Audit beginner Works with: Any AI

Event Registration and Attendee List Data Audit

Table output

Check an attendee or registration export for duplicates, missing fields and unusable entries, with counts, cleanup steps and a privacy check.

The Open buttons send the prompt text as currently shown below to that site only when you click. Fill in the placeholders first.

[VERIFY] is an editorial flag, not a blank to fill. Wherever the prompt shows [VERIFY], check that item against a reliable source before you use it.

Please audit the registration data for my event. I will paste an anonymised or sample export and want you to find problems before I use it for name badges, headcounts and emails.

Inputs: column headings and up to 40 sample rows [SAMPLE_ROWS], the number of rows in the full list [TOTAL_ROWS], the venue capacity [VENUE_CAPACITY], the fields I actually need for the event, such as dietary needs or access needs [FIELDS_NEEDED], and the date of the event [EVENT_DATE].

Report: (1) a data quality table with columns Check, Result, Count in sample, Example, Fix. Checks should cover duplicate people, duplicate emails, empty required fields, inconsistent name formats, invalid-looking emails, ticket-type mismatches, and entries after the closing date. (2) An estimate for the full list: scale the sample counts to the full list, showing the arithmetic and labelling these estimates only. (3) A headcount reconciliation against the venue capacity with the remaining or excess places. (4) A privacy check: which collected fields are not needed, whether sensitive items such as health or access needs are collected and stored sensibly, and what to delete after the event. (5) A cleanup plan in steps, applied to a copy of the data.

Guardrails: do not delete or merge entries yourself; list candidates only. Mark any data protection obligations as [VERIFY] and suggest checking with the organisation's privacy contact. This is practical review help, not legal advice.

Add a worked example with invented figures: 40 sample rows with 3 duplicate emails, full list of 400 rows. Estimate about 30 duplicates and state the caveat.

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