Microsoft Dynamics 365 Data Deduplication 101: Step-By-Step Guide

Introduction

As businesses scale their use of Microsoft Dynamics 365, manage more lead sources, and connect CRM data with other systems, keeping that data clean becomes challenging.

One of the most common issues is duplicate data in Dynamics 365.

A slightly different name, an old phone number, a list import, or a system sync can result in multiple records representing the same person or company.

Industry estimates put duplicate rates at 20% to 30% of a typical CRM database⁽ⁱ⁾.

The impact goes beyond a cluttered database.

Gartner estimates poor data quality costs businesses at least $12.9 million per year on average, while improving data quality can increase revenue by up to 66%⁽ⁱⁱ⁾.

For businesses using Dynamics 365 across connected systems, duplicates can spread and create inconsistent customer data.

Dynamics 365 offers native duplicate detection, but understanding its limitations is key to managing ongoing data quality challenges.

In this blog post, we’ll look at what causes duplicate records in Microsoft Dynamics 365, how to find and deduplicate them, where the native duplicate detection feature has limitations, and how a Dynamics 365 deduplication solution, M-Clean, can help automate the process.

What Causes Duplicate Records in Microsoft Dynamics 365?

Let’s follow one record through a typical week to see how duplicates can enter a CRM.

Say Alex is a sales representative at a mid-size B2B manufacturing company, and a prospect named James Whitfield is about to appear in Dynamics 365 in several different ways.

Manual Entry

James fills out a contact form on the company website using “Jim Whitfield” instead of “James.” Alex, who has not seen the existing record, creates a new record in Dynamics 365 instead of searching for an existing contact first. Now there are two records for the same person with slightly different names.

List Imports

Marketing runs a trade show list through an import job the following month. James’s business card contains his old job title and a slightly different phone extension. If the import criteria do not match closely enough with the existing record, another record can be created.

Lead Conversion

James eventually gets converted from a lead to a contact and an opportunity. If the process does not properly account for an existing contact, another record can be created instead of connecting the activity to the existing customer record.

System Integrations and Syncs

James is also present in the company’s marketing automation platform. When that platform synchronizes with Dynamics 365, differences in matching logic between the two systems can result in another version of the record being created or synchronized.

By the end of the quarter, James Whitfield may exist in Dynamics 365 in several forms, while Alex is working with more than one record for the same person.

This example shows why duplicates are rarely caused by one single action. They often result from several processes using different ways of identifying the same person or company.

Common sources include:

• Manual data entry
• Website and form submissions
• List and bulk imports
• Lead conversion
• CRM and marketing automation synchronization
• Third-party integrations
• Data migrations
• Multiple systems creating or updating the same customer record

The more systems and processes involved, the more important it becomes to have consistent matching and deduplication rules.

In fact, exact-match techniques alone miss an estimated 30% to 40% of real duplicates⁽ⁱⁱⁱ⁾, because names get misspelt, fields get left blank, and the same person shows up under three different email addresses across their career.

How Can You Find and Deduplicate Data in Microsoft Dynamics 365?

Dynamics 365 has a native duplicate detection capability, and it is a useful starting point for identifying potential duplicates.

Microsoft explains that duplicate detection rules use matchcodes generated from selected record fields. When records are created or updated, Dynamics 365 can compare their matchcodes with existing records and flag potential matches. Duplicate detection can also be used during data imports and through scheduled duplicate detection jobs.

Here is how the process typically works:

1. Create and Publish a Duplicate Detection Rule
A system administrator or system customizer defines which fields should be compared, such as email address, first name, last name, or other relevant fields. The rule must be published before it can be used.

2. Let Dynamics 365 Generate Matchcodes
When a duplicate detection rule is published, Dynamics 365 creates matchcodes for the relevant existing records. Matchcodes are also generated as new records are created or updated.

3. Check Records When They Are Created or Updated
When a user creates or updates a record through the supported Dynamics 365 interface, the system can compare it against existing records and display a duplicate warning when a potential match is found.

4. Run Scheduled Duplicate Detection Jobs
You can run jobs to search existing data for potential duplicates. These jobs can also be scheduled to recur at a defined interval.

5. Check for Duplicates During Imports
Duplicate detection can also be enabled when importing data, helping identify potential duplicates as records enter the system.

Once potential duplicates have been identified, users can review them and take appropriate action, including merging records where applicable.

