Back to insights

Data Quality|18 July 2026

Build a simple pre-automation validation pipeline to block bad CRM records (afternoon-ready)

A practical, afternoon-ready guide to stop incomplete, duplicate or malformed CRM records from tripping automations.

Where to run your checks (three sensible layers)

Start as close to the source as you can: validate at the webhook/form layer so obvious bad submissions never enter your stack. This is fast to reject simple problems (empty required fields, malformed emails, obvious spam) and works with Marketo/Pardot form handlers, server-side form scripts, or webhooks from tools that post to Zapier/Make.

Next, use your integration tool (Zapier, Make or a small lambda) as a richer gate. Here you can combine checks from multiple systems, call a postcode or phone-format API, run a lightweight duplicate check against a cached list, and decide whether to forward, quarantine or return an error to the source.

Finally, keep a CRM-level safety net in HubSpot, Salesforce or your marketing automation platform. CRM checks are the last mile: create a processing-state property or queue, and prevent customer-facing automations from running until a record has passed the earlier layers or been approved by a human.

The five essential validations and what to do when a record looks wrong

There are five checks that stop the most common problems: required fields, format checks, provenance/trust score, duplicate detection, and exception flagging. Together they catch empty or partial records, badly formatted contact details, low-trust inputs from unverified sources, repeats that cause duplicate work, and unusual values you want a human to inspect.

  • Required fields: ensure the minimal fields for your automation (email/contact name/order ID) are present. If missing, quarantine.
  • Format checks: validate email syntax, UK postcode format, phone numbers (basic length/prefix), and dates. Reject or flag near the source for easy fixes.
  • Provenance / trust score: capture source, submission method and a small trust score (0–3). Low-trust sources (old spreadsheet import, unknown API) should be gated.
  • Duplicate detection: check for matching email, phone or business name before creating a new record; prefer adding to an existing record or placing the new one in review.
  • Exception flagging: flag odd values (very large orders, unusual currencies, unknown product codes) so human reviewers can review before automations run.

When a check fails, move the record into a quarantine state rather than deleting it: set a processing-state property (e.g. pending_review), add a short reason field, and place it in a shared review queue (CRM list, Google Sheet or simple Kanban). Keep the original data so a human can fix and re-release without losing context.

A 60–90 minute implementation checklist and a short smoke test

  • 0–15 mins: Decide the three-layer approach for your case (form/webhook → Zapier/Make → CRM). Pick the single processing‑state field name your team will use (e.g. processing_state).
  • 15–30 mins: Implement required-field and basic format checks at the form/webhook layer (Marketo/Pardot forms, website script or Zapier filter). Reject or return a clear error message for submitters.
  • 30–50 mins: Add the integration-layer checks in Zapier/Make or your integration step: duplicate lookup (email/phone), simple trust scoring (source rules), and enrich/normalize common fields (postcode, phone prefix).
  • 50–70 mins: Add CRM quarantine and human‑review pattern: create the processing_state property, a short failure_reason field, a saved list/segment for pending_review, and a simple owner assignment rule.
  • 70–90 mins: Set up alerts and rollback patterns: daily digest to Slack or email for pending_review, immediate alert for high-risk exceptions, and a snapshot step that records previous values to a simple audit column or spreadsheet before any automated overwrite.

Quick smoke tests (10–15 mins): create three canary records — a) missing required field, b) malformed email/UK postcode, c) duplicate contact — and confirm each is rejected or routed to pending_review, that alerts are sent, and that the processing_state prevents downstream automations. Then fix one in the review queue and confirm it progresses through the pipeline.

If you want an extra pair of eyes on the rules or a short live review of your implementation, Optira can help with a practical checklist review.

Need this turned into action?

Optira helps smaller teams clean up data, connect systems, build lightweight tools and remove the manual work that keeps coming back.