Unified customer data for the clouds that sit above it.
Data Cloud is the foundation Agentforce and the rest of the Salesforce stack increasingly depend on. We design it once, correctly — identity resolution, data model, activation — so the clouds above it get clean context instead of garbage.
Data Cloud
Data Cloud is Salesforce's customer data platform (CDP) — identity resolution, unified profiles, calculated insights, segmentation, and activation to Marketing, Service, Sales, and external systems. It's where unified customer context lives for everything else in the Salesforce platform.
Fit signals.
- You have 3+ customer data silos (ecom, support, marketing, sales)
- Agentforce or Einstein outputs would improve materially with unified profiles
- Segmentation is manual and brittle, living in marketing automation
- Marketing wants real-time activation that your warehouse can't deliver
- You're planning a regulated-industry AI rollout and need grounded customer context
What we deliver in Data Cloud.
Every capability below is practiced across multiple production engagements — not a scoping checklist.
Data model
- Customer 360 data model design
- Data Streams — ingest from Salesforce, files, APIs, SaaS
- Data Lake Object vs Data Model Object modeling
- Retention, consent, and data residency configuration
Identity & insights
- Identity resolution ruleset design + tuning
- Unified profile testing + QA
- Calculated insights (SQL) — LTV, engagement scores, RFM
- Streaming vs batch insight tradeoffs
Activation
- Segments with freshness SLAs
- Activation to Marketing Cloud, Service, Sales
- External activation (Google Ads, Meta, TikTok, custom)
- Reverse ETL + warehouse round-trip patterns
Governance
- Consent management integration
- Audit + data lineage
- PII minimization + access controls
- Cost management (cred usage, ingestion, storage)
The shapes this work
usually takes.
Data Cloud foundation
Typical: 12–16 weeks. Data model + identity resolution + first three activations. Done before any AI rollout that relies on it.
Identity resolution rework
Typical: 6–10 weeks. Existing Data Cloud is live but profiles are wrong. Narrow, high-leverage engagement.
Activation expansion
Typical: 8–12 weeks. Adds segments + external activations + real-time triggers to a working Data Cloud.
Data Cloud managed service
Monthly. Model hygiene, cred usage reviews, activation SLA monitoring, new-source onboarding.
Pitfalls we've seen
and how we avoid them.
Ship it before the data model is right
Identity ruleset tuned during beta, never revisited. Downstream profiles are wrong. We invest upfront and re-verify every quarter.
Warehouse rivalry, not complement
Teams try to replicate the data warehouse. Data Cloud is for activation and profile-driven use cases, not analytics primacy.
Cred blowout
Streaming ingestion of low-value data. Costs explode. Usage monitoring + policy up front is non-negotiable.
Segmentation without freshness SLAs
Marketing activates stale segments, campaign performance suffers. Every segment needs a documented freshness contract.
Common questions about Data Cloud.
Depends on where your activation lives. If activation is primarily into Salesforce (Service, Marketing, Sales, Agentforce), Data Cloud is the better foundation. If your core activation is external and your analytics team owns the customer data story, a warehouse + specialist CDP may fit.
Data Cloud on your roadmap?
Thirty minutes with Ananya. Architecture sketch, candid second opinion, scope estimate — no slides.
Book the call