Ensuring Data Integrity: How to Handle Duplicate Data in Salesforce Data Cloud

80% of new records from integrations are duplicates, and across all sources, nearly half of your data could be duplicates too.^[i]

That’s a lot of clutter messing with your CRM, slowing teams down, and making reporting less reliable.

The sales team is chasing the same leads. Marketing is sending emails to outdated contacts. Service teams are dealing with confused customers whose records don’t match. When your data isn’t clean, everything feels harder than it should be.

Traditional deduplication tools help, but let’s be honest—they take too much manual effort and don’t always scale. 

Salesforce Data Cloud’s duplicate management changes the narrative with AI-driven identity resolution and real-time data unification, so you always have a single, accurate source of truth without the extra work.

In this blog post, we’ll explain how Salesforce makes duplicate management effortless. Let’s dive in and get that data cleaned up!

What is Duplicate Data and Why Should You Be Aware?

Meet Sasha. She’s a sales manager who just closed a deal with a new client, Lisa Carter from DQ Corp. 

Excited, she logs the details in her CRM. But there’s a catch—Lisa is already in the system, just under a slightly different email. 

Now, there are two Lisa Carters, each with incomplete and scattered information.

At first, it seems like no big deal. But soon, marketing sends duplicate emails, customer support struggles to find the right details, and finance can’t reconcile invoices properly. Teams start working with conflicting data, reports become unreliable, and customer experience takes a hit.

While data is the lifeblood of any organization, duplicate data acts like a growing tumor, spreading quietly, clogging up systems, and making everyday operations harder than they should be. 

It’s easy to overlook at first, but as duplicates pile up, so does the chaos they bring.

4 Smart Ways for Efficient Data Duplication Management With Salesforce Data Cloud

To effectively manage duplicates, understanding the fundamentals of matching rules is crucial. These rules apply to Salesforce’s solutions, third-party tools, and Data Cloud’s identity resolution features. You define criteria using two or more fields – if these fields match or are sufficiently similar, the records are considered duplicates.

• Matching for fields like names can be specified as Exact or Fuzzy (e.g., Bob vs. Robert).
• Salesforce Data Cloud intelligently handles formatting differences, recognizing similar phone numbers as identical despite variations (e.g., +1 415-555-1212 vs. (415) 555-1212).
• The design of match rules significantly impacts outcomes.
  • Using multiple fields can increase accuracy but may also exclude legitimate matches due to overly strict criteria.
  • Minimal criteria can lead to false positives, especially when unreliable data is present, such as ‘[email protected]’ or ‘[email protected].’

1. Evaluating Match Rule Scenarios

You’ll need to evaluate the outcomes for different match rule scenarios:

• Exact First Name, Exact Last Name, Exact Email
  • No records match.
  • Omitting relevant fields can lead to lost opportunities.
• Fuzzy First Name, Exact Last Name, Exact Email, Exact Phone, Exact Mobile
  • No records match.
  • Comparing data only within the same fields can miss opportunities when a denormalized data model is used (e.g., Phone vs. Mobile).
• Fuzzy First Name, Exact Last Name, Exact Email
  • The first two records match even though the email addresses are different.
  • This may or may not be a correct match, depending on whether the address or phone number is a personal or corporate contact point.
  • The worst-case scenario is when three records match incorrectly, leading to data loss.

Best Practices for Defining Match Rules

• Ensure match rules are comprehensive yet flexible to capture all potential duplicates without being overly restrictive.
• Avoid overly stringent rules that might exclude valid matches or fail to distinguish between unique entries.

2. Identifying Impactful Fields for Matching

If you’re a CRM admin or architect, familiarize yourself with various contact points within Contact, Lead, or Account records:

• Beyond standard Email and Phone fields, consider additional URL or String fields for matching.
• Data profiling helps identify the most effective fields by analyzing data types, distinct ratios, and PII classifications.

Best Practices for Field Selection

• Use data profiling to identify and leverage the most effective fields for duplicate matching.
• Do not overlook additional fields that could provide crucial matching data.
• Understand the nature of stored data within fields:
  • Determine if Phone or Email fields pertain to the individual, associated Account, or both.
  • If clear rules exist for certain record types (e.g., 90% of records refer to individuals, others to organizations), define precise matching rules accordingly.

3. Identifying Problematic Field Values

Common issues can increase the likelihood of incorrect matches:

• Defaulting business Contact or Lead information to company addresses.
• Mandatory validation rules force users to fill fields, leading to invalid entries.
• Users enter personal instead of official email addresses.

Best Practices for Data Cleanup

• Identify contact point values that appear disproportionately in Phone, Email, or Address fields.
• Assess and classify field values as:
  • Invalid (e.g., junk values).
  • Wrong context (e.g., organization vs. person).
  • Valid and useful.
• Perform data cleanup in a non-production environment first, ensuring a backup is available.

4. Handling Unmatchable Records

Once data is cleansed, some records may lack sufficient information for effective matching.

• Classify records based on matchability and business importance.
• Use formula fields in Salesforce CRM or Data Cloud to identify records with valid or invalid contact points.
• Leverage data profiling insights to build formula-based logic that identifies patterns in bad data.

Best Practices for Unmatched Records

• Use matchability formulas as a key filter in your duplicate management strategy.
• Do not process unmatched records without assessing their business relevance.
• Enrich important but unmatchable records through targeted data improvement efforts.
• Monitor unmatchable records using Data Quality KPIs to track improvements over time.
• Implement data governance strategies to archive or purge unimportant/unmatchable records.

Summary

Managing duplicates in Salesforce requires more than just merging records; it involves:

• Understanding when to merge, when to create unified profiles, and when to enrich data.
• Assessing matchability to improve data integrity.
• Using Data Cloud’s unified profiles to maintain relationships without losing critical details.
• Implementing a strategic approach that enhances CRM functionality while ensuring compliance and usability.

This holistic strategy ensures that every data management decision supports business objectives, enhances user experience, and maintains high-quality CRM data.

Statical References
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