Three Improvements That Turn Raw Telemetry Into 1st Class Data Products

Why 1st Class Matters

Everyone’s talking about data products, but most are just APIs wrapped around raw data. A 1st class data product is different. It’s engineered to carry its own meaning and context, travel safely across systems, and feed both humans and AI with understanding — not just numbers.

At NetMinded, we believe that making data self-describing is the critical leap that turns telemetry into intelligence.
Here are three things we’ve learned about how to do it.

Meaning and Context Travel With the Data

Raw telemetry tells you what happened. Context tells you why it matters.

By embedding meaning and context as JSON extensions to existing YANG models, every metric can describe:

• Its intent (“what this number represents”)

• Its derivation (“how it was engineered or scored”)

• Its impact (“what system or process it relates to”)

• Its range (“what are the upper and lower bounds of its value”)

That makes the data self-describing and interoperable — portable between platforms, understandable to both engineers and AI.

Engineered Features Create Shared Understanding

Feature engineering converts raw signals into scored, contextualised features. When those features are defined alongside their meaning/context in the data schema, they become reusable components — true building blocks for data products.

These engineered features are the fuel that lets LLMs and agentic AI systems reason, correlate, and act effectively.
They replace ambiguity with structure — turning data into knowledge.

Federation by Design Enables Safe Collaboration

When meaning and context are built into the product, data can be shared safely.

Using mechanisms like NetMinded’s Shared Message Exchange (SMX) protocol, organisations can federate data products across boundaries — while still respecting access policies and ownership.

That means AI agents and analytics engines can draw insight from federated meaning — not just federated data.

It’s the foundation for true multi-party network operations and shared situational awareness.

Conclusion: Why 1st Class Data Products Change Everything

A 1st class data product isn’t just “better organised data.” It’s data that carries its own expert interpretation — the context and meaning scored by those who know the system best.

When expert teams provide that context alongside the data itself, you don’t just improve dashboards — you transform how AI and LLMs understand the world they operate in.

That dramatically improves outcomes when prompting or orchestrating AI agents.
Because now your agents aren’t guessing what the data means — they know.

In short: A 1st class data product = data + meaning + context + policy. It’s the bridge between expert human understanding and autonomous machine reasoning.

FAQ

Q1. What’s the difference between a regular data product and a 1st class one?
A regular data product exposes data; a 1st class one exposes understanding. It carries meaning and context within its schema so that it can be reused, reasoned with, and federated by both humans and machines.

Q2. How does this help AI or LLM systems?
When AI agents consume a 1st class data product, they receive semantically enriched inputs — not just key/value pairs.
That allows for much more accurate reasoning, prompting, and action, because the intent and context of the data are explicit, not implied.

Q3. What role do YANG models play?
YANG provides the structural backbone.
By extending YANG models with JSON meaning/context descriptors, data engineers can make telemetry self-describing — capturing how and why data was generated alongside what it represents.

Q4. How does federation work?
Federation means that data products — complete with meaning and policy — can be securely shared across organisational boundaries.
Our SMX protocol handles this exchange, allowing trusted data mesh managers to collaborate without losing control or consistency.

Q5. Who benefits from building 1st class data products?

• Data Engineers – gain reusable, validated feature libraries

• AI/ML Teams – receive higher quality, prompt-ready data

• Operations & SRE Teams – achieve faster diagnosis and automation

• Enterprise Architects – enable cross-domain collaboration through policy-based federation