KX-14 · Commercialization
Data Asset Valuation Engine
Assess the strategic value of a programme's data assets independently of claim status.
Strong Scientific InferenceModel-Inferred
Separates the question of whether a dataset is valuable from whether a claim about it is true — negative and null datasets are frequently the more durable asset.
Institute module developed within the KRYOS v6 framework ecosystem. Established scientific methods retain their conventional attribution.
When to use
During due diligence, portfolio review, or partnership scoping.
When not to use
As a proxy for scientific validity.
Required inputs
- Data inventory with provenance
- Collection cost basis
- Rights and ownership position
Optional inputs
- Comparable market context
- Reuse constraints
Assumptions and constraints
- Valuation reflects replaceability and rights, not claim excitement.
Process steps
- 01Inventory datasets with provenance and completeness scores.
- 02Assess replaceability and cost to reacquire.
- 03Assess rights position and reuse constraints.
- 04Identify defensible derived-asset opportunities.
- 05Report value drivers with stated uncertainty.
Human review gates
- Commercialization Lead and counsel review the rights position.
Primary output — Data asset valuation with drivers and uncertainty statement.
- dataset_id, completeness, replaceability, reacquisition_cost
- rights_position, reuse_constraints[]
- value_drivers[], uncertainty_statement
Failure signals
- Valuation rises purely because a claim became more exciting.
Quarantine triggers
- Rights position unclear or contested.
Mini prompt
Inventory datasets with provenance and completeness. Assess replaceability, reacquisition cost, and rights position. Report value drivers with explicit uncertainty. Do not use claim status as a value driver.