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Salesforce Architecture, Data & AI

Salesforce Data 360

A governed customer-data foundation on Salesforce Data 360 that resolves records from multiple enterprise systems into one unified customer profile. It is the trusted base for CRM experiences, analytics and AI.

  • Sector Enterprise data · Confidential engagement
  • Client Enterprise organisation
  • Role Salesforce architect and Data 360 consultant
Duplicate records merged into a single customer identity
One profile
Governance designed in from day one
Governed
Unified customer context for CRM, analytics and AI
AI-ready
Data 360 record page: store transactions unified onto the contact
The starting point

The challenge

Customer information was spread across systems, so no team had a consistent, contextual view of the customer. The organisation needed better data quality, governance, and a foundation that downstream AI could rely on.

The solution

What we built

  • Ingestion: enterprise sources brought into Data 360 through data streams.

  • Mapping and transformation: source data standardised and mapped to a common customer data model.

  • Identity resolution: rulesets (match and reconciliation rules) that merge duplicate records into a single customer identity.

  • Unified profiles: one profile per customer.

  • Governance: built in from the start.

  • AI-ready context: unified customer context available to Salesforce CRM, analytics and AI.

Architecture

How the system works

Architecture: enterprise source systems feed a governed Data 360 pipeline of ingestion, mapping and transformation, identity resolution and a unified customer profile, which serves Salesforce CRM, analytics and AI
Data 360 pipeline from enterprise sources to a unified customer profile, governed throughout.
Identity resolution example with synthetic data: three source records for Jane Doe pass through match and reconciliation rules and merge into one unified profile
Identity resolution: match and reconciliation rules merge duplicate records into one profile. Illustrative example with synthetic data.
Engineering

How it was engineered

  • Foundation before features. The goal was a reliable data foundation, not just moving records into Salesforce, so quality is solved once, upstream of every consumer.

  • Sources chosen by use case. Each candidate source was assessed on relevance, ownership and the use cases it had to support before ingestion.

  • One profile per customer. Mapping, transformation and identity resolution work together to produce a single, consistent identity.

  • Governance from day one. AI use cases depend on trusted data, so governance was designed in rather than retrofitted.

Scope

What was delivered

01

Unified customer profiles

02

Consistent customer context across Salesforce experiences

03

A governed base for analytics and AI

Gallery

Screens and diagrams

Technology

Stack

Data 360Customer 360Salesforce CRMData IntegrationIdentity ResolutionData Modelling

Need something similar?

We can scope and build a system with comparable capabilities, shaped around your own workflows, data and integrations.