How can I discard values when detecting duplicates?

Avoid false duplicates in Salesforce by ignoring default values like [Not Provided] using Delpha’s Discard Values feature. Clean your data matching and reduce scoring noise.

How to Handle Frequent Default Values That Cause False Positives in Duplicate Detection

When a field commonly contains a default or placeholder value (like [Not Provided]), it can cause false positives in your duplicate detection process. These values trigger unnecessary scoring and generate noise in your Delpha results.

This article explains how to use the Discard Values feature in Delpha to prevent such false positives and reduce background processing.

Problem Example: [Not Provided]

Imagine you receive Leads from a webform where only First Name and Email are required. If fields like Last Name or Company Name are missing, they default to [Not Provided].

Your duplicate detection rules rely on: ✅ Name + Email + Company

Now consider:

  • 1,000 Leads all have Last Name = [Not Provided]

  • Each of these matches the others based on this placeholder value

  • Result: 1,000 x 1,000 = 1,000,000 scoring operations

  • Many will clear the screening phase and be treated as potential duplicates — even though they’re not!

This clogs your system with false positives and wastes processing capacity.

Solution: Use Discard Values

Delpha allows you to ignore specific field values during the duplicate detection process using the Discard Values setting.

Step-by-Step:

  1. Open the Delpha Setup app

  2. Go to the Duplicate Settings tab

  3. Scroll to the Properties section

  4. Locate the Discard Values input

  5. Add placeholder values (e.g., [Not Provided], N/A, Unknown) as a comma-separated list

What It Does:

  • These values are ignored during scoring

  • They are treated as empty, which disqualifies them from matching logic

  • This helps reduce false positives and keeps your scoring meaningful

Example Outcome

Before Discard Values
After Discard Values

1,000,000 unnecessary scoring operations

✅ Reduced

High false-positive rate

✅ Clean, focused detection

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