Home › Guides › Procurement dataset qualification
Guide · Procurement, AP & spend data
Procurement suites, AP-automation platforms, spend-management and expense tools, e-invoicing networks and supplier portals all record what organisations actually buy, from whom, at what price and on what terms. That record does not appear in anyone’s financial statements. Whether it is licensable depends less on how big it is than on how well it can be described, trusted and legally used.
The SEC describes “alternative data” as information not contained within companies’ financial statements or other traditional sources (SEC press release 2021-176, “SEC Charges App Annie and its Founder with Securities Fraud” (Sept. 14, 2021)). Observed prices, volumes and payment behaviour fall into that description. Commonly discussed uses include price and inflation benchmarks by category, supplier risk signals, demand indicators and training or evaluating software that reads invoices or runs procurement workflows. Whether a specific buyer wants a specific dataset is decided case by case; this guide does not predict it.
| Dimension | What gets asked | Why it matters |
|---|---|---|
| Coverage | How many distinct buying organisations, suppliers, categories and regions? How concentrated is it? | A trend drawn from a handful of large customers says more about them than the market. |
| History | How many years, continuous, with what gaps? | Evaluation usually means testing against past periods; short or broken history limits that. |
| Timeliness | How long from transaction to availability? | Lag decides whether data can inform decisions or only describe the past. |
| Granularity | Header-level or line-item? Unit prices and quantities? | Line items with units are what make price comparisons possible. |
| Normalisation | Are supplier names resolved to entities? Are items mapped to a standard category taxonomy? | Raw free-text supplier and item names cannot be aggregated reliably. |
| Point-in-time integrity | Can you show what was known on each date, and flag later corrections instead of silently overwriting? | Silent back-fills make historical tests look better than reality. |
| Panel stability | How do customers joining and leaving the platform change the series? | Customer churn can look like a market movement unless it is disclosed and adjusted for. |
| Rights & personal data | Do contracts permit the use? Which fields are personal? | Without clean rights nothing else matters. See the readiness guide. |
The UK Government Data Quality Framework uses six core dimensions defined by DAMA UK: completeness, uniqueness, consistency, timeliness, validity and accuracy (UK Government Data Quality Framework (data quality dimensions as defined by DAMA UK)). Reporting a figure for each on the fields you would deliver — for example, the share of invoice lines with a unit price, or the duplicate-supplier rate — is a far stronger opening than a row count.
This is the document that lets a buyer decide whether to ask for a sample. Everything in it should be aggregate and non-confidential.
| Section | Include |
|---|---|
| Scope | Record types, date range, refresh cadence, typical lag. |
| Coverage | Counts of buying organisations, suppliers and categories in bands; regional and industry mix; top-customer concentration. |
| Fields | Data dictionary for deliverable fields; which are normalised and to what taxonomy. |
| Quality | A measure for each of the six dimensions on the key fields. |
| Change history | How corrections, restatements and customer churn are recorded. |
| Rights | Summary of the contract basis for the use, and what is excluded. |
| Privacy | Personal fields identified and how they are removed or aggregated. |
| Security | Available evidence (for example, a SOC 2 report) and proposed delivery method. |
Who is writing this. MyDataWorth is an independent introduction desk. We are not a data buyer, a marketplace, a law firm or an agent of any buyer, and we never receive, hold or resell a company’s data. We review whether a company looks ready for a data-licensing conversation and, only with its written go-ahead, introduce it to programs that license business data. When an introduction leads to an engagement we may be paid a referral fee by that program; we tell you who, and on what basis, before your name goes anywhere.
Ask for a free review of your dataset →
Primary sources, each read on 22 September 2026. Laws and guidance change; check the current version before relying on any of it.