The Noun Topology

jinflow has a vocabulary problem the way every grown-up system does: too many words for someone walking in cold, and not enough structure to tell you which words sit on top of which. Signal, Finding, Perspective, Thesis, Verdict, Observation, Explanation, Contributing Factor, Suggestion, Intervention, Scenario, Simulation, Dossier, Wisdom, 50cents, Direction. Sixteen nouns. They each earn their place — but only if you can see the shape they fit into.

This page is that shape. One diagram, five colour bands, the entire vocabulary in one view.

The sentence the diamond carries:

Jinflow fosters Progress by gaining Understanding through Observations, Knowledge, and Data.

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Three feeders, one centre, one forward arm

Most analytics systems flow in one direction. Data comes in at the bottom, it gets cleaned, it gets analysed, it produces findings, and somewhere near the top a chart appears. The user reads the chart. That’s the entire conversation.

jinflow has three feeders converging on one centre — and one forward arm leaving it.

Data rises from the bottom: source layers feed into Signals (typed observations of patterns), Signals into Perspectives (lenses across signals) and Theses (business questions), and these resolve into Verdicts. This is the path most BI tools also walk, more or less. It produces evidence.

Observations descend from the top. An Observation is a signed notice about the business — “we suspect we’re losing money on generic substitutions”, “our supplier base is too concentrated for comfort”. It carries a state: suspected first, then validated, refuted, inconclusive, or resolved once the data has spoken. Explanation and Contributing Factor are facets the Observation carries — not peers — describing the why and citing the specific Understanding rows that ground it.

Knowledge enters from the side. Subject Matter (Wisdom from the pack, 50cents from the tenant, Dossiers grouping them) and activatable patterns (Theses and Verdicts shipped pre-formed) inform what Understanding can defensibly say at every layer. A Wisdom item about the Swiss BAG generic-pricing rule informs how you read pricing findings; the pack’s library of theses gives the tenant pre-formed questions worth asking. Knowledge doesn’t sit on the Data-Observations axis — it constrains how the axis reads itself.

The three feeders converge at Understanding — the resolution. Findings are the atomic rows; Verdicts are the aggregate rows. Whichever feeder asks, all of them read the same currency here. Understanding is where evidence, notice, and knowledge agree on what’s true.

And there’s a fifth name — Progress — that extends out from Understanding toward decision: a Suggestion (what to do), an Intervention (how exactly), a Scenario (what would the world look like), a Simulation (what happens when we model it), and a Direction (the attitude, goals, intent that everything else is validated against). These are how jinflow closes the loop from “we suspect this” to “we modeled the impact and acted”.

The three feeders develop in parallel — a tenant matures Observations, Knowledge, and Data concurrently. The diagram presents them in one snapshot, but no order is implied.

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The diagram

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The five names, briefly

Observations (orange) — what the human notices

Where the C-level walks in. An Observation is a signed notice someone puts their name to: we suspect this matters. It carries a state (suspected → validated / refuted / inconclusive → resolved) that earns truth over time as Understanding either supports or rejects it. Explanation and Contributing Factor are facets the Observation carries — Contributing Factors cite specific Findings or Verdicts, grounding the narrative in Understanding. The Observations name respects suspicion as a first-class state and admits when the world doesn’t decide (inconclusive) — both moves that most BI tools refuse to make.

Understanding (yellow) — the shared currency

Findings and Verdicts. Findings are atomic — one row, one observation. Verdicts are aggregate — one row per (tenant × thesis), summarising whether the thesis holds. The three feeders speak the same language here. Entity + Contract describes how the data shape is stable enough that all three flows can rely on the same artifacts.

Data (blue) — what the data says

Signals scan the data for patterns. Perspectives aggregate signals into entity-level views. Theses ask business questions and gather signal evidence; they judge into Verdicts. This is the path traditional BI also walks — the difference is that in jinflow it’s declarative, not coded, and each layer has a typed contract with the next.

Knowledge (purple) — what the system holds

Two kinds of inhabitants, both pre-formed before Data ever runs.

Subject Matter — always-on. Wisdom (pack-level atoms), 50cents (tenant-level atoms), Dossiers (curated reading guides bundling related entries). Authored by domain experts, lives across tenants and across the analytical layers, anchors decisions at every layer. “Articles in this tag class ignore the standard pricing ceiling.” True wherever the pack runs; you don’t “check it out”, you either hold it or you don’t.

Activatable patterns — opt-in. Theses and Verdicts shipped pre-formed by the pack. The pack’s library of questions-worth-asking and root-causes-worth-recognising. A tenant engages the patterns that match its situation; the unactivated ones aren’t wrong, they don’t apply.

Both are pack-shipped by default; tenants add 50cents and can edit (or tenant-author) any artifact. Every artifact carries a provenance state — 📦 pack-original, ✏️ pack-modified, 🆕 tenant-original — so a reader can tell at a glance where the framing came from.

Progress (green) — what we’ll do about it

A Suggestion is a recommendation in plain language. An Intervention is the deterministic AFS edit that realises it. A Scenario is the AFS branch with one or more Interventions applied. A Simulation is the act of building the Scenario and comparing it to production. Direction carries the steering — the attitude, goals, intent that suggestions / interventions / scenarios are validated against; “more employee happiness, or less salary spend?”. The Progress chain is grounded — every Suggestion traces back through Contributing Factors → Understanding → Data: through evidence that someone signed for, through reasoning that someone defended, through data that the machinery actually saw. That trace is what makes a Suggestion adoptable rather than ignorable.

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What to take away

The thing worth remembering is the shape, not the sixteen names. jinflow runs three feeders that converge at one currency, with a forward arm into modelled action. Once that shape is clear, every concept finds its place — and every conversation about jinflow can start from “which name are we in?” rather than “what’s the difference between a thesis and a verdict?”.

A few practical reading conventions:

• Solid arrows are flow — A produces B, A composes B.
• Dotted arrows are reference — A reads B, A informs B, A cites B. The reference can cross names without consuming the target.
• The thick arrow (Contributing Factor → Suggestion) is the load-bearing seam between understanding and change. Without it, Suggestions would be opinions.
• Feedback loops are not drawn. Simulation results can spawn new Observations; confirmed Verdicts can prompt new Observations; fresh Subject Matter can sharpen existing Contributing Factors. All true, all everyday, none rendered. The forward flow is the spine.

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Where each concept goes deeper

Concept	Goes deeper in
Signal, Perspective, Thesis, Verdict	The Signal (Sense 14)
Observation, Explanation, Contributing Factor	Observation and Explanation (Sense 15)
Suggestion, Intervention, Scenario, Simulation	The Simulation (Sense 19)
Dossier, Wisdom, 50cents	The Dossier system (Sense 13 lineage)
Findings, Verdicts (Understanding)	Both Sense 14 and Sense 15
Provenance (📦 / ✏️ / 🆕)	The Lineage (Sense 36)

For a guided walk through these concepts on synthetic data, visit the inspire training tenant — it teaches the same shape via worked examples instead of definitions.