The Great Telemetry Debate: Why AI-Ready Operations Require a True Data Fabric

If you are leading technology strategy today, you face consequential choices about how to manage your enterprise telemetry. Your decisions determine not only where logs, metrics, traces, and events are stored, but also who controls how operational data is collected, shaped, governed, and put to work in an optimal way for the security, observability, analytics, and AI systems that power your business.

Recent deal activity makes the strategic stakes clear: control of enterprise telemetry is becoming a platform-level priority. Dynatrace acquired Bindplane to extend control upstream into telemetry pipelines. Palo Alto Networks completed its acquisition of Chronosphere, bringing observability and telemetry-pipeline capabilities into a security platform. CrowdStrike acquired Onum after describing real-time telemetry pipeline management as a data foundation for agentic security and IT operations. Datadog's earlier acquisition of Timber Technologies, the company behind Vector, was another signal that pipeline control would become part of the observability platform itself.

At the same time, standalone data management has matured into a category, and some standalone pipeline vendors are expanding into storage and analytics. This evolution may simplify parts of the data lifecycle, but it comes with an architectural tradeoff. Leaders should consider whether combining data routing, storage, and analysis in another point solution expands enterprise choice or creates another destination-centric silo.

The same convergence is also unfolding in the opposite direction. Snowflake completed its acquisition of Observe, bringing AI-powered observability into its cloud data platform, while Databricks announced Lakewatch, an agentic SIEM, and acquired Antimatter and SiftD.ai to deepen its security capabilities. These moves enhance Security and Observability workflows that data platforms can support and reinforce the market signal that vendors are extending more across the data lifecycle. For enterprise leaders, that sharpens the question - can each approach correlate cross-domain telemetry across security, IT, networking, and engineering, and connect that context to governed action without creating another silo?

For CIOs, CTOs, and CISOs, this is more than a period of vendor consolidation. The market is voting with capital: telemetry and operational data management have become strategic platform capabilities. The more important question is architectural. Should control of enterprise operational data live inside an observability platform, a security platform, a general-purpose data lake, or standalone middleware? Or should it serve all of them?

A Data Fabric Starts with the Enterprise, not the Destination

This is exactly the divide the Cisco Data Fabric powered by the Splunk Platform is designed to address. It is not another destination for data, and it is not a standalone point product. It is an architecture that connects distributed operational data to the teams, analytics, and AI experiences that depend on it.

The distinction matters. A pipeline attached to an observability platform can optimize data for observability. A layer attached to a SIEM can optimize data for security. A data lake can retain large volumes economically. Each solves a valuable problem, but none automatically creates shared, decision-ready context for all operational teams including Security, IT, Networking, Engineering, and for the overall business.

Cisco Data Fabric takes a broader view of the lifecycle. It brings together three connected responsibilities: unify data and insights at scale, fuel intelligence with contextualized machine data, and activate AI and agentic operations with governance and human oversight. Splunk serves as the intelligence layer between distributed operational data and AI, helping teams analyze data where it lives, correlate signals across domains, and move from reactive response toward preemptive operations.

The goal is not to force every byte into one place. It is to make the right data available in the right place, with the context and controls required for the job at hand. That is the difference between moving telemetry and managing it as an enterprise asset.

The Middleware Tax is Now an AI Tax

For years, the hidden cost of telemetry pipelines was measured in infrastructure, ingestion fees, and engineering time. Teams built collectors, maintained routing rules, rewrote parsers, and repaired integrations every time a source changed. For many organizations, that created a double tax, one cost to process and move data through standalone middleware, and another to store and analyze it downstream. Those costs have not disappeared. But in the AI era, the larger cost is what brittle pipelines do to the data itself.

Think about a traditional schema-on-write pipeline as a customs checkpoint where every shipment must be repacked into the same container before it can move forward. When the contents change, the forms change. When the forms change, the line stops. Data engineers then spend their time writing and repairing fragile parsing logic, often including Grok patterns and destination-specific transformations, simply to keep data flowing.

