How to build a marketing data warehouse?

The Short Version

A marketing data warehouse centralizes all your customer, campaign, and revenue data into a single queryable system. It replaces fragmented spreadsheets and disconnected tools with a unified source of truth that powers analytics, reporting, and AI-driven decision-making. Think of it as the infrastructure layer that makes your marketing stack actually work together.

Why CMOs Need a Data Warehouse Now

Most marketing teams operate with 7-12 disconnected tools: email platforms, ad networks, CRM systems, analytics tools, and attribution software. Data lives in silos. When you need to answer "What's our true customer acquisition cost?" or "Which campaigns drive revenue?", you're manually pulling reports from five different systems.

A data warehouse eliminates this friction. It becomes the backbone for:

• Real-time dashboards showing campaign performance across all channels
• Attribution modeling that connects marketing spend to actual revenue
• Predictive analytics using historical data to forecast outcomes
• AI agents and automation that need clean, structured data to operate
• Compliance and governance with audit trails and data lineage

The 4-Step Build Process

Step 1: Choose Your Cloud Data Platform

You have three main options:

• Snowflake ($2K-$5K/month): Most popular with marketing teams. Easy to scale, excellent performance, strong ecosystem of integrations. Best if you want vendor flexibility.
• Google BigQuery ($1.5K-$4K/month): Tightly integrated with Google Analytics 4 and Google Cloud. Best if you're already in the Google ecosystem.
• Amazon Redshift ($1K-$3K/month): Most cost-effective at scale. Best if you have deep AWS expertise.
• Databricks ($3K-$8K/month): Modern, AI-native platform. Best if you plan heavy machine learning and predictive modeling.

Recommendation for most CMOs: Start with Snowflake. It has the gentlest learning curve, strongest third-party integrations, and the largest community of marketing practitioners.

Step 2: Connect Your Data Sources (ETL/ELT)

You need a tool to automatically pull data from your marketing stack into the warehouse. This is called ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform).

Top tools for marketing teams:

• Fivetran ($1.5K-$5K/month): Pre-built connectors for 300+ marketing tools. Minimal configuration. Best for non-technical teams.
• Stitch ($1K-$3K/month): Simpler than Fivetran, fewer connectors but covers all major marketing platforms.
• Airbyte ($500-$2K/month): Open-source option. Requires more technical setup but very flexible.
• Custom API integrations ($0 + engineering time): If you have a data engineer, you can build custom connectors. Most flexible but slowest to implement.

What to connect first:

1. Your CRM (Salesforce, HubSpot, Pipedrive)
2. Your email platform (Marketo, Klaviyo, Mailchimp)
3. Your ad platforms (Google Ads, Meta Ads, LinkedIn Ads)
4. Your analytics tool (Google Analytics 4, Mixpanel)
5. Your attribution tool (if you have one)

Start with these 5. You can add more later.

Step 3: Structure Your Data (Schema Design)

Once data flows into your warehouse, you need to organize it so it's actually useful. This is where schema design matters.

Use a star schema structure:

• Fact tables (center): Transactional data. Examples: marketing_campaigns, customer_conversions, ad_impressions
• Dimension tables (surrounding): Descriptive data. Examples: customers, campaigns, channels, dates

Example structure:

```

Fact: conversions

├── customer_id (FK to customers)

├── campaign_id (FK to campaigns)

├── channel_id (FK to channels)

├── conversion_date (FK to dates)

├── revenue

└── conversion_type

Dimension: customers

├── customer_id (PK)

├── email

├── segment

└── lifetime_value

Dimension: campaigns

├── campaign_id (PK)

├── campaign_name

├── channel

└── budget

```

This structure makes queries fast and dashboards easy to build.

Who builds this: Either hire a data engineer ($120K-$180K/year) or use a consultant ($5K-$15K for initial setup). Many ETL tools now have AI-assisted schema builders that reduce this work.

Step 4: Add Governance and Connect BI Tools

Once your warehouse is structured, connect it to visualization tools:

• Tableau ($70-$140/user/month): Most powerful, steepest learning curve
• Looker ($50-$100/user/month): Best for self-service analytics
• Power BI ($10-$20/user/month): Most affordable, integrates with Microsoft stack
• Metabase ($0-$3K/month): Open-source, easiest to get started

Governance essentials:

• Define who can access what data (role-based access control)
• Document data definitions so everyone uses the same metrics
• Set up automated data quality checks
• Create an audit log for compliance

Timeline and Budget Reality

Small team (1-2 people managing): 3-4 months, $50K-$100K first year

• Platform: $2K-$5K/month
• ETL tool: $1.5K-$3K/month
• BI tool: $1K-$3K/month
• Data engineer (contract): $15K-$30K
• Training and setup: $5K-$10K

Medium team (3-5 people): 4-6 months, $100K-$200K first year

• Add full-time data engineer: $120K-$150K/year
• More ETL connectors and tools
• Advanced governance and security

Quick wins to show ROI immediately:

1. Week 1: Connect your CRM and email platform. Build a simple dashboard showing leads by source.
2. Week 2: Add ad platform data. Show cost per lead by channel.
3. Week 3: Calculate true customer acquisition cost (CAC) across all channels.
4. Week 4: Build attribution model showing which campaigns drive revenue.

These 4 dashboards typically save marketing teams 10-15 hours per week in manual reporting.

Common Mistakes to Avoid

• Starting too big: Don't try to connect all 12 tools at once. Start with 5 core sources.
• Ignoring data quality: Garbage in, garbage out. Invest in data validation rules early.
• No documentation: Your team won't use the warehouse if they don't understand the data.
• Skipping governance: You'll create compliance nightmares later.
• Choosing the wrong platform: Snowflake and BigQuery are both solid. Pick one and commit.

Bottom Line

Building a marketing data warehouse takes 3-6 months and costs $50K-$200K depending on team size, but it pays for itself in the first year through better decision-making and time savings. Start with a cloud platform (Snowflake or BigQuery), connect your top 5 data sources via an ETL tool, structure the data in a star schema, and visualize it with a BI tool. The real competitive advantage comes from having clean, unified data that powers AI agents, attribution models, and real-time dashboards—things your competitors are still building in spreadsheets.