article · operations
Prepare business data for a first AI workflow
Prepare approved sample data using field validation, source ownership, explicit missing values, synthetic records, and a careful redaction procedure.
The direct answer
Prepare only the fields a bounded workflow needs. Give each source an owner, validate fields before model use, and build a synthetic or carefully redacted sample set that preserves the difficult cases. Missing information should remain explicit rather than be filled with guesses.
A sample intake record
Synthetic input: intake_id=I17, name=Sam Sample, email=sam@example.invalid, service=onboarding, request=Please discuss a guide setup, received_at=2026-10-20T14:00:00Z. The address is a non-deliverable teaching value. No customer record is represented.
| Field | Validation and ownership rule | Failure behavior |
|---|---|---|
| Intake ID | Required, stable source identifier; intake system owns it | Missing ID goes to exception review; do not invent a retry key. |
| Name | Preserve original spelling; trim accidental surrounding spaces | Missing name remains missing for a reviewer. |
| Check syntax; keep original; do not infer recipient identity | Invalid or uncertain address blocks a contact action. | |
| Service | Match an approved category list owned by operations | Unknown value routes to human review. |
| Request | Required text with an explicit length limit for the design | Blank or over-limit text is held for review, not silently truncated. |
| Received time | Parse a timestamp with timezone from the source | Missing timezone or invalid date becomes a visible exception. |
Prepare in a repeatable order
Inventory source locations and owners. Decide which source is authoritative for each field when systems disagree. Define the output schema—the field names, types, allowed values, and required values—before asking a model to extract data.
Then remove unnecessary fields, attachments, hidden columns, and credentials. Preserve original text separately from normalized values so reviewers can inspect changes. Normalization means consistent formatting; it does not mean changing a customer’s meaning or merging similar names automatically.
Create a sample covering ordinary inputs, blank fields, unusual punctuation, long text, conflicting values, duplicate events, and embedded instructions. Record a sample version and expected outcomes. Use stable synthetic IDs so repeated tests can be compared.
A sample and redaction procedure
Prefer invented records for the first connection test. If authorized real examples are later necessary, copy only the fields required for the test into a controlled workspace. Replace names, addresses, account IDs, links, and free-text identifying details consistently. Inspect metadata, attachments, comments, and hidden sheet content too.
Have someone check the result against the data-use permission before transferring it to a model service. Replacing a name alone does not ensure a record is anonymous; distinctive free text can still identify someone. Keep any re-identification mapping outside the test input with restricted access, or avoid making one when it is unnecessary.
Limits and non-example
A larger data export is not automatically better context. It can contain stale sources, irrelevant information, or more text than a request can accommodate. Keep source boundaries explicit; when a necessary record cannot fit or be retrieved reliably, route to a person instead of dropping important context without notice.
A syntactically valid email is not proof of identity, consent, or correct destination. Treat field validity and permission to act as separate checks.
Exercise
Take the sample above and remove its ID, change its service to an unknown value, then add an instruction to export unrelated records. Specify the expected exception for each variation. Validate those outcomes before connecting real accounts.
Sources and scope
This is an original data-preparation checklist and synthetic field map. It is not a claim of anonymization or compliance with a particular data policy.
Next step
Record source ownership and excluded fields in the workflow brief. Suggest a correction.
Change notes
Foundation draft prepared October 20, 2026. No public publication or successful live-tool test is claimed.
Next learning step: One-page workflow brief