AI DATA FOR YOUDISCOVER FOUNDATION / F1 PRACTICAL TOOL
Source Data Checklist.
Build an evidence-based inventory of every place the organisation creates, receives, stores or transforms data — including systems, databases, spreadsheets, documents and external feeds that sit outside formal architecture diagrams.
Capture source metadata only: purpose, ownership, location, format, sensitivity, dependencies and known concerns. Never enter passwords, access keys, personal record values or other secrets into this public tool.
LIVE COLLECTION WORKSPACE
Build the source inventory.
Start with a blank checklist or load the worked example. Add incomplete records when necessary — the completion score shows what still needs to be confirmed with owners.
METADATA ONLY
Do not enter passwords, tokens, connection strings, encryption keys, personal record values or confidential document contents. Describe the authorised access method; keep secrets in the organisation’s approved credential platform.
Nothing is submitted from this form. Download the CSV before leaving or refreshing the page.01 / INVENTORY CONTEXT
Define the discovery boundary.
Record enough context for someone else to understand who owns the inventory, what it covers and when it should be reviewed.
04 / COLLECTION TABLE
Recorded sources.
| Source / category | Business purpose | Owner | Sensitivity | Frequency | Complete | Actions |
|---|---|---|---|---|---|---|
| No sources recorded yet.Add the first source above, load the worked example or download the blank CSV template. | ||||||
HOW TO USE IT
Collect evidence in four passes.
First agree the boundary: a business process, data domain, migration, report, AI use case or operating area. Ask business teams what they actually use, not only what appears in the application catalogue.
Add sources early even when fields are incomplete, then confirm ownership, protection, dependencies and concerns through interviews and evidence. Export the CSV as the working inventory and review unresolved gaps before profiling or remediation begins.
Set the scope
Name the process, decision, project or data domain the discovery must support.
Define context ↑Ask beyond IT
Include business systems, local files, documents, external data and manual workarounds.
Clarify source coverage ↗Confirm with owners
Validate purpose, access, sensitivity, dependencies, quality concerns and retention.
Clarify system assessment ↗Prioritise next action
Use the completed inventory to decide what to profile, clean, govern, migrate or retire.
Explore Diagnose ↗WORKED EXAMPLE
One process rarely means one source.
The Northstar example begins with customer reporting and AI readiness, but discovery finds four materially different sources: a governed CRM, a manual finance model, an external supplier feed and an unstructured policy library.
Each requires different ownership, protection and quality questions. Load the example to see complete records in the collection table, then edit any field to test how the inventory works.
Customer CRM
Manage prospects, customers, consent and sales activity.
Known concern. Duplicate organisations, incomplete consent status and inconsistent industry values
Monthly margin model
Prepare the monthly margin forecast and executive performance pack.
Known concern. Manual formula changes, version conflicts and unexplained source adjustments
Supplier availability feed
Update product availability and expected replenishment dates.
Known concern. Supplier product codes differ from ERP codes and late files are not alerted
Policy and procedure library
Provide approved operating, risk and compliance guidance to employees.
Known concern. Superseded copies remain discoverable and several documents lack a review date
FROM INVENTORY TO BUSINESS CASE
Know the sources. Then measure what matters.
Use the inventory to select priority sources for profiling and diagnosis, then quantify the operational or project value of addressing the verified issue.