Bring your marketing data into one place
Your data lives in ad platforms, analytics tools, and your CRM, and they rarely agree. We bring it together so reporting and modeling start from one trusted source.
What is marketing data infrastructure?
Marketing data infrastructure is the set of systems that collect, store, and organize your marketing data. It usually includes a data warehouse, pipelines that move data into it, and models that make the data consistent and ready to use.
Why platform reports aren't enough
Each platform reports on its own performance, using its own definitions and its own view of credit. Comparing them means exports, spreadsheets, and numbers that never quite match.
A warehouse gives you history you control and one definition of each metric. It's also the foundation for forecasting, predictive models, and AI tools, which are only as good as the data they use.
What's included
BigQuery and data warehouse integration
Set up or connect a data warehouse such as BigQuery, and bring in data from your marketing and sales systems.
Data pipelines and ETL/ELT
Build pipelines that extract data from each source, load it into your warehouse, and transform it on a reliable schedule.
Unified marketing data models
Combine data from every source into one consistent structure, with shared definitions for campaigns, leads, and revenue.
Data quality monitoring and anomaly alerts
Watch for missing data, broken feeds, and unusual changes, and alert your team before bad numbers reach a report.
How we approach marketing data infrastructure
1. Inventory your sources
List where your data lives, who uses it, and what questions it needs to answer.
2. Design the model
Agree on shared definitions and how the data should fit together.
3. Build the pipelines
Move data into your warehouse and keep it updated automatically.
4. Monitor quality
Check the data continuously and fix problems at the source.
How this connects to the loop
From Acquisition
Ad platforms and search tools provide the cost and campaign data.
To Prediction
One clean, unified dataset is what scoring and forecasting models are built on.
Common questions
What is a data warehouse?
A data warehouse is a central database built for analysis. It stores data from many sources in one place, so you can report on it together and keep a long history.
What is the difference between ETL and ELT?
ETL transforms data before loading it into the warehouse. ELT loads the raw data first and transforms it inside the warehouse, which modern cloud warehouses handle well.
Do we need a data warehouse?
It depends. If you're combining several sources, want long-term history, or plan to use predictive models, a warehouse usually makes sense. Simpler setups may not need one yet.
Do we have to use BigQuery?
No. BigQuery is a common choice, but the same approach works with other data warehouses.
More in Measurement
- Tracking ArchitectureCollect accurate first-party data with GA4, GTM, and server-side tracking, with privacy handled from the start.
- Closed-Loop Conversion TrackingConnect CRM outcomes back to the campaigns that produced them.
- Reporting & AnalyticsGive leaders clear dashboards, plain-language answers, and explanations of what changed.
Talk to us about marketing data infrastructure
Tell us where your marketing stands today, and we'll show you what to do first.