From Dashboards to Decisions — How Grafana + MNOC Turns Metrics into Meaning

Most teams already use Grafana to see what’s happening. Few use it to understand why. That’s the gap MNOC was built to close — a lightweight, open-source layer that turns time-series metrics into self-describing, shareable data products. It’s observability that actually thinks.

The limits of dashboards

Dashboards are brilliant at showing you what’s going on. But when a chart turns red or an alert fires, someone still needs to ask:

“Is this real? Is it important? What should we do?”

That last mile — turning raw signals into decisions — is where most observability systems stop short. They monitor, but they don’t interpret.

That’s fine for single systems, but in modern operations; across cloud, optical, and access networks, the volume and diversity of metrics make human interpretation the bottleneck.

Enter MNOC: the feature-engineering layer for Grafana

MNOC (Multi-party Network Operations by Collaboration) was built for exactly this gap.It sits between your data collectors and Grafana dashboards, transforming raw telemetry into scored, contextualised data products.

Think of MNOC as a kind of feature-engineering engine for observability — one that standardises how meaning and context are embedded in your data before it ever hits the dashboard.

In practice, that means:

• Scoring rules that express business logic (e.g., what counts as “healthy traffic”)

• Embedded metadata that describes why a signal changed

• JSON/YANG extensions that let the data carry its own description wherever it travels

A simple example: the “vampire threshold”

Suppose Grafana shows an interface marked “up” but traffic looks suspiciously low. MNOC can calculate a rate score that compares live throughput against an engineered “vampire threshold” — the minimum expected utilisation that distinguishes a healthy link from an idle one.

For instance:

• Raw telemetry: 200 bits/s

• Threshold: 20 kbps

• MNOC score: amber — interface up, but below expected activity

The result? Grafana now visualises not just state (up/down) but behavioural meaning (healthy, degraded, idle). As MNOC attaches this scoring context as metadata, downstream systems — AI agents, automation scripts, or partner dashboards — can all interpret it the same way.

Meaning that travels with the data

Traditional telemetry is “mute.” It tells you what happened but not why it matters.

MNOC enriches each metric with context fields like:

• Intent – what the metric represents

• Derivation – how it was computed or scored

• Impact – what service or process it affects

• Range – expected upper and lower bounds

These become part of the data record itself. So whether the data lives in InfluxDB, Prometheus or is shared via API, it carries meaning that both humans and AI can use without needing a lookup table or tribal knowledge.

Why Grafana is the perfect partner

Grafana has become the common language of observability — an open, pluggable canvas for everything from SNMP to OpenTelemetry.

Adding MNOC as a preprocessing layer, you are able to:

• Prototype scoring profiles directly in Grafana transformation

• Push those profiles into MNOC for continuous scoring

• Feed back richer, self-describing data for dashboards, alerts, or AI models

The result is a tighter loop between visualisation, understanding, and action.

What this means for DevOps and AIOps teams

• Faster triage: You see why something changed, not just that it did

• Fewer false alerts: Scoring smooths noise into meaningful states.

• Shared interpretation: Teams across vendors and domains can speak a common data language.

• AI-ready telemetry: LLMs and agentic AI can reason over data that already includes intent and derivation.

It’s a small change to how you engineer your data — but a big step toward self-describing observability.

Join us for the first open-source release

We’re getting ready to release MNOC’s first open-source version — designed to slot straight into Grafana environments and help technical teams start building their own feature-engineering pipelines.

If you live in the Grafana ecosystem and want to explore what’s possible when your data carries meaning, watch this space (and our GitHub) in the coming weeks.

FAQs for SEO

What is MNOC?
MNOC (Multi-party Network Operations by Collaboration) is an open-source system that transforms raw telemetry into scored, contextual data products designed for observability and AI-driven automation.

How does MNOC work with Grafana?
MNOC preprocesses telemetry data — scoring, adding metadata, and applying business rules — before visualisation in Grafana, making dashboards more meaningful and automations more reliable.

What is a “vampire threshold”?
It’s a simple scoring rule that flags network interfaces that are technically “up” but carrying little or no traffic — a sign of potential misconfiguration, congestion, or idle capacity.

Why are self-describing data products important?
Because they make telemetry portable and interpretable — usable across different tools, vendors, and AI systems without custom code or human translation.

Who should use MNOC?
DevOps, SRE, and network engineering teams who want to make their observability data smarter, sharable, and AI-ready.