That is not just middleware friction. It is an AI blocker. AI systems need timely, high-quality, well-governed data with enough context to understand what changed, what matters, and what action is appropriate. If telemetry arrives late, stripped of useful context, duplicated across silos, or locked into a schema optimized for one destination, the organization cannot create a reliable operational picture. Without that picture, AI may produce answers faster, but it cannot produce better decisions.

The upside? This is a solvable problem. Enterprises do not have to choose between sending everything everywhere and dropping data they may need later. They need intelligence earlier in the lifecycle, flexibility in where data is stored, and a consistent way to find and use it after it lands.

Splunk Data Management: The Control Point for Data in Motion

Within Cisco Data Fabric, Splunk Data Management provides that critical control point for data in motion. Edge Processor lets organizations process data close to the source, while Ingest Processor provides Splunk-hosted processing at ingest. Together, these capabilities can filter, mask, transform, enrich, and route data before it reaches a downstream destination.

This changes the economics and the quality equation at the same time. Teams can reduce noise before paying to store and analyze it. They can mask sensitive fields before data leaves a controlled environment. They can preserve high-value, latency-sensitive signals for real-time investigation while routing lower-value or archival data to more economical storage. Most importantly, they can apply policy earlier when the data is still in motion and before inconsistencies spread across downstream tools.

The control point becomes more valuable when it connects to the rest of the data lifecycle, and the distilled insights from the data. Federated Search helps teams query distributed data without forcing unnecessary movement or duplication. Splunk has also announced Machine Data Lake, currently in alpha, and built-in Data Catalog capabilities to help land, discover, contextualize, and govern machine data at scale. The architecture connects data in motion, data at rest, context, analytics, and action rather than treating each as a separate project.

For security leaders, that means richer signals and more reliable detection. For IT and engineering leaders, it means broader visibility without multiplying pipeline work. For data and AI leaders, it means a more trusted operational foundation for assistants, models, and supervised agents. One data lifecycle can support many teams without forcing every team into the same storage or analytics choice.

The Executive Decision Behind the Telemetry Debate

Market consolidation will continue, and more vendors will describe their pipeline, lake, or platform as the control plane for enterprise data. Leaders should look past the packaging and ask four practical questions.

• Control: Can we filter, mask, transform, and route data at the edge and at ingest, before cost and risk multiply downstream?
• Reuse: Can the same operational data serve security, IT, networking, engineering, analytics, and AI without unnecessary copies or dependence on a single destination?
• Context and governance: Can we preserve lineage, provenance, access controls, and business context so teams can trust what they see and AI systems are grounded in trusted data?
• Action: Can we connect insight to governed workflows and agentic operations while keeping people in control?

If the answer depends on one tool owning all the data, the enterprise may be trading today's pipeline problem for tomorrow's platform silo. A true operational data fabric should expand choice, not narrow it.

The Market has Made its Statement

The latest acquisitions are not a sideshow. They are a clear signal that telemetry pipelines have moved from background plumbing to a strategic layer of the enterprise architecture. Whoever controls that layer influences cost, governance, visibility, and the quality of the data that fuels AI.

But adding standalone middleware or stitching together third-party data shippers and disconnected pipelines will not, by itself, solve the broader problem of driving the business outcomes efficiently at scale. Enterprise leaders need an architecture that connects the full lifecycle from collection and control, to economical storage and federation, to shared context and governed action, to drive actionable insights and action.

That is the opportunity with Cisco Data Fabric powered by the Splunk Platform, which is to unify data and insights, fuel AI with trusted operational context, and activate agentic operations across domains. The telemetry debate is not about which vendor wins. It is about whether your data strategy is ready for the next era of operations.

Ready to see what your data can do? Learn more about Cisco Data Fabric, explore Splunk Data Management solutions, or take a tour of Splunk Platform.