Identity Resolution
Matching rules, keys, and conflict resolution across sources.
Data Cloud and Data Unification
A single customer view — without moving your entire organization to the CRM.
Designing a unified data layer: identity resolution, harmonization, accessing information from external systems, and feeding processes, reports, and Agentforce from a controlled source.
Capability Map
What's Included and in What Order
This map illustrates the work layers in this service, from infrastructure to user-facing capabilities.
Data Cloud — Capability Map
Identity Resolution
Matching rules, keys, and conflict resolution across sources.
Harmonization
Normalizing fields and values for comparison.
External Information Access
Viewing data from a remote system instead of redundant copying.
Segments and Audiences
Building audiences based on a unified source for use in marketing and service.
Process Feeding
Triggering automation, alerts, or tasks based on data events.
Governance and Quality
Ownership, quality metrics, and deviation tracking.
The Background
What Truly Matters Here
Data unification is not a technical project but a series of decisions: what constitutes a customer entity, which identifiers determine a match, and what to do when conflicts arise between sources.
Not all data needs to move to the CRM. Some data is better viewed from a remote location, and some is best brought in only in an aggregated form. This decision impacts both performance and cost.
Data Cloud becomes particularly significant before AI: an agent relying on ununified information will provide answers that sound plausible but are incorrect.
Areas of Work
What we actually do
Harmonization
Normalizing fields and values for comparison.
External Information Access
Viewing data from a remote system instead of redundant copying.
Segments and Audiences
Building audiences based on a unified source for use in marketing and service.
Process Feeding
Triggering automation, alerts, or tasks based on data events.
Governance and Quality
Ownership, quality metrics, and deviation tracking.
Decision Matrix
Decisions that determine the outcome
| Decision | Option A | Option B | What is Decisive |
|---|---|---|---|
| Copying vs. Viewing | Copying the data to CRM | Accessing the remote source | Usage frequency and performance sensitivity |
| Unification Key | Technical identifier | Complex business key | Reliability of identifiers in each source |
| Conflict Resolution | The most recent source wins | Defined source hierarchy | Is there an agreed single source of truth? |
| Initial Scope | Customer entity only | Customer and transactions | Maturity of the sources |
Copying vs. Viewing
- Option A
- Copying the data to CRM
- Option B
- Accessing the remote source
- What is Decisive
- Usage frequency and performance sensitivity
Unification Key
- Option A
- Technical identifier
- Option B
- Complex business key
- What is Decisive
- Reliability of identifiers in each source
Conflict Resolution
- Option A
- The most recent source wins
- Option B
- Defined source hierarchy
- What is Decisive
- Is there an agreed single source of truth?
Initial Scope
- Option A
- Customer entity only
- Option B
- Customer and transactions
- What is Decisive
- Maturity of the sources
How We Work
Execution stages
- 01
Source Mapping
Which systems hold which entities.
- 02
Defining a unified entity
Fields, keys, and matching rules.
- 03
Harmonization
Normalizing values and formats.
- 04
Matching quality check
Measuring rate of false positives and misses.
- 05
Consumption enablement
Connecting to reports, processes, and Agentforce.
- 06
Ongoing governance
Defined quality metrics and ownership.
Continue from here
Related services and guides
Frequently Asked Questions
Frequently Asked Questions
When is Data Cloud justified?
When customer identity is scattered across multiple systems, and basic questions cannot be answered without manual consolidation. If there is a clear single source of truth, a simpler solution will suffice.
Does this replace Data Warehouse?
No. A data warehouse is designed for broad historical analysis, while a customer data layer is designed for operational activation in near real-time. In many organizations, both coexist and complement each other.
What is the connection to Agentforce?
An agent can only be accurate when the information behind it is unified and controlled. Data unification is often a true prerequisite for business use of AI, not an optional improvement.
How long does identity unification take?
The time is determined by the quality of identifiers in the sources. When reliable business identifiers exist, the process is short; when matching relies on name and address, a longer calibration round is required.
The Next Step
Unified Data Layer Design
We will examine the sources and the quality of identifiers — and define a realistic unification path.