The important distinction is that finding a duplicate and cleaning it up are separate steps. Native detection is designed primarily to identify potential matches. The actual resolution still requires a decision about which record to retain and how information from the duplicate records should be handled.

What Are the Limitations of Dynamics 365’s Native Duplicate Detection Feature?

The native feature is useful for identifying likely duplicates, but there are some practical limitations to consider when you need a broader deduplication process.

It Only Alerts, It Doesn’t Act

When Dynamics 365 detects a possible duplicate during record creation or update, it can show a warning to the user. The user still needs to review the records and decide what to do.

That works for occasional duplicates, but manual review becomes harder to manage when a business has a large existing backlog or receives duplicates regularly.

The Rule Logic Can Be Restrictive

Conditions within a duplicate detection rule are combined using logical AND. For example, if a rule compares name, email, phone, and address, the conditions need to satisfy the configured criteria for the record to be identified as a match.

That can make the rules less effective when real-world data contains variations such as missing fields, different formatting, or minor spelling differences.

There is a Five-Rule Limit Per Entity Type

Microsoft allows multiple duplicate detection rules for an entity/table, but only five can be published for the same entity type at one time.

For businesses with different matching requirements across multiple data sources or business processes, that can limit how many scenarios can be covered with native rules.

Scheduled Detection Still Requires Follow-Up

Scheduled duplicate detection jobs are useful for finding duplicates already present in the database, and they can be configured to recur. But identifying a duplicate does not automatically resolve the record.

Someone still needs to review the results and decide how the records should be merged or otherwise handled.

Deciding Which Record Should Win is a Separate Problem

Identifying two records as duplicates is only part of the cleanup process.

You also need to decide which record should remain and what should happen to the information stored across both records. Depending on the data, that could include contact details, notes, activities, or other related information.

Native duplicate detection does not provide the same configurable winning-record and field-level merge controls available in dedicated deduplication solutions.

This is where the difference between duplicate detection and deduplication becomes important. Detection helps you find potential duplicates. Deduplication involves identifying the duplicates, deciding how they should be merged, and applying those decisions consistently.

How Does M-Clean Simplify Dynamics 365 Deduplication?

M-Clean is designed to extend the deduplication capabilities available across Marketo and CRM platforms, including Microsoft Dynamics.

It can identify and merge duplicate records, support real-time deduplication, and allow businesses to configure how records and individual fields are handled during a merge.

The approach can be configured around four main areas.

System Selection

You can choose where deduplication should take place.

For businesses using Adobe Marketo Engage with Microsoft Dynamics, M-Clean can support deduplication across both systems, helping maintain consistency between marketing and CRM records. It can also be configured for the system where the cleanup is required.

Duplicate Identification

You define what should count as a duplicate based on your business requirements.

Matching can use fields such as:

• Email address
• Full name
• Company name
• Custom business fields

M-Clean also supports fuzzy matching, which can help identify variations that may not satisfy an exact-match rule. For example, “Jim” and “James” or variations in company names may be treated as potential matches depending on the configured criteria.

Winning Record Rules

Once duplicate records are identified, you can define which record should be retained.

M-Clean supports rules such as:

• Oldest record
• Newest record
• Most recently updated record
• Custom business logic

This means the decision about which record should remain can be defined in advance instead of being made manually for every duplicate.

Field-Level Merge Settings

Different records can contain different pieces of useful information. A merge therefore needs to account for more than which record wins.

M-Clean allows field-level settings such as:

• Append values
• Prepend values
• Keep the oldest value
• Keep the newest value
• Use the value from the winning record

This gives teams more control over what happens to individual fields during the merge.

M-Clean also supports one-time cleanup of existing duplicates, real-time deduplication for newly created records, and live reports that provide visibility into merged records.

Native Duplicate Detection vs. M-Clean

Conclusion

Duplicate records are an ongoing data quality issue rather than a one-time cleanup task.

They can enter through form submissions, list imports, lead conversion, manual updates, and system integrations. Dynamics 365’s native duplicate detection provides useful tools for identifying potential matches, but businesses still need a process for reviewing, merging, and managing those records.

For teams with more complex matching requirements or a growing volume of duplicate records, a dedicated Dynamics 365 deduplication tool can reduce the manual work involved in keeping the database clean.

M-Clean adds configurable matching, winning-record rules, field-level merge settings, bulk cleanup, and real-time deduplication across supported CRM and marketing environments.

Statistics References:

^{(i) & (ii)} CRM Software Blog
⁽ⁱⁱⁱ⁾ Digital Applied