---
title: CDP Data Quality Discipline — Customer Data Platforms Module 4 — RGM Training
url: https://realgrowthmatters.com/training/customer-data-platforms/cdp-data-quality-discipline/
updated: 2026-06-10
source_html: https://realgrowthmatters.com/training/customer-data-platforms/cdp-data-quality-discipline/
---

RGM° · Training

# CDP Data Quality Discipline

Data quality typically gets 5 - 15% of CDP investment but determines 60 - 80% of the marketing outcome. This module covers the four-dimension framework and the governance that maintains quality.

### What you will learn

1. Why data quality is the highest-leverage CDP investment
2. The data quality dimensions: accuracy, completeness, consistency, timeliness
3. Source-data quality vs CDP-resolved quality
4. Event schema and naming discipline
5. Customer record deduplication
6. Address normalization
7. Email and phone validation
8. Suppression lists and lifecycle states
9. Data quality testing
10. Governance: who owns data quality
11. The annual data quality audit

## 1. Data quality leverage

A CDP with poor data quality produces poor marketing outcomes regardless of how sophisticated the rest of the stack. Data quality typically receives 5 - 15% of CDP investment but determines 60 - 80% of the marketing outcome.

## 2. Four dimensions

| Dimension | Definition |
| --- | --- |
| Accuracy | Data reflects reality |
| Completeness | Required fields are filled |
| Consistency | Same fact same across sources |
| Timeliness | Recent enough for the use case |

## 3. Source vs resolved

Quality must be enforced both at source (event tracking, form validation, integration accuracy) and after resolution (deduplication, normalization, merging). Source quality is cheaper than after-the-fact cleanup.

## 4. Event schema discipline

- Standard event names (page\_viewed, product\_added, purchase\_completed).
- Standard property names and types.
- Versioning for schema changes.
- Documentation accessible to engineering and analytics.
- Schema validation in the data pipeline.

## 5. Customer record deduplication

- Exact match on email or phone.
- Fuzzy match on name + address.
- Probabilistic match with confidence threshold.
- Manual review queue for uncertain matches.
- Audit trail for merge / split actions.

## 6. Address normalization

USPS address standardization (Smarty Streets, Lob, Loqate, Melissa). Normalized addresses enable better matching and reduce duplicate records.

## 7. Email and phone validation

- Email syntax validation at collection.
- Email deliverability validation (NeverBounce, ZeroBounce, Kickbox).
- Phone format normalization (E.164).
- Phone-line-type detection.

## 8. Suppression and lifecycle

- Unsubscribed.
- Bounced / undeliverable.
- Spam-flagged.
- Compliance-suppressed (GDPR / CCPA opt-out).
- Deceased.
- Test / internal.

## 9. Data quality testing

- Continuous validation rules (dbt tests, Great Expectations, Soda).
- Alerts on anomalies.
- Dashboards for key quality metrics.
- Regression testing on schema changes.

## 10. Governance

Who owns data quality? Typically a data steward role works with marketing operations, analytics, and engineering. Without clear ownership, quality degrades.

## 11. Annual audit

1. Inventory of sources.
2. Schema compliance review.
3. Sample-based accuracy audit.
4. Duplicate-record analysis.
5. Suppression-list audit.
6. Improvement plan.

**How to use this module:** The four-dimension framework (Section 2), the schema discipline (Section 4), and the suppression categories (Section 8) are the planning artifacts.

### Sources & further reading

- [Great Expectations](https://greatexpectations.io/)
- [Soda](https://www.soda.io/)
- [dbt data tests](https://docs.getdbt.com/docs/build/data-tests)
- [Dataversity Data Quality](https://www.dataversity.net/data-quality/)
- [Smarty (formerly SmartyStreets)](https://www.smarty.com/blog)
- [Lob blog](https://www.lob.com/blog)
- [Melissa Data resources](https://www.melissa.com/resources/)
- [NeverBounce blog](https://neverbounce.com/blog)
- Books: Thomas Redman, *Data Driven*; David Loshin, *The Practitioner's Guide to Data Quality Improvement*
- [DAMA International](https://www.dama.org/)
- [TDWI Data Quality](https://tdwi.org/research/data-quality.aspx)
- [CDP Institute on Data Quality](https://www.cdpinstitute.org/insights/data-quality/)

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Part of the [Customer Data Platforms](/training/customer-data-platforms/) series · RGM Training