Every company in Lumino carries a Data Quality Index from 0 to 100. It doesn’t grade the company — it grades our evidence about the company: how it’s proven, how complete it is, and how rich the picture is. This page shows exactly how the number is built, because a quality score you can’t inspect isn’t worth trusting.
methodology version: dqi_v2Marketing and IT each earn their own score from the same three elements. The company DQI is simply both halves of one ring — each function fills its side, and the center shows what they make together. A function with no published data leaves its half empty, and the whole ring shows it.
Shown here: a typical US public discloser — strong Reported marketing on the left, externally-modelled IT on the right, meeting in the middle at 84.
Same three questions for both functions. What counts toward each answer differs slightly — because marketing evidence and IT evidence come in different shapes.
How is each number proven?
Every published figure carries a tier. Evidence scores the tier mix of a company’s data — weighted so its most important metrics count most.
The tier of the marketing spend figures.
The tiers of revenue and IT spend together.
How much of the expected picture exists?
Each company is expected to have its core metrics for the 3 most recent fiscal years — including the latest one. Gaps and honest refusals lower Coverage, and only Coverage.
Spend figure present per expected year.
Revenue + IT spend present per expected year.
How rich and coherent is the picture?
Beyond the headline numbers: corroborating sources, supporting signals, and internal consistency. Two companies can share a tier mix and still differ in how much surrounds the numbers.
Scope precision, corroboration, year-over-year consistency.
Grounding specificity, confirmed signals (tech capex, transformation programs), segment detail.
Inside the Evidence element, each published figure earns credit based on its tier. A disclosed number is worth more than a derived one, which is worth more than a peer-based estimate.
Modelled data earns real credit deliberately — a reviewed, versioned, benchmark-grounded estimate is genuine value, just weaker evidence than a disclosure. Zero is reserved for absence, not honesty.
Instead of grades like “good” or “poor,” DQI bands tell you what kind of evidence dominates. A luxury house that discloses nothing isn’t failing; our data about it is simply built differently.
Assemble one function’s evidence below — click each cell to cycle its tier — then choose what the company’s other function looks like, and watch both halves of the ring make the company score.
Click a cell to cycle its tier: missing → Modelled → Inferred → Reported
Left half: the function you’re building. Right half: the preset. The center is their meeting point.
Three archetypes from the actual data landscape — the same company can carry very different evidence on its two functions, and the ring is designed to show that, not smooth it over.
Discloses advertising spend in its 10-K every year. Revenue Reported; IT spend externally modelled — like most of the industry.
Doesn’t break out marketing — an honest non-disclosure with a peer-modelled estimate. Revenue fully Reported with real segments.
No public filings exist. Everything is honestly modelled from peers and benchmarks — or honestly refused where even that isn’t grounded.
Only human-reviewed, published figures — and reviewer-confirmed signals — ever influence a score. Nothing unreviewed touches it.
Every score records the methodology version and the exact inputs it was computed from. When a score changes, the reason is inspectable.
The score is never a bare number. Every place it appears, it opens into its three elements — and each element opens into the actual data behind it.
A refused estimate lowers Coverage — there’s no number — but never Evidence. Declining to guess is the highest-quality choice available.