What it is
BigQuery is a data warehouse used to store and analyze large volumes of data.
In an analytics setup, it is often used to:
- store raw event data
- connect data across systems
- support deeper analysis and reporting
It provides a durable, queryable layer for your data.
What it’s good for
Used well, BigQuery gives you more control over your data.
It allows you to:
- retain full event-level data
- connect analytics with backend systems
- build custom reporting and models
- reduce reliance on platform limitations
This makes it a key component for more advanced measurement systems.
Where it breaks down
BigQuery does not fix data quality issues.
It stores what it receives.
If the incoming data is incomplete or inconsistent, BigQuery will store that inconsistency at scale.
Over time, this often leads to:
- complex queries built on unreliable data
- discrepancies between warehouse and platform reports
- increasing effort to reconcile numbers
- false confidence in large datasets
Scale does not correct bad data—it amplifies it.
The issue is not BigQuery.
It’s the data being sent into it.
Why tools alone aren’t enough
A data warehouse does not guarantee reliable data.
Reliable measurement depends on:
- accurate data collection
- consistent event design
- aligned definitions across systems
- ongoing maintenance
Without this, BigQuery becomes a larger container for the same underlying problems.
What this means
If your data is unreliable, moving it into BigQuery does not resolve the issue.
It carries the same problems forward—at scale.
The value of BigQuery comes from:
- structured data pipelines
- consistent logic
- controlled transformation
Without that, complexity increases faster than clarity.
The next step
Before expanding into BigQuery, you need to understand how your current system is behaving.
An Evaluate engagement helps identify:
- whether your data is ready for a warehouse
- where inconsistencies will carry through
- what is required to build a reliable data foundation
Start with Evaluate
Doug McCaffrey
Designs and maintains analytics systems that remain reliable over time.
Explore how this connects across your data estate: