Peoplewoo - Blogs #DMOPeoplewoo - Blogs #DMOhttps://www.peoplewoo.com/blogs/tag/dmoFri, 10 Jul 2026 17:01:16 +0530http://zoho.com/sites/<![CDATA[Configuring Identity Resolution Matching Rules In Data 360 | Peoplewoo Skills]]>https://www.peoplewoo.com/blogs/post/configuring-identity-resolution-matching-rules-in-data-360-peoplewoo-skillsSalesforce Data Cloud uses Identity Resolution and AI-driven matching rules to merge duplicate records from multiple platforms into a single, accurate Unified Customer Profile.]]>

Identity Resolution in Data 360

Managing customer data across multiple platforms is a major challenge for enterprise businesses. A single customer might use different names, nicknames, or emails across your website, mobile app, and loyalty programs. Without a way to connect these dots, you end up marketing to duplicate profiles—wasting thousands of dollars.

This is where Identity Resolution in Salesforce Data Cloud steps in, combining disparate individual profiles into a single, Unified Customer Profile. Here is a quick guide on how to set it up.

Step 1: Create a New Rule Set

To start merging your data, you must tell the system which base objects to evaluate.

  • Navigate to Identity Resolution and click Create New Rule Set.

  • Select the Individual Object as the primary Data Model Object (DMO) you want to unify.

  • Define your Rule Set Name (e.g., People) and a unique Rule Set ID.

Once created, the system automatically prepares the target architecture. For every standard object (like contact point email or phone), Data Cloud creates a corresponding unified object, such as Unified Individual and Unified Individual Email.


Step 2: Configure Matching Rules

Next, you define the logic Salesforce uses to catch duplicate records. Because customers rarely provide identical information everywhere, leveraging built-in AI precision rules is essential:

  • Fuzzy First Name: Accounts for variations or typos (e.g., matching "Harsh" with "Harshit"). You can adjust the AI precision level to low, medium, or high.

  • Exact Last Name: While people play with their first names, surnames usually stay consistent. Setting this to "Exact" keeps your matching accurate.

  • Exact Normalized Email: This looks at the core email address, ignoring minor formatting differences.

Data Quality Tip: For normalized matching to work effectively, ensure good data hygiene upfront. Validate customer emails using OTPs or double opt-ins before importing them into Data Cloud.

Step 3: Run and Wait for Reconciliation

After saving your rules, click Update Status. The backend AI will begin processing, running reconciliation rules to link matching IDs across your Salesforce CRM, Marketing Cloud subscriptions, and external data lakes.

Keep in mind that this reconciliation process typically takes 2 to 3 hours to complete. Once finished, you can view the clean, single-source-of-truth profiles in the Data Explorer, fully primed for advanced segmentation and targeted marketing campaigns.


Explore Salesforce Data 360 (Data Cloud)

Learn Salesforce Data 360 with real-world ingestion, identity resolution, segmentation, and activation workflows. Build job-ready Data Cloud consulting skills with hands-on practice and interview-focused preparation.

Live Training Udemy Data 360 Course Interview Q&A Course WhatsApp
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]]>Wed, 08 Jul 2026 13:24:53 +0530<![CDATA[Data Streams, DLO & DMO in Data 360 | Peoplewoo Skills]]>https://www.peoplewoo.com/blogs/post/data-streams-dlo-dmo-in-data-360

How Salesforce Data Cloud Works

A simple breakdown of how data moves from isolated systems into a single, unified view.

Connecting the Source: DSOs and Data Streams

Before Data Cloud can harmonize your data, it requires a secure ingestion pipeline to your external environments.

  • Data Source Objects (DSOs): These are the native objects residing inside your source systems—such as the Lead, Contact, or Account objects within Salesforce Sales Cloud.
  • Data Streams: The actual functional connections established to transition data from your DSOs into Data Cloud. Data Streams seamlessly support both real-time integrations and batch uploads.

To accelerate implementation, Salesforce offers pre-built Starter Bundles (e.g., Sales Cloud and Service Cloud bundles). Deploying these bundles automates the connection setup for standard enterprise objects, removing the need for manual configuration.


Data Categorization: Profile vs. Engagement

When connecting custom objects or external data lakes, Data Cloud requires you to categorize the incoming data schema. This taxonomy determines how data behaves during identity resolution workflows.

CategoryDefinitionEnterprise Examples
ProfilePersonally Identifiable Information (PII) defining who the customer is.Leads, Contacts, Accounts (Names, Emails, Addresses)
EngagementBehavioral tracking data detailing actions taken by the customer.Email opens, link clicks, website visits, form downloads
OtherSupplementary data that does not strictly match Profile or Engagement schemas.Product catalogs, support cases, operational logs

The Architecture: DLOs vs. DMOs

The journey from raw data ingestion to standard data modeling relies on two foundational architecture components:

 1. Source System / DSO (Raw Source)
 ↓ Connected via Data Stream
 2. Data Lake Object / DLO (Physical Storage Container)
 ↓ Standardized via Data Mapping
 3. Data Model Object / DMO (Unified Customer 360 Shape)


Creating a Data Stream automatically provisions a corresponding Data Lake Object (DLO). The DLO serves as the exact physical storage container for your raw data inside Data Cloud. However, at this stage, the data is unstructured and unaligned with the broader ecosystem.

To make this information usable, fields from the DLO must be mapped directly to a Data Model Object (DMO). DMOs are the standard semantic templates that comprise the global Customer 360 model.


4. The Foundation of Unification: The Individual Object

The single most critical DMO for true profile unification is the Individual object.

In complex multi-org enterprise environments, a single human being often exists under completely different unique keys across multiple systems. They might be tracked as a Lead ID in Sales Cloud, a converted Contact ID in a secondary business unit, and a Subscriber Key within Marketing Cloud.

By mapping every single profile-category data stream to the central Individual Object, you establish a universal anchor point. This mapping enables Data Cloud's Identity Resolution engines to successfully cross-reference disparate source records and consolidate them into one single, cohesive, and actionable customer profile.

Explore Salesforce Data 360 (Data Cloud)

Learn Salesforce Data 360 with real-world ingestion, identity resolution, segmentation, and activation workflows. Build job-ready Data Cloud consulting skills with hands-on practice and interview-focused preparation.

Live Training Udemy Data 360 Course Interview Q&A Course WhatsApp
Learn • Build • Get Certified | Peoplewoo Skills

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