Specify intended cardinality
List the keys and expected relationship for each table pair. A customer can have many orders, while an order identifier may need a business-unit prefix to be unique. Inspect actual combinations instead of assuming that a field named id is globally unique across every source system.
Measure more than match rate
Count duplicate keys on both sides, unmatched records, and output rows after joining. A high apparent match rate can coexist with a many-to-many explosion that repeats transactions. Compare totals and distinct entity counts before and after each join to catch multiplication and accidental record loss.
Include time in the relationship
A customer’s address or organizational membership can change. Determine whether a transaction should link to the current record or the version valid at the event date. Ask for effective dates or historical snapshots when the task depends on what information was available at a particular moment.
Review realistic slices
Test joins across regions, old records, merged accounts, and deleted entities. Request an explanation for residual unmatched records rather than silently dropping them. A sourcing brief for Fiund should include the intended analytical or model task, necessary relationships, and acceptable handling of ambiguous or missing links. Test one small join manually using a customer with two orders and several order lines. Write the expected output row count before running the operation. Compare the result with that expectation and inspect any repeated amounts, because a correct-looking sample row can conceal a multiplication error in the full joined table.
Review checklist
- List composite keys and cardinality
- Check duplicate and orphan rates
- Compare counts before and after joins
- Specify historical versus current lookup behavior