---
title: Data Schema and Architecture — Customer Data Platforms Module 6 — RGM Training
url: https://realgrowthmatters.com/training/customer-data-platforms/data-schema-and-architecture/
updated: 2026-06-10
source_html: https://realgrowthmatters.com/training/customer-data-platforms/data-schema-and-architecture/
---

RGM° · Training

# Data Schema and Architecture

The CDP data model determines what marketing can do. This module covers schema design, computed properties, multi-tenant architecture, and the governance that makes the system maintainable.

### What you will learn

1. The CDP data model: entities, events, profiles, audiences
2. Profile schema design
3. Event schema design and naming conventions
4. Computed properties and predictive scores
5. Audience definition: SQL vs UI vs hybrid
6. Schema evolution and versioning
7. Multi-tenant and multi-brand data architecture
8. Data residency and regional compliance
9. Performance and scale considerations
10. Documentation and discoverability
11. Schema governance

## 1. CDP data model

The standard model includes:

- **Entities:** Customers, accounts, products.
- **Events:** Behavioral actions (page\_viewed, product\_added, purchase\_completed).
- **Profiles:** Unified customer records with attributes.
- **Audiences:** Filtered subsets of profiles.
- **Journeys:** Multi-step orchestration of audiences.

## 2. Profile schema

- Identity attributes: email, phone, customer\_id, device\_ids.
- Demographic attributes: age range, gender (where appropriate), location.
- Behavioral attributes: most recent purchase, lifetime value, lifecycle stage.
- Predictive attributes: churn risk, propensity scores, segment.
- Consent attributes: marketing consent, channel preferences.

## 3. Event schema

| Layer | Properties |
| --- | --- |
| Standard | event\_name, timestamp, user\_id, session\_id |
| Context | page, device, app version, referrer |
| Event-specific | product\_id, value, quantity |
| Custom | Business-specific properties |

## 4. Computed properties

Most CDPs compute properties on top of raw events: total lifetime value, days since last purchase, count of category purchases, segment membership. These computed properties become the primary audience inputs.

## 5. Audience definition

- SQL-defined: full flexibility, requires SQL competence.
- UI-defined: limited but accessible to marketers.
- Hybrid: SQL-defined "computed properties" that marketers reference in UI.

## 6. Schema evolution

Schemas change over time as products and events evolve. Versioning practice:

- Backward-compatible additions are safe.
- Breaking changes require coordinated migrations.
- Version field on events.
- Documented deprecation cycles.

## 7. Multi-tenant / multi-brand

Holding companies with multiple brands face an architecture choice:

- Separate CDP per brand.
- Single CDP with brand-scoped audiences and governance.
- Hybrid: shared identity layer, separate activation layers.

## 8. Data residency

GDPR and other regional rules may require EU customer data to stay in EU. Most major CDPs offer regional data residency. Confirm at vendor evaluation.

## 9. Performance and scale

- Profile lookup latency (real-time use cases).
- Audience computation time (large audiences may take minutes).
- Event ingestion throughput.
- Storage costs (events accumulate; archival strategy matters).

## 10. Documentation

CDP documentation should include: schema reference, event catalog, audience inventory, lifecycle definitions, computed-property definitions. Without documentation, the CDP becomes a black box.

## 11. Schema governance

- Schema change-request process.
- Review by data and marketing teams.
- Impact analysis on downstream consumers.
- Versioning and migration plans.
- Annual schema audit.

**How to use this module:** The data model (Section 1), the event-schema layers (Section 3), and the multi-tenant options (Section 7) are the planning artifacts.

### Sources & further reading

- [Segment Academy](https://segment.com/academy/)
- [mParticle documentation](https://docs.mparticle.com/)
- [RudderStack documentation](https://docs.rudderstack.com/)
- [Tealium documentation](https://docs.tealium.com/)
- [CDP Institute](https://www.cdpinstitute.org/)
- Books: Martin Kihn, *Customer Data Platforms*; Bill Inmon, *Data Modeling in the Age of Data Lakes*
- [Snowflake data modeling blog](https://www.snowflake.com/blog/topic/data-modeling/)
- [dbt data modeling](https://www.getdbt.com/blog/)
- [Holistics modeling resources](https://www.holistics.io/blog/)
- [IAB data clean rooms](https://www.iab.com/insights/data-clean-rooms/)
- [GDPR data residency](https://gdpr.eu/data-residency-and-data-localization/)
- [Data & Policy journal](https://www.cambridge.org/core/journals/data-and-policy)

---

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