Trust is built into a data project through small, visible habits. First, document where important fields come from and what each transformation does. This gives reviewers something concrete to inspect.
Second, test the edges. Check missing values, unusual ranges, duplicate records, and changes in volume. Automated checks turn quiet data problems into visible work items.
Third, show the limits. Every analysis has assumptions and uncertainty. Naming them clearly helps stakeholders use the result well instead of treating it as a promise.