Customer data architecture guide
CDP vs CRM, data warehouse and data clean room
A CRM manages customer relationships. A warehouse stores and analyses data. A CDP resolves people, builds audiences and activates them. A clean room supports governed collaboration and measurement. Most useful architectures combine several of these jobs.
The short comparison
- CRM: records leads, contacts, accounts, interactions and pipeline or service state.
- Cloud data warehouse: stores structured data for querying, modelling and analysis at scale.
- Customer data platform: resolves customer identities, applies audience logic and moves usable segments into destinations.
- Data clean room: lets parties match, analyse or activate governed data under agreed controls without treating unrestricted raw sharing as the default.
A CRM knows the relationship
Salesforce, HubSpot and Microsoft Dynamics are strong sources of declared customer and lead state. They may know that someone is a qualified lead, current customer, lapsed student or active member.
A CRM is not automatically the best place to reconcile every behavioural and operational record or keep the same audience current across multiple paid-media platforms. It is often a source of customer truth, not the complete activation layer.
A warehouse knows the data
Google BigQuery, Snowflake and other cloud warehouses can centralise large volumes of CRM, analytics, transactional and operational data. They are powerful environments for transformation, querying and modelling.
A warehouse does not create value simply because data has been loaded into it. Audience definitions, permissions, identity rules, destination connections and ongoing operation still need to exist around it.
A CDP turns customer truth into an audience decision
A CDP connects relevant sources, resolves records that belong to the same person, applies transparent segment and suppression logic, then sends the result to a destination.
Fractyl is a focused composable CDP. Google Cloud and BigQuery sit at the core, while Fractyl adds identity, audience construction, scheduled synchronisation, governance and an operating model.
A clean room governs collaboration
A data clean room is useful when organisations need to compare, measure or activate data within controlled rules. It may support advertiser and media-owner matching, campaign measurement or collaboration with another approved data holder.
A clean room does not replace the internal work of deciding which records are eligible, current and lawful to use. Fractyl can prepare and deliver governed audiences into supported third-party clean rooms.
How the systems work together
- CRM contributes lead, customer, product and lifecycle state.
- GA4, server-side signals and operational systems add behaviour and outcomes.
- The warehouse stores and transforms the agreed data.
- The CDP resolves identity and creates a reviewable audience.
- Paid-media platforms and clean rooms receive governed outputs.
- Measurement returns match rates, conversion quality and customer value to the decision process.
What Fractyl does not replace
Fractyl does not replace the CRM used by sales or service teams, the warehouse used for enterprise analytics, the consent and privacy work owned by the client, or the media platform where campaigns are configured.
Its job is to make the customer layer usable in activation, while keeping ownership and operating responsibility explicit.
