Giving Data a Voice: Storyboarding How 1st Class Data Products Transform AI Outcomes

When AI Meets Raw Telemetry

Alex is an automation engineer working in a national optical network team.
Their goal: find out whether a large language model (LLM) can help identify faults in transceiver telemetry faster than traditional dashboards or scripts.

They start with what’s easy — a dump of raw telemetry: optical power, bias current, temperature, FEC counters.
They paste the metrics into a prompt and ask:

“What’s wrong with this transceiver?”

Thirty seconds later, the model replies:

“Check the laser temperature; it might be drifting.”

It sounds reasonable — but it’s wrong.

When Data Has No Voice

Alex runs a few more tests. Each time, the answers change. Sometimes it’s “dust on a connector.” Sometimes it’s “an unstable module.”
The longer the prompt, the slower and vaguer the response.

It becomes clear what’s happening: the AI is trying to interpret data that carries no meaning.
The data lists numbers but tells no story.

Without context, the AI is reasoning in a vacuum.

And this isn’t just Alex’s problem — it’s the challenge across every observability and telemetry system today.
Telemetry shows what happened, but not why, how, or what it means.

As a result:

• Every question takes longer to process

• Every answer is uncertain

• Every insight must be re-interpreted by a human expert

The data is silent.

Giving Data a Voice

Alex teams up with the optical data specialists and uses MNOC — NetMinded’s data-product platform — to give the data a voice.

Together they create a 1st class data product:

• They extend the YANG model for the transceiver with JSON-based meaning and context.
  
  

• Each metric now describes what it measures, its normal range, dependencies, and confidence score.
  
  

• They design engineered features such as Optical Path Stability and Degradation Confidence, combining multiple raw metrics into single, scored indicators of network health.

Now, the data is self-describing — it explains itself.
Context travels with the data, ready for both human reasoning and AI orchestration.

From Speculation to Understanding

Alex re-runs the experiment, this time using the 1st class data product — the metrics, meaning, and engineered features together.

AI Response:

“Optical path degradation detected. Power variation and FEC trends suggest connector loss at the remote end. Confidence: 92%.”

Three seconds instead of thirty.
No hesitation. No random guesses.

Now, the AI speaks the same language as the engineers — because the data finally has a voice.

The Breakthroughs of Voiced Data

By giving data a voice through MNOC, Alex and the team achieve three breakthroughs:

Speed

AI reasoning time drops from ~30 s to ~3 s because the context is built in, not inferred.

Accuracy

Answers are explainable and repeatable. The AI stops speculating and starts diagnosing.

Reusability

Using MNOC’s Shared Message Exchange (SMX), the same voiced data product can be safely federated across organisations — spreading shared understanding without losing control.

Why Raw Data Fails AI

AI is powerful at reasoning, but only when the data carries its own semantics — the relationships, intent, and impact of each value.
Without that, every LLM prompt becomes a guessing game.

A 1st class data product changes this by encoding meaning alongside the metrics:

• What the number represents

• How it was engineered or scored

• Its expected range or thresholds

• Its relationship to other signals

That’s how AI moves from speculation to understanding — and how telemetry becomes intelligence.

FAQs: Turning Telemetry into Shared Understanding

Q1. What does “giving data a voice” mean?
It means embedding meaning, context, and engineered features directly into your data model, so that every metric can describe itself — its purpose, dependencies, and expected behaviour.

Q2. How does MNOC make that possible?
MNOC extends YANG models with JSON-based meaning/context descriptors and integrates feature-scoring pipelines.
This creates 1st class data products that can be consumed directly by AI or shared through the SMX federation layer.

Q3. Why does it speed up AI response times?
Because the AI no longer has to infer relationships between unlabeled numbers.
With context attached, the model reads meaning directly — turning 30 seconds of speculative reasoning into 3 seconds of informed understanding.

Q4. What’s the connection between feature engineering and “voice”?
Feature engineering captures expert insight in mathematical form.
When paired with MNOC’s context extensions, those features gain semantics — they speak the language of the domain.

Q5. Who benefits?

• Automation engineers – faster, more confident AI diagnostics

• Data teams – reusable, policy-controlled feature libraries

• Network operators – federated situational awareness via SMX

AI developers – prompt-ready data that improves reasoning accuracy

What “Giving Data a Voice” Really Means

A 1st class data product isn’t just clean data — it’s data that carries its own explanation.
When expert teams describe why a signal matters and embed that meaning alongside the data, AI agents can finally understand rather than infer.

Giving data a voice means transforming silent telemetry into something that speaks for itself —
letting AI move from speculation to shared understanding.

That’s what MNOC does:
it turns silent data into conversational intelligence.

At NetMinded

We help organisations give their data a voice — so that humans and AI can understand each other through shared meaning, context, and trust.