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Identity Resolution Matching & Reconciliation Rules In Data 360 | Peoplewoo Skills

Fri, 10 Jul 2026 10:54:17 +0530

Identity Resolution Matching In Salesforce Data 360

Imagine a customer signs up on your website as "Hers Suri" from "New Delhi." Meanwhile, your CRM already lists them as "Harshit S" from "Delhi." They share the exact same email address, meaning they are the same person. However, because the data is split and slightly conflicting, your system sees two different people.

This is where Identity Resolution comes in. It is a powerful feature within Salesforce Data 360 that merges duplicate records mapped to the Individual Data Model Object into a single, cohesive Unified Individual ID.

To achieve this perfect data harmony, Identity Resolution relies on two critical pillars: Matching Rules and Reconciliation Rules.

1. Matching Rules: Finding the Duplicates

Matching rules act as a detective, scanning your data streams to identify which records belong to the same human being. You can leverage two types of matching:

Exact Match: Looks for identical data, such as the exact same email address or first name ("Hers").
Fuzzy Match: Catches slight variations or partial data, recognizing that "Suri" and "S", or "New Delhi" and "Delhi", are likely matches.

By combining these (e.g., exact email + fuzzy last name), the system confidently flags the records as duplicates.

2. Reconciliation Rules: Resolving the Conflicts

Once the system groups the duplicate profiles, a new question arises: Which data wins? Should the unified profile say "Delhi" or "New Delhi"? Reconciliation rules resolve these conflicts by defining your preferred values based on three main criteria:

Most Trusted Source: You can prioritize a specific platform. If your CRM is highly accurate, the system will select the CRM's value ("Delhi").
Newest Value: The system chooses the most recent data. If the website signup happened today, it will pull the website's value ("New Delhi").
Most Frequent Value: If the value "Delhi" appears across both a lead and a contact record, but "New Delhi" only appears once on the website, the system picks the most frequent value ("Delhi").

The Audit Trail: Unified Individual Link

Once the heavy lifting is done, how do you track the changes? Salesforce Data 360 provides the Unified Individual Link object. Accessible via the Data Explorer, this object links your new Unified Individual ID back to the original source individual IDs, offering total transparency.

The Bottom Line: Matching rules identify your duplicates, while reconciliation rules solve your data conflicts—giving you a flawless, single source of truth.

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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.

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Configuring Identity Resolution Matching Rules In Data 360 | Peoplewoo Skills

Wed, 08 Jul 2026 13:24:53 +0530

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.

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Individuals In Data 360 | Peoplewoo Skills

Mon, 06 Jul 2026 12:41:58 +0530

Individual

If you’ve ever dug into Salesforce Data Cloud (formerly known as Salesforce CDP / Data 360), you’ve probably run into the Individual object. It sits under the "Profile" category, and honestly, it is the secret sauce to making sense of all your customer data.

But what exactly is it, and why is it so important? Let's break it down in plain English.

The Big Problem: One Person, Many Identities

Imagine a customer named Sarah. Sarah interacts with your business in three different ways:

As a Lead: She fills out a form on your website. Salesforce creates a Lead record with a specific Lead ID.
As a Contact: Your sales team closes a deal with her. That lead gets converted into a Contact record with a completely new Contact ID.
As a Subscriber: She gets synced to Salesforce Marketing Cloud Engagement to receive your newsletters. Now she has a Subscriber ID.

Technically, Sarah has three different identities across your systems.

If you look at your engagement data—like who is opening emails, clicking links, or opening support cases—it’s scattered. To your systems, Sarah looks like three entirely different people.

Enter the "Individual" Object

This is where the Individual Data ModelObject (DMO) comes to the rescue.

In Data Cloud, your data is divided into different categories, like Engagement Data (clicks, opens, visits) and Profile Data (who the person actually is). The Individual object belongs to the Profile category.

Think of the Individual object as a universal bucket. When you bring your leads, contacts, and subscribers into Data Cloud, you don't just leave them in their separate corners. You map all of them to the Individual object.

By pointing all these different profile records to the Individual object, you are telling Data Cloud: "Hey, all of these records represent actual human beings. Keep them here."

How Data Cloud Unifies Your Data

Mapping everything to the Individual object is just step one. The real magic happens with Identity Resolution Rules.

Because you mapped your Leads, Contacts, and Subscribers to the Individual object, you can now write rules to link them together. For example, you can tell the system:

"If a Lead, a Contact, and a Subscriber all share the exact same email address, they are actually the same person."

Data Cloud runs these rules against the Individual object. It matches the duplicates and merges those three separate identities into a single, master profile called the Unified Individual.

Why This Matters

Without the Individual object and identity resolution, your data is siloed. You might send a marketing email to Sarah the "Subscriber" about a product that Sarah the "Contact" already bought.

