. To learn more, read the Moor Insights & Strategy research paper “Nokia — Delivering Operational Cost Savings through Autonomous Networks.”
The Future Looks Bright
The deployment of disaggregated networks provides tangible benefits, but it also presents operational challenges. However, deep analytics and network observability can assist CSPs in managing the pitfalls and complexities of these multi-cloud and hybrid cloud environments.
Nokia offers deep and broad capabilities that address both analytics and observability, and its broader focus on reducing operational cost through its autonomous networks platform is significant. Breaking news related to the Stargate Project’s $500 billion investment in future AI data centers is an exciting development. It represents an opportunity for Nokia to lean into its platform to capture enterprise share of wallet.
