Data Management & Analytics:
How Do I Build a Marketing Data Warehouse?
Centralize multidomain marketing data to power accurate reporting and activation. This blueprint covers architecture, pipelines, modeling, governance, and measurement—so you launch a warehouse that leaders trust.
Start with a cloud warehouse (Snowflake/BigQuery/Redshift), land raw data via ELT pipelines (connectors + reverse ETL), model into clean, conformed marts (dbt/SQL), and enforce governance (taxonomy, consent, roles). Ship an exec dashboard first, then scale to attribution, LTV, and activation.
Warehouse First Principles
Choosing the Core: Warehouse vs. CDP vs. Lakehouse
Pick the control plane that fits your team’s skills, privacy needs, and activation goals.
| Option | Best For | Strengths | Trade-offs | Typical Add-ons |
|---|---|---|---|---|
| Cloud Warehouse (Snowflake, BigQuery, Redshift) | Analytics-first teams needing SQL control and cross-domain joins. | Open modeling (dbt), low vendor lock-in, scalable compute, strong governance. | Requires data engineering; activation needs reverse ETL/CDP. | ELT connectors, dbt, BI, reverse ETL, consent/PII vault. |
| CDP-Centric (platform-native) | Fast audience activation and real-time personalization. | Out-of-box identity, audiences, destinations, consent tooling. | Less flexible modeling; analytics often limited without a warehouse. | Warehouse export, BI, server-side tagging, clean room. |
| Lakehouse (Databricks, OSS) | Advanced data science on semi-structured/streaming at scale. | Unified batch/stream, ML-friendly, cost-effective storage. | Higher complexity; more engineering; BI/SQL maturity varies. | SQL endpoints, governance layer, BI, reverse ETL/CDP. |
Your 90-Day Warehouse Build Plan
Deliver value early while laying a scalable foundation.
Phase 1 → Phase 2 → Phase 3
- Days 1–30: Foundations — Select warehouse; define data contracts and taxonomy; set up ELT tool; ingest CRM, MAP, web analytics, spend; implement role-based access & naming conventions.
- Days 31–60: Model & Validate — Create dbt repo; build staging models; implement identity stitching; publish core marts (campaigns, contacts, accounts, opportunities, spend); add schema tests, freshness monitors.
- Days 61–90: Activate & Measure — Stand up BI dashboards (pipeline, ROAS, velocity); deploy reverse ETL to ad/email tools; add attribution model(s); document lineage; establish cost and performance SLAs.
Warehouse Build Matrix (Phases, Owners, Outputs)
| Phase | Primary Focus | Owner(s) | Key Outputs | Primary KPI |
|---|---|---|---|---|
| 1. Foundations | Platform selection, ELT setup, governance baseline | Data Eng + MOps + Security | Warehouse provisioned, connectors live, taxonomy & data contracts, RBAC | Ingestion Freshness & Coverage |
| 2. Model & Validate | dbt models, identity stitching, tests | Analytics Eng + MOps | Conformed marts, data quality tests, documentation | Test Pass Rate & Match Rate |
| 3. Activate & Measure | Dashboards, reverse ETL, attribution | Analytics + MOps + Channel Owners | Executive dashboard, governed audiences, attribution reports | Dashboard Adoption & ROAS Lift |
Client Snapshot: Warehouse in 90 Days
A B2B SaaS team centralized CRM, MAP, ads, and product logs into BigQuery with dbt. Identity stitching lifted match rates to 82%, campaign tagging standardized UTMs, and reverse ETL powered audience suppression—cutting paid CAC by 14% and reducing dashboard prep time from days to minutes.
Map your warehouse to RM6™ and instrument journeys with The Loop™ so modeling mirrors the buying cycle.
Frequently Asked Questions about Marketing Warehouses
Short, self-contained answers designed for AEO and rich results.
Stand Up a Warehouse Leaders Trust
We’ll centralize your sources, model reliable marts, and activate governed audiences—so reporting is fast and decisions are confident.
Tie Your Warehouse to RevOps Operationalize with Marketing Operations