Rohan Light

Question: What would others realistically pay to use this data? Level 1 dimensions 1. Observed pricing / deals 2. Addressable market size 3. Exclusivity vs competition 4. Licensing model and constraints Level 2 indicators and observables * Observed pricing / deals * Actual licence fees earned to date (per record, per API call, per

Question: What does it cost to produce, maintain and protect this data? Level 1 dimensions 1. Acquisition / production cost 2. Processing / storage / maintenance cost 3. Quality management cost 4. Compliance / security overhead Level 2 indicators and observables * Acquisition / production * Annual spend on collection channels (sensors, surveys, vendors). * Internal labour time

Key Question: What is the net financial contribution from this data? Level 1 dimensions 1. Revenue uplift 2. Cost savings / avoidance 3. Risk reduction / capital efficiency 4. Net value after costs and risk Level 2 indicators and observables * Revenue uplift * Incremental revenue attributable to data‑driven decisions (e.g. price

This is the third post of six going into building a table of observables for each of the Infonomics metrics. The aim of this set of posts is to help build the management case that will enable capitalisation of a data entity/object rather than expensing it. I'm

This is the second of six posts, where I identify how to assess the business value of information (BVI, from the Infonomics schema) from what can be observed within the organisation. As raised in a comment, this set of observables are just meant to get us off the ground internally.

At some point we will want to turn the six Infonomics metrics into something an analyst can score by listing what they can see and count. For each metric, we can organise the observables into a simple hierarchy (Level 1 dimension → Level 2 indicators → Level 3 measures). Below is a