How we assess a data asset — and why we won't invent a number.
TokenizeScore answers three questions about a data asset: whether you can release it, how valuable it is and why, and how to commercialise it. Every judgement traces to recognised standards — the ICO, the NHS Value Sharing Framework, UK Government and DSIT work, Ofgem, IAS 38, and the International Valuation Standards. The engine is deterministic; the same inputs always produce the same answer.
What a board actually needs to know before commercialising a data asset.
Can you release it?
A Red / Amber / Green verdict across seven readiness dimensions — assessed against ICO-aligned standards, with a named fix-list for anything Amber.
How valuable is it, and why?
A sourced value-driver assessment — rarity, linkage, coverage, richness and more — drawn from the NHS Value Sharing Framework, UK Government data-economy work, and IAS 38 / IVS.
How do you commercialise it?
A readiness pathway and the sanctioned commercial mechanisms (upfront, subscription, milestone, royalty), plus exactly what a formal monetary valuation would require.
Deterministic
Same inputs, same verdict. No model drift, no "because the AI said so".
Sourced
Every threshold and every judgement traces to a published source — regulator guidance or peer-reviewed convention.
Conservative
Where the law is unsettled, we set the threshold on the safe side. Better an honest Amber than a flattering Green.
How we answer “can you release it?” — the same way every time.
- 01Singling-out riskCan an individual be isolated in the dataset on the basis of the released attributes? Assessed against k-anonymity-style group sizes set conservatively to UK statistical-disclosure conventions.
- 02LinkabilityCould the asset be combined with another reasonably available dataset to identify a person? Assessed by quasi-identifier surface and the plausibility of linkage attacks.
- 03Inference riskCan sensitive attributes be inferred for an individual even where no direct identifier is released? Assessed against ℓ-diversity / t-closeness style conditions on sensitive columns.
- 04Lawful basis to commercialiseIs there a defensible lawful basis under UK GDPR for the intended commercial use? Assessed against purpose, consent posture, and the specific counterparty surface.
- 05Governance postureIs the asset's governance fit for the proposed release route? Assessed against TRE / federated-query / controlled-access requirements where applicable.
- 06Counterparty surfaceWho is the asset being released to, and under what accreditation? Assessed against accreditation regimes and contractual controls available to the data holder.
- 07Commercial route fitIs the proposed commercial route (licensing, federated query, controlled access, partnership) consistent with the asset's lawful and governance posture?
How we answer “how valuable, and why?” — without inventing a number.
For each asset we assess the drivers that recognised frameworks identify as the things that make a dataset more or less commercially valuable. Each driver is scored against your asset and paired with concrete, sourced ways to improve it.
Rarity
How scarce is the asset in the market — is there a substitute?
Coverage
Population, geography and time-window completeness.
Richness
Depth of attributes per record relative to the use case.
Linkage
How well the asset joins to other datasets a buyer would pair it with.
Provenance & quality
Lineage, documentation, validation, refresh cadence.
Lawful basis & governance
Whether the route to release is sanctioned and contractable.
Where a defensible monetary figure would need deal-specific inputs we don't have (counterparty, intended use, comparable transactions), we say so — and tell you exactly which inputs a formal valuation under IAS 38 / IVS would require.
Value and risk, in the same data, from one engine.
Richer linkage raises value — and raises re-identification risk. Wider coverage raises value — and raises singling-out risk. The engine surfaces where your asset's value drivers and its disclosure risks are the same features, and points to the commercial route (often controlled access, federated query or accredited release) that captures the value while managing the risk.
Green
A release candidate. The basis on which the asset can be signed off, named.
Amber
Not a rejection — a named, ordered fix-list against your actual data, with the route to Green spelled out.
Red
The asset cannot be commercialised in its current shape, on the basis we name. The engine that says no, and says exactly why.
Every judgement traces to a recognised standard.
ICO anonymisation
Releasability, singling-out, linkability, inference.
NHS Value Sharing Framework
Value-driver model for health-data commercialisation.
UK Gov / DSIT
Data-economy work on commercialisation and access.
Ofgem
Energy-data access and governance guidance.
IAS 38
International accounting standard for intangible assets.
IVS (IVSC)
International Valuation Standards — what a formal valuation requires.
Commercialisation intelligence — not legal advice, not a monetary valuation.
- We do not produce a £/$ valuation of your data. No published framework supports turning a dataset's shape into a defensible number; instead we tell you which deal-specific inputs a formal valuation would require.
- We do not replace a Data Protection Impact Assessment, a legal opinion, or sign-off by your DPO.
- We do not see, ingest or store your underlying records. The assessment operates on the shape of the data, computed in your environment.
- A Green verdict is the basis on which a release can be signed off — it is not the sign-off itself.