BigQuery

Build a scalable marketing data warehouse that brings analytics, advertising and business data together in one place.

For measurement-specific use cases, see BigQuery for measurement

What it is

Google BigQuery is a serverless cloud data warehouse designed to store, process and analyse large volumes of data.

For marketing teams, BigQuery can bring together data from Google Analytics 4 (GA4), Google Ads, Campaign Manager 360, CRM systems, e-commerce platforms and other business sources.

Rather than analysing each platform separately, organisations can transform that data into consistent business definitions and curated datasets for reporting, measurement, attribution, audience activation and advanced analytics.

BigQuery’s native connections with Google Analytics and Google Cloud make it particularly useful for organisations using Google’s marketing ecosystem, while its scalable architecture allows the data environment to grow with the business.

Why it matters

Marketing data is often spread across advertising platforms, analytics tools, CRM systems and internal reporting environments.

Each system has its own definitions and reporting methods, making it difficult to create a consistent view of customers and marketing performance.

BigQuery provides a central data foundation where those sources can be combined, modelled and governed using business definitions your organisation controls.

That creates more flexibility for reporting and analysis while supporting more advanced use cases including first-party data activation, customer modelling, attribution and AI and machine learning.

What Louder does

Warehouse design - project and dataset structure, region selection, partitioning and clustering strategy set against how the data will actually be queried.
Export and transfer configuration - GA4 export, Google Ads and Campaign Manager data transfers, backfill, schema change handling.
Transformation layer - raw to staged to modelled tables, session and user reconstruction, scheduled queries or dbt, so business logic lives in one place instead of in every analyst’s head.
Cost governance - on-demand versus capacity, custom quotas, cost attribution by team, and the query patterns that quietly cost the most.
Activation - audience and conversion pushes back to ad platforms, reverse ETL, and the serving layer for Looker Studio or Power BI.
Access and governance - IAM, authorised views, row-level security, and handling of personal information within the warehouse.

Common challenges

Unpartitioned tables behind live dashboards. On-demand billing charges by bytes scanned. A wide, unpartitioned events table sitting behind a dashboard that fifty people refresh daily is a recurring cost with no corresponding benefit.
Data region chosen by default. The GA4 export region is set once and cannot be changed without rebuilding, and joins across regions aren’t possible. Teams with Australian residency obligations frequently discover their data has been landing in the US for a year.
No transformation layer. Every analyst writes their own session and attribution logic, so three dashboards produce three defensible answers and the meeting becomes about the discrepancy.
Streaming export mistaken for the daily table. The intraday table isn’t deduplicated and has a different shape. Using it interchangeably with the daily export inflates counts in ways that take a long time to notice.