Raw data collection

Capture event-level marketing and customer data before platforms transform, aggregate or model it.

What it is

Raw data collection captures event-level data directly into an environment your organisation controls, usually alongside the data being sent to analytics and advertising platforms.

The same customer interaction can be sent to platforms such as Google Analytics, Google Ads and Meta while also being captured in a cloud data warehouse such as BigQuery.

Unlike standard platform reporting or processed data exports, raw data collection happens further upstream. It preserves the original event and its associated context before individual platforms apply their own processing, aggregation or modelling.

This gives organisations greater control over how their measurement data is stored, structured and used over time.

Why it matters

Analytics and advertising platforms increasingly process and model data before making it available for reporting. That can make it difficult to separate what was directly observed from what has subsequently been transformed or inferred.

Maintaining your own raw, event-level dataset creates an independent measurement record that isn’t tied to a single platform’s reporting interface, data retention settings or modelling approach.

It also provides a durable foundation for reporting, advanced measurement, customer analysis and future platform migrations, giving organisations more flexibility as their measurement needs change.

What Louder does

Collection architecture - server-side fork to warehouse, streaming or batched, with schema versioning built in from the start.
Schema design - event structures that serve analytics, activation and finance rather than mirroring one vendor’s model.
Consent-aware capture - collection scoped to the consent given, with the consent state itself stored alongside the event.
Retention and cost design - partitioning, clustering and tiered storage so raw capture doesn’t become an open-ended bill.
Downstream modelling - curated marts built on the raw layer, so analysts aren’t querying the firehose directly.

Common challenges

Consent state not captured with the event. Without the consent context stored alongside, the dataset can’t be filtered retrospectively and its lawful basis can’t be demonstrated. That makes the archive a liability rather than an asset.
No schema versioning. Event structures change and old and new rows sit in the same table with no version field. Queries silently span incompatible shapes.
Cost discovered in month three. Streaming inserts and unpartitioned tables produce a bill nobody forecast, and the usual response is to switch collection off entirely rather than fix the design.
Collecting identifiers with no retention policy. Raw capture makes it easy to store more personal data than any policy actually authorises, indefinitely, without a deletion path.