How network intelligence can help businesses anticipate risks, ensure uptime, and deliver on AI

By Seth Brickman, head of global product — platform at Splunk, a Cisco company

Machines tell stories. Humming data centers are constantly logging every movement, transaction, and connection into an intricate tapestry of activity that stretches across applications, networks, and devices. But what good is data if it is not put to good use? Data on its own has very little value, but data understood in the context it was created can be powerful fuel for AI, resiliency, and insights.

Network device telemetry data is now more important to organizations than ever before, providing teams with a clear means of diagnosing incidents, detecting security threats, maintaining compliance, and optimizing business outcomes. Agentic AI requires context, and the journey to unlocking its full value starts with raw, unfiltered signals and ends with business wisdom that powers breakthroughs. Telemetry data doesn't become strategic until it is unified and contextualized.

To get there, the resilience operations center (ROC) will need to bridge network telemetry with broader enterprise data to tap into a new generation of network intelligence — one that's predictive, proactive, and powered by AI.

Mapping the data path to intelligence

To achieve peak value from machine data, organizations will need to move up the data, information, knowledge, and wisdom (DIKW) hierarchy.

It begins with raw data, including logs, metrics, and event records with unknown or untapped potential. From there, data transitions to information, processed, and structured summaries including system reports and dashboards that validate findings and paint a clear picture. The next step in the data hierarchy is knowledge, which entails recognizing patterns and trends, including root cause analyses and predictive alerts that offer context around events. And the final step is wisdom, where organizations can use AI or humans to make informed, strategic decisions to improve the business, such as optimizing network performance and automating remediation.

A unified data fabric can be the catalyst that quickly gets organizations to the top of the hierarchy, helping them progress from raw, noisy, and underutilized telemetry data to actionable wisdom. For example, in a fragmented enterprise with thousands of network devices across numerous physical locations, data centers, and cloud platforms, network engineers might take hours to diagnose a sudden performance drop. However, prioritizing machine data and implementing a data fabric architecture allows AI agents to instantly correlate all relevant telemetry, eliminating hours of analyst labor.

This reclaimed time directly impacts businesses' bottom-line. Not only does the data fabric ensure a smooth, seamless experience for ROC teams, it enables an intentional shift from the reactive to the proactive. Now, businesses can focus on innovation and mission-critical projects instead of emergencies.

The power of data fabric architecture

With traditional workflows, network teams check their telemetry, application teams comb through logs, and security teams scan for threats, each working in isolation. Addressing this fragmentation requires a sustainable way to stitch all that data together. A unified data fabric provides this connection.

A data fabric architecture is a strategic approach to data management, acting as a loom that spins raw signals into intelligence, and correlates network events with user activity, application performance, and security alerts.

Operationally, a data fabric architecture can:

• Federate and access data where it lives: It eliminates the need for massive, disruptive, and costly data migrations by connecting directly to the source.

• Normalize and enrich in real time: It converts raw, disparate logs into a common, actionable language, adding critical context the moment data arrives.
• Create correlation across domains: It ties together network, application, and security signals to provide a holistic, end-to-end view of your operations.

Organizations applying this architecture will realize new, untapped value. By embedding NetOps telemetry within a data fabric, ROCs can collaborate more effectively and share trusted insights and correlation of telemetry data across network layers. They can also enable AI-driven systems to detect anomalies in real time and trigger automated remediation workflows.

This proactive approach also minimizes manual intervention by allowing the network to autonomously identify root causes and apply corrective actions, such as configuration adjustments or traffic rerouting. This, in turn, reduces mean time to detect (MTTD) and mean time to repair (MTTR).

Troubleshooting therefore takes on a new rhythm. Governance and compliance become embedded features, not afterthoughts. Instead of chasing shadows, teams trace problems to their root, and organizations lay a strong foundation for resilience and continuous improvement. The result is a more agile, secure, and reliable network, one that adapts to changing demands and empowers teams to focus on strategic outcomes. That means end-to-end visibility is no longer aspirational — it's an operational reality.

How to achieve network intelligence

Transforming network telemetry into true network intelligence requires an intentional, phased approach.

Here is how you can begin that journey:

1. Unify key data sources

The foundation of intelligence is high-quality data. Begin by cataloging all relevant telemetry sources across your environment, including network infrastructure, application servers, cloud workloads, security tools, and IoT devices. By converting disparate logs, metrics, and events into a common standardized schema, you ensure that cross domain data is consistent and ready for advanced analytics.

2. Normalize and standardize telemetry data

Once data is unified, set up pipelines that ingest and enrich telemetry with critical metadata, such as device location, event type, and user impact, while filtering out operational noise. By deploying AI-native assistants directly into your existing workflows, you can surface these insights to network engineers and IT operators exactly when they need them.

3. Adopt supervised agentic playbooks

To reach the next level of operational efficiency, transition from manual intervention to supervised automation. Use agentic playbooks that leverage AI to propose solutions for common network incidents. By keeping human experts in the driver's seat to validate, interpret, and authorize AI-driven actions, you maintain full control.

Autonomous operations, self-healing networks, and AI-native assurance depend on context-rich data flowing seamlessly across every layer. A unified data fabric provides a critical piece of the puzzle, helping organizations anticipate risks, ensure uptime, and deliver on AI. By starting the journey from disparate, fragmented data to connected, resilient systems, organizations will be future-proofing their environment and empowering their digital resilience.

Maximize the value of your data in an AI-driven world with Splunk.

This post was created by Splunk with Insider Studios.

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