Salesforce & Veeva – CRM for life sciences
Life Sciences Cloud, Marketing Cloud, Data 360, Veeva CRM & Vault – connected to your warehouse landscape via zero-copy instead of data copies. One continuous HCP/HCO engagement loop: identity-safe, consent-driven, MLR-proof.
Salesforce Life Sciences Cloud
The new CRM suite for pharma & medtech – HCP engagement, compliance and field-force processes in one system.
Marketing Cloud & Data 360
Unified data layer (formerly Data Cloud) with zero-copy to the warehouse – the basis for segmentation and activation.
Veeva CRM & Vault
The life-sciences standard: Veeva CRM for the field force, Vault for MLR-compliant content management.
The bridge between the worlds
The real value is created in the integration: connecting Salesforce/Veeva CRM, AEM web and content, the data layer (Data 360, formerly Data Cloud) and your data warehouse or lakehouse (Snowflake, Databricks, BigQuery) into one consistent system. That bridge between the Adobe and Salesforce worlds is exactly what I build – vendor-neutral and aligned to your maturity level. And with Agentforce, the agentic layer arrives: agents that must run on the same profile and consent foundation as every journey – otherwise they automate right past your compliance framework.
Reference setup life sciences
From MLR review to the field force
Veeva and Salesforce as one system: approved content, one HCP profile, orchestrated channels – with the rep at the center.
← Swipe sideways to explore →
How to read it: only MLR-approved content from Vault reaches the channels. The CRM sync keeps Veeva and Salesforce consistent, Marketing Cloud delivers the journeys – and the field force gets engagement back as next best action.
Architecture logic: CONTENT → CRM SYNC → DATA 360 → ACTIVATION
following the official Salesforce Architects reference diagrams and the Life Sciences Cloud developer guide
FAQ
Frequently asked questions
Salesforce or Veeva – which fits us?
That depends on your sales model and regulation: Veeva is the standard in pharma field sales, Salesforce is strong in marketing automation and service. Often the answer is a clean integration of both worlds rather than either-or.
Can Adobe and Salesforce/Veeva run in parallel?
Yes – that’s exactly what architecture is for. Profiles, consent and events are synchronized via defined interfaces, so each system plays to its strength without creating data silos.
What happens to our existing CRM data?
Existing customer and consent data isn’t replaced but connected: it remains the source of truth in the CRM and becomes usable for orchestration and personalization.
How does automated communication stay MLR-compliant?
Through modular, pre-approved content and traceable rules: every delivery is based on approved building blocks, every step is documented and auditable.
Data platform architect: the data layer underneath Salesforce
Marketing Cloud, Life Sciences Cloud and Veeva are applications. Underneath them sits a layer that rarely belongs to anyone, and that gets decided anyway – usually by implication, during the first integration project.
What gets decided at this layer
- Where the regulated data actually sits. In an org that has grown for years, nobody is certain which fields across which objects carry health or financial data. Classification at scale is the precondition for any reliable statement about consent, deletion or activation – not a compliance afterthought.
- Who owns what in the security model. Field and object level, encryption, Shield. The question is rarely what is possible, but who makes the call and who still maintains it two releases later.
- Change Data Capture. If Salesforce is to push changes outward in real time, CDC is the documented route. The architectural question is not whether, but which objects, at what latency expectation – and what happens when the consumer is down for an hour.
- Data that is fit for AI at all. Before Agentforce or Einstein return anything you can rely on, the layer underneath has to hold: duplicates resolved, fields named unambiguously and meaning the same thing everywhere, and a record of which data may be used. Most AI efforts fail one layer below where they started.
- Sandboxes. Production data in a sandbox is a data protection incident on a delay. Masking and seeding belong in the architecture, not on the checklist before the audit.
When you do not need this
- One org, one team, no interface to the outside. That is admin work, and you probably already have it covered.
- The open question is a single report. That is BI, not architecture.
- You are considering Data Cloud because it says AI on the box. Then the data question comes before the licence question, and it can be settled in a few days.
If the data layer underneath your Salesforce currently belongs to nobody, write to me.