Moving data from one system to another does not improve its quality automatically.

If the source environment contains duplicate, incomplete, inconsistent or inaccurate customer information, simply transferring those records can recreate the same problems in the destination system.

Successful CRM migration and integration therefore need to consider both data movement and data quality.

The objective should not simply be to move information successfully. It should be to ensure that the information arriving in the destination system is accurate, consistent and fit for its intended use.

Data cleansing is the process of identifying and correcting data that is inaccurate, incomplete, inconsistent, duplicated, poorly formatted or otherwise unsuitable for its intended use.

Customer data cleansing might include:

• Standardising company names
• Standardising address information
• Correcting formatting
• Validating contact information
• Removing or consolidating duplicates
• Identifying incomplete records
• Transforming legacy values
• Correcting inconsistent field values
• Enriching missing information

Cleaning customer information helps create a more reliable foundation for CRM, reporting, analytics, automation and AI.

CRM data migration is the process of moving information from one CRM or source system into another.

This could involve:

• Moving from a legacy CRM to Microsoft Dynamics 365
• Migrating from Salesforce to Dynamics 365
• Consolidating several CRM environments
• Moving information following an acquisition
• Replacing legacy business applications
• Combining databases during a digital transformation programme

Migration requires more than copying records between databases.

Fields need to be mapped, values transformed, relationships maintained and quality problems addressed.

A new CRM provides an opportunity to improve the customer data foundation.

Without cleansing, organisations risk bringing years of historic problems into their new environment.

This can result in a technically successful migration that employees immediately distrust because they encounter duplicate customers, incomplete information and inconsistent records.

Pre-migration cleansing can therefore reduce post-migration corrections and help users begin working with more reliable information from launch.

Data integration connects information held in separate applications or databases so it can move between systems or support a shared business process.

For example, Microsoft Dynamics 365 might receive information from:

• ERP platforms
• Marketing systems
• Website forms
• Finance systems
• Customer portals
• Legacy databases
• Other CRM platforms

These applications may represent information differently.

Without appropriate transformation and quality controls, an integration can continuously introduce new inconsistencies into the CRM.

Think of them as three parts of the same challenge:

Cleansing improves the information.

Migration moves information to a new environment.

Integration keeps information moving between connected environments.

If these activities are designed separately, poor-quality information can move successfully while still being unsuitable for business use.

A data-quality-led approach asks:

What condition is the data currently in?

What condition does it need to be in?

How should it be transformed?

How will we identify the same customer across different systems?

How will we prevent the integration from introducing new quality problems?

Who owns the data after the project is complete?

DQ Studio is DQ Global's platform for data migration, integration, transformation and cleansing.

It supports more than 130 connectors, including Microsoft Dynamics 365 and Salesforce, allowing organisations to connect to a wide range of source and destination systems.

DQ Studio can be used to apply data quality and transformation processes while information is being moved or integrated.

This allows migration and integration projects to address the condition of the data as well as its technical movement.

A successful migration should leave the destination system with data that employees can confidently use.

That means looking beyond whether every row successfully moved from system A to system B.

Organisations should consider:

• Is the information accurate?
• Are unnecessary duplicates being migrated?
• Are important fields complete?
• Have values been standardised?
• Are relationships being retained correctly?
• Can records from multiple sources be matched?
• Can users trust the resulting customer view?
• Is the data suitable for reporting, automation and AI?

The best migration is not simply one in which all the data moved.

It is one in which the right data arrived in the right condition and can continue to be trusted afterwards.