The scores
Every step of the journey is measured
Metis uses four measurements, and each one answers a different question: how available is the data, how capable is the unit, how sound are its decisions, and are its instruments telling the truth over time.
Four measurements
What is scored, and on what
Two describe the ceiling, two describe what is realized under it.
- 01
The availability profile
- Scored on
- One decision's data, against one instrument
- What it measures
- Whether the data a decision needs exists, can be reached and lawfully used, is true, current, and understood, and is fit for the instrument: ten dimensions in three bands.
- How it is scored
- Each dimension 0 to 10 with evidence, against the level the instrument requires; combined as a weakest link, never an average; the largest gap is the binding gate.
- What moves it
- Closing the binding gate: connecting a source, conforming it, agreeing a definition, capturing what was never recorded.
- 02
The Corporate Intelligence Capability (CIC) score
- Scored on
- An organizational unit
- What it measures
- How intelligently the unit can act today: the highest instrument tier its priority decisions reliably support, from descriptive reporting to agents that act.
- How it is scored
- 0 to 10, anchored to typical availability profiles; the unit's score is the one that holds for the majority of its priority decisions, never an average across them.
- What moves it
- Raising the profiles of the decisions that matter until the next tier's requirements are met for most of them.
- 03
The Decision Quality Score, with decision velocity and decision scale
- Scored on
- One decision, before it is committed
- What it measures
- Whether the decision process was sound: the right frame, real alternatives, reliable information, clear trade-offs, sound reasoning, and commitment to act. Decision velocity adds how fast; decision scale adds how many, how consistently.
- How it is scored
- Six links each 0 to 10, combined as the minimum; latency against the decision's required latency; throughput, coverage, and noise across the people who make it.
- What moves it
- The information link is bounded by availability, so it moves with the profile; the others move with how the decision is framed, argued, and adopted.
- 04
Outcome calibration
- Scored on
- Many decisions of one kind
- What it measures
- Whether stated confidence matches what happened: when an instrument says a thing is likely, is it.
- How it is scored
- Over a population of logged decisions, the stated probabilities are compared with the outcomes at the horizon. A single outcome says almost nothing; a hundred say a great deal.
- What moves it
- Logging every decision with its expectation, measuring at the horizon, and reviewing gaps by cause.
The ten dimensions
Three questions the data has to pass
The availability profile scores ten dimensions in three bands, in dependency order: reach, then fidelity, then fitness.
Can the instrument get it, lawfully
Existence: was it recorded at all, at the grain the decision needs. Accessibility: can the systems and people serving the decision reach it. Permission: may it be used for this purpose, at this grain.
Can the instrument trust and understand it
Quality: is it true to what it claims. Currency: is it current when the decision is made. Provenance: where it came from, who owns it, what happened to it. Meaning: does everyone, human and machine, compute the metric the same way.
Does it serve this decision through this instrument
Relevance: would knowing it change the choice. Sufficiency: enough history, enough labeled outcomes, enough of the rare cases. Shape: consumable without a preparation project.
A dataset with perfect quality that the instrument may not lawfully use has an availability of zero for that decision. That is what weakest link means.
How they relate
The ceiling, and what is realized under it
The four scores are not independent. Two set the ceiling; two show what the unit actually achieves under it. And higher quality decisions mean better outcomes and returns.
The profile bounds the CIC score
A unit cannot be model-ready if its priority decisions' data does not meet the model-ready profile. The CIC score summarizes; the profiles explain. Metis never shows one without the other.
The profile bounds decision quality
The information link of a decision can be no better than the availability of its data, and a decision is no better than its weakest link. So decision quality has a ceiling too, and it is the same ceiling.
Outcome calibration shows the truth over time
Whether the instruments the unit runs are actually right, at the confidence they claim, only shows across many decisions. Outcome calibration is the lagging measure that keeps the other three honest.
Higher quality decisions mean better outcomes and returns. A decision that scores higher on its six links is, over many decisions, right more often at the horizon, and the returns are stated in what those outcomes do to the unit's own metric. Raise availability and decision quality rises with it; raise decision quality and the returns follow.
How Metis raises them
A gap names its owner
When a decision's profile falls short of the instrument it needs, the binding gate says which discipline owns the fix. That is the whole coordination model: the score dispatches the work.
| Binding gate | Who owns the fix | The usual remedy |
|---|---|---|
| Existence | Application development | Build the application that creates the data: capture at the point of decision, a workflow application in place of a spreadsheet, an extractor, an experiment, a collector. |
| Accessibility | Data engineering | Connect the source, or run over it where it sits. |
| Permission | Decision analysis with legal | A purpose register, a consent flow, a contract, per-action policy for agents. |
| Quality | Data engineering | Conformance, a contract with the producer, monitors on freshness, volume, and schema. |
| Currency | Data engineering | Streaming or change capture; a freshness target set by the decision's clock. |
| Provenance | Data engineering | A named owner, end-to-end lineage, a trust grade on the source. |
| Meaning | Decision analysis, then data engineering | One agreed definition per metric, encoded in the semantic layer so people and machines compute it the same way. |
| Relevance | Decision analysis | Re-derive what would actually change the choice; price it by the value of information; stop collecting what would not. |
| Sufficiency | Data science with application development | Capture outcomes and labels in the flow of work; collect the rare cases; run experiments for the interventions history never tried. |
| Shape | Data science with data engineering | Consumption-ready marts, features, indexes, and tool interfaces, so no instrument starts with a preparation project. |
Every arrow is an object in the system. Nothing in the loop lives in a slide or a ticket outside Metis.
Start with your decisions
Bring the decisions that matter and the data you have. The first thing we do is score them; the second is show you what moves the score.