Dynamics 365 data quality refers to how accurate, complete, consistent, timely, unique and usable the information within a Microsoft Dynamics 365 environment is.

Good-quality CRM data gives employees confidence that customer records accurately represent the people and organisations they work with.

Poor-quality data does the opposite.

A CRM might be technically working exactly as designed while users still experience inaccurate reports, duplicate customers, incomplete records and unreliable automation.

In these situations, the CRM itself may not be the real problem. The quality of the data inside it may be.

Duplicate records

Two or more Accounts, Contacts or Leads may represent the same real-world entity.

Duplicates can be introduced by manual data entry, integrations, imports, different naming conventions and inadequate matching rules.

Incomplete data

Important attributes such as addresses, telephone numbers, email addresses or classifications may be missing.

This reduces the usefulness of a customer record and can limit segmentation, reporting and automation.

Inconsistent data

The same information may be recorded in different ways.

For example, company names, country values, telephone formats, addresses and other information can follow different standards depending on how and where they were entered.

Incorrect or outdated information

Customer information changes.

Employees move roles. Businesses relocate. Contact details change. Organisations merge.

Without ongoing management, previously accurate customer data can gradually become unreliable.

Fragmented customer information

Different systems or CRM records can contain different portions of the customer's history.

This prevents users from seeing a complete customer relationship and makes it more difficult to establish a trusted Single Customer View.

Poor-quality Dynamics 365 data can have consequences throughout the organisation.

Reporting

Reports and dashboards can only reflect the records on which they are based.

Duplicates or incomplete records can distort customer counts, activity information and other metrics.

Productivity

Employees may spend time searching for the correct record, removing duplicates and manually checking whether information is accurate.

Sales and marketing

Fragmented customer information can affect segmentation, sales activity, account management and communications.

Automation

Automated processes depend on consistent inputs. Poor data can therefore cause workflows to produce inconsistent or undesirable outcomes.

AI

Microsoft Copilot and other AI technologies can consume information held within organisational systems.

AI does not automatically make unreliable source information trustworthy. If its inputs are incomplete, duplicated or inconsistent, the usefulness of its outputs can be affected too.

A sustainable programme should consider the entire data lifecycle.

1. Measure

Understand the current condition of the data.

Identify duplicates, missing information, inconsistent fields and other issues rather than relying on assumptions about CRM quality.

2. Clean

Correct existing problems through cleansing, standardisation and validation.

3. Consolidate

Identify records that represent the same customer and combine fragmented information where appropriate.

4. Prevent

Put controls in place to reduce the creation of new data quality problems.

5. Govern

Define ownership, standards and responsibilities for customer information.

6. Monitor

Data quality changes continuously. Organisations therefore need to monitor it rather than assume a clean-up project has solved the problem permanently.

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DQ Global provides technology and expertise focused on customer data quality within Microsoft Dynamics 365.

DQ for Dynamics helps organisations prevent, cleanse, consolidate and manage customer information within their Dynamics environment.

DQ Studio supports broader data migration, integration, transformation and cross-system data quality requirements.

Together, these capabilities allow organisations to address both existing CRM data problems and the processes through which new information enters the business.

The desired outcome is not simply a cleaner CRM.

It is trusted customer data that supports better decisions, stronger processes and more reliable AI.

DQ Studio can also support Master Data Management strategies.

Where several systems contain information about the same customer, organisation or entity, businesses need a reliable method of determining which records relate to one another and how information should be brought together.

DQ Studio's data quality, matching, transformation and integration capabilities can form part of an architecture for creating and maintaining trusted master customer information.

A technically successful migration can still be a business failure if unreliable information arrives in the destination CRM.

Poor migration quality can result in users entering a new system and finding the same duplicate, incomplete and inconsistent information they had before.

For this reason, data quality should be treated as part of migration and integration design, rather than as a clean-up activity after the project is complete.