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Build an AI-ready customer profile by starting with the decisions an AI system must support, then inventorying and mapping the data needed for those decisions, resolving identities with explicit rules, and enforcing governance before making profiles available to applications. A unified profile can give an AI workflow useful context; it does not, by itself, make the workflow accurate or safe.
1. Define the decisions the profile must support
Start with the customer-facing or internal task, not a platform purchase or a list of fields already available. Specify what the AI application needs to answer or do, who will use it, and what decision depends on the profile. A support assistant, for example, might need a verified customer identifier, recent orders, and relevant service interactions; a different task may need different data.
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For each use case, write down the minimum attributes and events required, how current they must be, and which actions the AI may take. Exclude fields that have no clear purpose. This keeps collection and access proportionate to the intended use.
2. Find the data and assess its quality
Map where customer information is created and stored, who owns each source, and how it can be used. Common sources include CRM systems, websites and mobile apps, contact centers, email interactions, point-of-sale systems, and transaction records. AWS describes a CDP architecture drawing on these kinds of interaction and system-of-record sources, while Salesforce recommends assessing source locations, identifiers, shared fields, customer journeys, segmentation needs, and data quality.
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Use these questions for each source:
- Where is the data located, and who is responsible for it?
- How are people or accounts identified in this system?
- Which fields overlap with other sources, and what do they mean?
- How often is the data updated, and what freshness can the application expect?
- What formats, missing values, duplicates, or known errors affect it?
- What permissions or sharing restrictions apply?
Map important customer interactions across the journey as well as static records. A profile built from a CRM alone can omit behavior or service history that is essential to a particular task. Conversely, adding every available event can introduce noise and unnecessary access.
Sources: AWS Guidance for Customer Data Platform on AWS; Salesforce Trailhead: Creating Unified Profiles.
3. Define a shared customer data model
Before trying to unify records, define common entities, field meanings, allowed formats, and authoritative sources. Decide, for example, whether a value represents a person, an organization, an account, or an interaction; whether dates use a consistent time zone; and which system is authoritative for a verified contact detail.
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Source: Salesforce Help: Customer 360 Data Model.
4. Resolve identity, then reconcile profile values
Identity resolution answers which source records belong to the same person or entity. Reconciliation answers which value should appear when linked records disagree. They are separate decisions and need separate rules.
Choose match rules for the use case
Use identifiers that are available and reliable enough for the task. Depending on the data, matching may use exact values, normalized values, or fuzzy comparisons. A fuzzy match can help where formats vary, but it can also join records that belong to different people. There is no universal confidence threshold: define one for the organization and test it against representative records.
Set reconciliation rules for conflicting values
For each attribute, decide whether one source takes precedence, whether the newest valid value wins, or whether a different rule applies. Keep the reason for the selected value understandable. A match rule that links two records does not determine which address, preference, or other field is correct.
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Test both false merges and missed matches
Review examples where distinct people could be joined and examples where one person appears under multiple identifiers. Measure and investigate both errors before exposing results to customer-facing AI. Preserve links to contributing source records so teams can inspect and correct the underlying relationship.
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Sources: Salesforce Trailhead: Understand Unified Profiles and Their Impacts on Data Strategy; Salesforce Architects: Data 360 Architecture.
5. Preserve meaning, freshness, and lineage
A profile should not hide where its values came from. Retain source references and relevant timestamps or freshness context, and record how an attribute was selected or transformed. Keep verified source fields distinct from summaries or inferred attributes generated by an AI system; a generated statement should not silently overwrite a verified value.
Provide a correction path for wrong identity links and profile attributes. Corrections should flow to the responsible data owner or system of record where appropriate, rather than merely masking the problem in one downstream view.
6. Make privacy and access part of the design
Define permitted purposes, role-based access, retention, and how consent or customer preferences affect use. Specify which fields each AI workflow may retrieve, and prevent a profile’s broad availability from becoming blanket permission to use every field. Salesforce’s data model includes a privacy subject for certain privacy preferences, and its architecture guidance discusses consistent access controls for data use, including generative-AI retrieval.
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Legal obligations depend on geography, data type, and use. These architecture sources do not establish a universal compliance checklist, so have qualified privacy and legal teams determine the requirements for the organization and the particular workflow.
Sources: Salesforce Help: Customer 360 Data Model; Salesforce Architects: Data 360 Architecture.
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Choose an access pattern and freshness level that fit the task and architecture. A use case that depends on recent activity may need a different update pattern from one based on stable account information. Define the interface, permitted fields, and expected availability as implementation requirements; the cited architecture guidance does not prescribe one universal interface or latency target.
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- Identity match quality, including false merges and missed matches.
- Missing or invalid values in fields required by the use case.
- Freshness of data relative to the task’s needs.
- Whether access policies are applied as intended.
- Whether the AI task performs acceptably with the profile context.
A unified profile is an enabling data layer, not a guarantee of accurate AI output. Evaluate the application itself as well as the data it receives. AWS describes processed profile data as available for analysis and collaboration in a controlled environment; Salesforce describes unified profiles as supporting segmentation and activation.
Sources: AWS Guidance for Customer Data Platform on AWS; Salesforce Trailhead: Creating Unified Profiles; Salesforce Trailhead: Understand Unified Profiles and Their Impacts on Data Strategy.
Should you build the profile yourself or use a CDP?
A customer data platform (CDP) can implement parts of this pattern, such as bringing sources together, resolving identities, and making unified profiles available for activation. The choice should follow the use cases, data landscape, and governance requirements—not replace decisions about them.
Compare approaches against the capabilities and effort your organization actually needs:
- Coverage of source systems and fit of available connectors.
- Control over identity matching and reconciliation rules.
- Ability to retain source lineage and support corrections.
- Data freshness and downstream activation patterns.
- Privacy, permissions, and governance controls.
- Fit with existing cloud and data platforms.
- Implementation and ongoing operating effort.
Official product and architecture materials establish these as relevant capability areas, but they do not provide a neutral comparable scorecard or a common pricing basis. Assess options against documented requirements and comparable evidence rather than assuming a product purchase will resolve data quality or governance problems.
For context on platform approaches, see SAP Customer Data Platform product overview and Oracle Unity Data Platform.
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