# BigQuery and Dataform consultancy | Intelligence platforms | Measurelab

> Connect marketing, customer and product data in BigQuery. Shared definitions, tested Dataform models and documentation your team can use.

Source: https://www.measurelab.co.uk/solutions/intelligence-platforms/

## Intelligence platforms & data engineering

Your analysts shouldn't have to piece the customer journey together from exports. We join your marketing, customer and product data in BigQuery, with tested Dataform models your team can use and maintain.

## From scattered sources to shared answers

Start with the sources your first question needs and add more when there's a useful reason. It's built in your cloud, and you keep the code, definitions and documentation. We're independent, so we recommend tools around your requirements and explain the trade-offs.

### Bring the data together in BigQuery

Marketing, customer and product data joined in BigQuery, starting with what your first question needs.

- GA4 export
- Ad platforms
- CRM and sales data

[It starts with tracking you can trust](https://www.measurelab.co.uk/solutions/analytics-implementation/)

### Model it once in Dataform

Tested Dataform transformations, with checks for missing, duplicate or unexpected records and costs made clear.

- Dataform models
- Data quality checks
- Documentation

[Read our Dataform guide](https://www.measurelab.co.uk/insights/blog/dataform-for-bigquery-basic-end-to-end-guide/)

[Already in BigQuery? What a BigQuery audit covers](https://www.measurelab.co.uk/solutions/foundation-audit/#scope-bigquery)

### Agree what the numbers mean

What counts as a customer? Which revenue figure should the board see? Definitions agreed and connected to the models that use them. They can also go into Knowledge Catalog, so Gemini and other agents use the same meanings.

- Metric definitions
- Customer and revenue rules
- A named owner for each definition

### Put it to work

Results available in the reports, customer tools and workflows where your team needs them.

- Tables for reporting
- Audiences for CRM and ad platforms
- Access and permissions

[Marketing analytics & data products](https://www.measurelab.co.uk/solutions/data-products/)

## Tools we work with

BigQuery and Dataform are where most of this work happens. These are the other tools we often connect them with.

Data integration

data-integration

## What changes for you

You end up with your marketing, customer and product data in one place: BigQuery, in your own Google Cloud project. Pipelines bring each source in on a schedule, and Dataform turns the raw tables into tested models your analysts can query straight away.

Checks run every time the data loads. If a source stops arriving, or a number moves outside what's expected, the right person hears about it before it reaches a report.

Each metric is defined once, signed off by the people who own it and written into the models, so finance and marketing work from the same figures. You keep the code, the definitions and the documentation. When you want to add a source, build a report or try a model, the groundwork is already there.

Guides and conversations from our team on BigQuery, Dataform and shared definitions.

### [When a BigQuery query needs a maintainable Dataform pipeline](https://www.measurelab.co.uk/insights/blog/big/)

### [Springer Nature's Dataform models](https://www.measurelab.co.uk/insights/success-stories/streamlining-analytics-for-springer-nature-with-dataform/)

### [A conversation on BigQuery and GA4](https://www.measurelab.co.uk/insights/podcast/bigquery-ga4-with-johan-van-de-werken-ga4bigquery/)

### [How to put a spend cap on your BigQuery queries](https://www.measurelab.co.uk/insights/blog/bigquery-tips-how-to-put-a-spend-cap-on-your-bigquery-queries/)

### [What on earth is a semantic layer?](https://www.measurelab.co.uk/insights/podcast/what-is-a-semantic-layer-david-jayatillake/)

### [Warehouse-native analytics](https://www.measurelab.co.uk/insights/podcast/warehouse-native-analytics-adam-greco/)

[Everything we've published on BigQuery and Dataform](https://www.measurelab.co.uk/insights/tag/bigquery+dataform/)

The work, in practice

## [Ad to enrolment connected](https://www.measurelab.co.uk/insights/success-stories/unifying-student-data-for-university-of-exeter/)

We connected marketing interactions with application and enrolment records in a shared reporting platform. Exeter's recruitment team can follow the student journey and track cost per enrolment.

University of Exeter

Full-funnel visibility

## [Web analytics connected to CRM](https://www.measurelab.co.uk/insights/success-stories/integrating-web-analytics-with-crm-charles-stanley/)

Shared identifiers connected GA4 and BigQuery data with CRM records. Scheduled transfers reduced manual handling and made web insights available for customer marketing.

Charles Stanley

Automated data transfers

## [Siloed data integrated](https://www.measurelab.co.uk/insights/success-stories/integrating-siloed-data-springer-nature/)

Sales and marketing worked from incomplete data: transactions and revenue sat in another platform, with no ready-made connector into BigQuery. We built a custom Google Cloud pipeline, using Cloud Functions, Workflows, Dataform and BigQuery, that joins acquisition and behavioural data to transaction and revenue detail for a clearer revenue view across channels.

Springer Nature

One revenue view across channels

## [Scheduled queries to modular Dataform](https://www.measurelab.co.uk/insights/success-stories/streamlining-analytics-for-springer-nature-with-dataform/)

Reporting relied on stacked scheduled queries in BigQuery that had become hard to maintain. As part of the GA4 migration we rebuilt them as modular, documented Dataform models with clear lineage, version control and testing, which reduced maintenance overhead and sped up delivery.

Springer Nature

Documented, testable models

## Commercial and ERP sources unified

Eaglemoss had a spread of disconnected commercial and ERP sources. We brought them into a central BigQuery warehouse and automated reporting the analysis team had been assembling by hand.

Eaglemoss

Central BigQuery warehouse

## [Web, ordering and call-centre data joined](https://www.measurelab.co.uk/insights/success-stories/call-propensity-analysis-for-sanderson/)

Sanderson could not see what happened before a customer picked up the phone. We joined GA4 to their ordering system and call-centre data in BigQuery, keyed on user ID, which showed which pages people were on immediately before they called.

Sanderson Design Group

Joined in BigQuery on user ID

## Data pipelines moved into their own Google Cloud

An agency was paying for a managed marketing data platform that struggled with a changing list of client accounts. We rebuilt the pipelines in their own Google Cloud project, with Meltano and Cloud Functions for ingestion, Cloud Composer for scheduling, BigQuery for storage and Dataform for transformations, and handed over with documentation and training. Monthly running costs fell by around 87%.

Digital marketing agency

Around 87% lower monthly running costs

## Analytics built from scratch

Marqeta runs a card-issuing platform used by fintechs. We built their analytics foundation, covering GA4, GTM governance, flattened BigQuery tables and multi-source ingestion, and we look after it as an ongoing engagement.

Marqeta

Analytics foundation built and looked after

## E-commerce intelligence

BFI generates revenue from memberships, subscriptions, cinema tickets, print and merchandise, all on separate platforms. We built the tracking and data layer to unify these streams into one coherent commercial view.

BFI

Five revenue streams unified

## What clients say

## Tell us what you need to connect

Bring the sources you want joined and the first question you want answered. We don't buy or sell media or take a cut of ad spend, so we recommend tools around what you need and explain the trade-offs.

Discuss your data platform