CASE STUDY

CRM Data Quality Automation

From inconsistent records to controlled data quality

Project Overview

CRM Data Quality Automation was built around a common operational problem: customer records entering the CRM with inconsistent, incomplete, or duplicate data. New records are checked for missing or inconsistent information, phone data is normalized, possible duplicates are identified, and the resulting data-quality status is written back into the CRM for downstream use.

The workflow extends beyond individual records by tracking hygiene events, flagging cases that need cleanup, and keeping the team informed through alerts and scheduled reporting. This creates a repeatable data-quality layer around the CRM instead of leaving record cleanup to manual review.

Problem

CRM records were entering the system with missing fields, inconsistent formatting, or duplicate contacts, while data quality depended too heavily on manual cleanup. These issues could persist into connected deals, making it harder to keep records clean and requiring additional review downstream.

Solution

The automation adds a structured data-quality layer to new CRM records. Contact data is checked as records are created, phone numbers are normalized, potential duplicates are identified, and the resulting hygiene status is written back into the CRM so each record carries a clear data-quality state.

The same status is then used when related deals are created, allowing records that need cleanup to be flagged before issues spread further. Hygiene events are logged, exceptions are surfaced through team alerts, and a scheduled digest provides a consolidated view of records that require attention.

Safeguards

This workflow includes safeguards that separate clean records from cases that require attention. Contacts with missing information or possible duplicates are marked for review, while connected deals are checked against the contact’s hygiene status before moving forward. Exceptions are recorded in the hygiene log and surfaced through team notifications.

What was built

The build includes CRM contact validation, phone normalization, duplicate checking, hygiene status updates, and checks on connected deals. Records that require cleanup are surfaced through team notifications, while a daily hygiene report brings together the latest data-quality events in one operational view.

Expected Impact

The CRM data layer becomes more reliable by catching quality issues earlier, keeping cleanup work visible, and reducing the amount of manual cleanup required later. Team alerts and daily reports make issues easier to review, while the hygiene log provides a clearer audit trail of data-quality activity.