Joiner FAQ

What kind of problem is Joiner best for?

Joiner is best when the primary task is to combine two related datasets using shared keys.

If the main question is "which Fields from this other File should be added to these Records?", Joiner is usually a strong fit.

What is a match key?

A match key is the Field, or set of Fields, used to connect Records between datasets.

Examples include customer ID, account number, product code, employee ID, vendor ID, or location code.

Should I clean data before joining?

Clean the data first if the key values are inconsistent.

Small differences such as extra spaces, missing leading zeros, or inconsistent punctuation can prevent expected matches.

Can Joiner help find unmatched Records?

Yes. Reviewing unmatched Records is often one of the most useful parts of a Joiner workflow.

Unmatched Records can reveal missing reference data, formatting issues, source-system timing differences, or incorrect keys.

How should I choose output Fields?

Include Fields that support the next business step.

Avoid carrying every Field from both datasets unless the downstream process truly needs them. Smaller outputs are easier to review.

What if one key matches multiple Records?

Review whether that is expected.

Some relationships are naturally one-to-many, but unexpected duplicate keys can make the output harder to review. Deduplicate or clarify the source data when needed.

What should I review after a Run?

Review known matched examples, expected unmatched examples, blank or unusual keys, and whether the output Fields make sense.

For recurring work, it is also useful to compare match rates against past Runs.

When should I use another Tool first?

Use another Tool first when keys need cleaning, duplicates need to be resolved, or the main task is filtering, splitting, validating, or calculating statistics.