By unifying everything into a single Unified Individual:

You get a true 360-degree view of your customer.
Your marketing becomes smarter because you know exactly who you are talking to.
You stop wasting effort on duplicate records.

In short, the Individual object is the foundation that allows Salesforce Data Cloud to bridge the gap between fragmented data and a perfect, unified customer experience.

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Mapping Data Streams To DLOs and DMOs In Data 360 | Peoplewoo Skills

Fri, 26 Jun 2026 10:22:36 +0530

Raw Data to Customer 360

Unlocking a true 360-degree view of your customer requires pulling data from various platforms into one centralized hub. In Salesforce Data Cloud, this architectural journey follows a seamless path from raw source data to a unified profile.

Here is a quick, easy guide to understanding how your data moves through the cloud.

1. Connecting Data via Data Streams

Every source system contains native assets known as DataSourceObjects(DSOs)—think of standard objects like Leads, Contacts, or Accounts. To bring this data into Data Cloud, you must establish a DataStream.

Starter Bundles: Salesforce provides pre-built bundles for Sales and Service Cloud, allowing you to install and connect standard objects instantly.
Custom Objects: For unique data sitting outside of standard bundles, you can manually ingest them by selecting the "All Objects" option.

2. Categorizing Your Data

When data streams are configured, the data must be classified into one of three categories. These categories are vital for setting up rules around how data is handled later:

Profile: Contains Personal Identifiable Information (PII) like names, phone numbers, and addresses.
Engagement: Tracks real-time or batch behavioral data, such as email opens, link clicks, or website visits.
Other: Reserved for auxiliary data that does not fit into profile or engagement definitions.

3. From Data Lake Objects (DLO) to Data Model Objects (DMO)

Creating a data stream automatically generates a Data Lake Object (DLO). Think of a DLO as the physical container that holds your raw data.

To make this raw data usable, you must map the DLO to a Data Model Object (DMO) within the standard Customer 360 model. While starter bundles map fields like Contact Point Phone or Contact Point Email automatically, custom data requires manual mapping to organize it correctly.

4. Unifying Profiles with the Individual Object

A single customer might exist as a Lead in one Salesforce org, a Contact in another, and a subscriber in Marketing Cloud. To prevent duplicate profiles, Data Cloud maps all profile data to a core standard DMO called the IndividualObject.

By linking various system IDs to a single Individual ID, Data Cloud resolves identities across multiple connected orgs, creating a singular, unified source of truth for your entire enterprise.

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What Is Salesforce Data 360(Data Cloud)? | Peoplewoo Skills

Thu, 25 Jun 2026 13:29:34 +0530

Salesforce Data 360

Imagine running a growing business where your customer data is scattered everywhere. Your website tracks browsing history, your mobile app logs transactions, your CRM manages sales leads, and your marketing tools send out emails and WhatsApp messages.

The major challenge? You cannot see a single customer’s journey in one place. Instead, you are dealing with duplicate profiles across different systems. This duplicate issue weakens your personalization, skews your reporting, and breaks your sales forecasting.

This is exactly where SalesforceData360 (also widely known as DataCloud) comes to the rescue. It acts as a central Customer Data Platform (CDP) that connects, unifies, and activates your data.

The Three Pillars of Data 360

Salesforce Data 360 solves the distributed data crisis through a powerful, three-step process:

1. Connect Internal and External Sources

You don’t need complex coding to bring your data together. Data 360 features inbuiltconnectors for Salesforce apps, Meta ads, Google Ads, Amazon AWS, and Snowflake. It uses datastreams to smoothly pull all this distributed information into a single, centralized data lake.

2. Unify via Identity Resolution

A single customer might have a Meta ID, a CRM Lead ID, and a Marketing Cloud subscriber key, causing them to look like three different people. Data 360 uses IdentityResolution rules—such as matching exact email addresses or fuzzy first names—to clean up the clutter. The result is a single UnifiedProfile where every interaction is tied to one unique ID.

3. Segment and Take Action

With a clean, unified database, you unlock advanced cross-objectsegmentation. You can seamlessly group customers based on combined data from your website, CRM, and marketing history. Finally, use DataActions to instantly trigger a Marketing Cloud journey or a Salesforce Flow to deliver the perfect message at the perfect time.

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Data Streams, DLO & DMO in Data 360 | Peoplewoo Skills

Fri, 26 Sep 2025 15:27:25 +0530

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.

Category	Definition	Enterprise Examples
Profile	Personally Identifiable Information (PII) defining who the customer is.	Leads, Contacts, Accounts (Names, Emails, Addresses)
Engagement	Behavioral tracking data detailing actions taken by the customer.	Email opens, link clicks, website visits, form downloads
Other	Supplementary 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.

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