Some duplicate are not detected, what can I do?

Missing valid duplicates in Delpha? Learn how to improve detection using Screening Fields, Optional rules, and better data quality practices in Salesforce.

What to Do If Legitimate Duplicates Are Not Being Detected in Delpha

If some valid duplicates are not flagged by Delpha's duplicate detection process, there are several ways to improve match detection and scoring accuracy.

This article outlines the key configurations you can adjust to optimize the detection process and identify more true duplicate pairs.

1. Add Additional Screen Fields

What Are Screening Fields?

Screening fields are used during the initial filtering phase to decide whether two records should even be considered as a potential match. By default, this relies heavily on the Name field.

If two records differ significantly in name but match on other key fields (e.g., email, phone, domain), they may be missed.

How Screening Fields Help

• ✅ Adding relevant fields (e.g., Email, LinkedIn URL) increases the chance of catching real duplicates that don't match on name alone.

• ⚠️ Using too many screening fields can increase processing time and generate low-confidence pairs (false positives).

Example: Add Website and LinkedIn URL as screening fields for Company duplicate detection or add Email as screening for Contact duplicate detection

Field	Contact A	Contact B	Screening
Name	William SMITH	Bill Charles Henry SMITH - JOHNSON	Default Screening: based on the Name ⇒ NOT DETECTED, names are too different
Email	[email protected]	[email protected]	Screen Fields: Email ⇒ DETECTED -> the pair Contact A - Contact B is sent to the scoring process

2. Review and Adjust Fields detection Rules

Mandatory vs. Optional rules

• Mandatory fields reduce false positives by requiring data in both records for scoring.

• Optional fields allow records with missing data to still contribute to a pair’s score.

📌 Tip: Consider relaxing some fields from Mandatory → Optional to allow more flexible matching.

Add New Optional Fields

• Introducing new fields as optional comparison points (like Industry, Phone, Region) can help boost the match score and uncover missed duplicates.

3. Improve Your Underlying Data Quality

Duplicate detection accuracy directly depends on the quality and completeness of the data being evaluated.

Steps to Take:

• Run Data Quality assessments in the Data Steward View

• Apply Delpha’s recommended values to improve data validity, consistency, and completeness

• Ensure key fields used in deduplication (e.g., Name, Email, LinkedIn) are up to date

Summary

Improvement Area	What It Does	Recommendation
Screening Fields	Helps include more potential duplicate pairs	Use sparingly
Detection Rules	Impacts score calculation logic	Adjust Mandatory settings, add Optional fields
Data Quality	Better input = better detection	Apply fixes from Steward View