How to set up field validation
Field validation enforces correctness at entry, but a badly tuned rule blocks good data and teaches reps to resent the system.
The goal is rules that catch genuine errors while never rejecting a legitimate value, paired with messages that explain the fix.
Short answer
Set up field validation by identifying the fields where bad values cause real problems, writing rules for format, range, and logical consistency, testing them against real data so they do not block legitimate entries, and pairing validation with clear error messages. Validate the fields that matter, and make rules match real-world valid values to avoid frustrating users.
Step by step
Target high-impact fields
Validate the fields where a bad value causes real downstream harm: email, phone, amount, close date. Skip low-stakes fields.
Write format, range, and logic rules
Add checks for correct formats, sensible ranges, and logical consistency between related fields.
Test against real data
Run rules against actual records to confirm they do not reject legitimate values, since real data is messier than examples.
Pair with clear error messages
Tell the user exactly what is wrong and how to fix it, so validation guides rather than just blocks.
Test rules against messy real data
A validation rule that looks right on clean examples often rejects legitimate real-world values - international phone formats, valid but unusual emails. Test every rule against actual data before turning it on, or you will block good entries and train reps to work around the system.
How Ardovo helps
Ardovo validates key fields with sensible defaults tuned to real-world values, and clear inline messages. Rook keeps validation focused where correctness matters, so rules catch genuine errors without rejecting the messy-but-valid data that trips up naive validation.
Frequently asked questions
Which fields need validation rules?
The ones where a bad value causes real harm: email and phone for deliverability, amount and close date for forecasting, and any field where a wrong format breaks a downstream system. Low-stakes fields rarely need validation and adding it just creates friction.
How do you avoid validation rules blocking good data?
Test every rule against real, messy data before enabling it. Rules that look correct on clean examples often reject legitimate values like international phone formats or unusual-but-valid emails. Testing against actual records catches these before they frustrate users.
Should validation rules have error messages?
Yes. A rule that blocks a save without explaining why just frustrates users. A clear message stating what is wrong and how to fix it turns validation from an obstacle into guidance, so reps correct the value rather than fighting the system.