Seth Brickman, Head of Global Product Platforms, Splunk, Cisco
Machines tell stories. Hamming data centers constantly record every movement, transaction, and connection in a complex tapestry of activity across applications, networks, and devices. But what good is data if it’s not put to good use? Data itself has little value, but when understood in the context in which it was created, it can be a powerful fuel for AI, resilience, and insight.
Telemetry data from network devices is more important than ever to organizations, giving teams a clear way to diagnose incidents, detect security threats, maintain compliance, and optimize business outcomes. Agentic AI requires context, and the journey to unlocking its full value begins with raw, unfiltered signals and ends with business wisdom that creates breakthroughs. Telemetry data isn’t strategic until it’s integrated and contextualized.
To get there, resilience operations centers (ROCs) must bridge network telemetry with broader enterprise data to harness a new generation of AI-powered predictive and proactive network intelligence.
Map your data path to intelligence
To achieve maximum value from machine data, organizations must move up the data, information, knowledge, and wisdom (DIKW) hierarchy.
It starts with raw data such as logs, metrics, and event records with unknown or untapped potential. From there, the data moves into processed, structured, high-level information, such as system reports and dashboards that validate results and paint a clear picture. The next step in the data hierarchy is knowledge. This requires recognizing patterns and trends, including root cause analysis and predictive alerts that provide context around events. And the final step is wisdom, where organizations can use AI or humans to make informed strategic decisions to improve their business, such as optimizing network performance and automating remediation.
A unified data fabric can be the catalyst that quickly propels organizations to the top of the hierarchy, helping them evolve from raw, noisy, and underutilized telemetry data to actionable wisdom. For example, in a fragmented enterprise with thousands of network devices across many physical locations, data centers, and cloud platforms, it can take network engineers hours to diagnose a sudden performance drop. However, by prioritizing machine data and implementing a data fabric architecture, AI agents can instantly correlate all relevant telemetry, saving hours of analyst effort.
This reclaimed time directly impacts a company’s bottom line. Data Fabric not only ensures a smooth and seamless experience for ROC teams, but also enables a deliberate transition from reactive to proactive. Companies can now focus on innovation and mission-critical projects rather than emergencies.
The power of data fabric architecture
In a traditional workflow, network teams check telemetry, application teams sift through logs, and security teams scan for threats, each working independently. Addressing this fragmentation requires a sustainable way to connect all your data. An integrated data fabric provides this connectivity.
A data fabric architecture is a strategic approach to data management that acts as a loom that spins raw signals into intelligence, correlating network events with user activity, application performance, and security alerts.
Operationally, a data fabric architecture can:
- Federate and access your data where it resides. Eliminate the need for large-scale, disruptive, and costly data migrations by connecting directly to the source.
- Normalize and enrich in real time. Transform raw, disparate logs into a common, actionable language and add critical context to data the moment it arrives.
- Create correlations between domains. Connect network, application, and security signals to provide a holistic, end-to-end view of your operations.
Organizations that apply this architecture realize new, untapped value. By embedding NetOps telemetry within the data fabric, ROCs can collaborate more effectively and share trusted insights and correlations of telemetry data across network layers. You can also enable AI-driven systems to detect anomalies in real-time and trigger automated remediation workflows.
This proactive approach also minimizes manual intervention as the network can autonomously identify root causes and apply corrective actions such as configuration adjustments and traffic rerouting. This reduces mean time to detection (MTTD) and mean time to repair (MTTR).
Therefore, troubleshooting has a new rhythm. Governance and compliance will become a built-in feature, not an afterthought. Instead of chasing shadows, teams trace problems to the root, and organizations build a strong foundation for resilience and continuous improvement. The result is a more agile, secure, and reliable network that adapts to changing demands and allows your team to focus on strategic outcomes. This means end-to-end visibility is no longer an aspiration, but an operational reality.
How to achieve network intelligence
Converting network telemetry into true network intelligence requires a deliberate, step-by-step approach.
Here’s how to start that journey.
1. Integrate major data sources
The foundation of intelligence is high quality data. Start by cataloging all relevant telemetry sources across your environment, including network infrastructure, application servers, cloud workloads, security tools, and IoT devices. Transforming disparate logs, metrics, and events into a common standard schema ensures cross-domain data consistency and makes it ready for advanced analytics.
2. Normalization and standardization of telemetry data
Once the data is integrated, set up a pipeline that ingests and enriches the telemetry with important metadata such as device location, event type, and user impact, while filtering out operational noise. By bringing AI-native assistants directly into your existing workflows, you can deliver these insights to network engineers and IT operators exactly when they need them.
3. Adopt a supervised agent playbook
To reach the next level of operational efficiency, move from manual intervention to supervised automation. Use AI-powered agent playbooks to suggest solutions for common network incidents. Maintain complete control by keeping a human expert in the driver’s seat to verify, interpret, and approve AI-driven actions.
Autonomous operations, self-healing networks, and AI-native assurance rely on context-rich data that flows seamlessly across all layers. A unified data fabric provides a critical piece of the puzzle, helping organizations anticipate risk, ensure uptime, and enable AI. By beginning to transition from disparate, fragmented data to connected, resilient systems, organizations can future-proof their environments and strengthen their digital resilience.
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This post was created by Splunk. Insider Studio.