Data quality services

Make your data consistent and suitable for the systems your team relies on.

Improve the quality
of your CRM data

Inconsistent records can make routine work difficult. Teams spend time checking information manually, reports group records incorrectly and missing fields cause problems when data moves between systems.

Our data quality services address errors and inconsistent formats. For migration projects, we also help define the rules your records must meet before entering the new system.

What our data quality services cover

The steps we take to standardise, structure and strengthen your data.

Error correction

We fix inaccurate or incomplete records to improve overall data integrity. That means fewer errors – and more confidence in every field.

Standardisation

We bring structure and consistency to your data using recognised formats and rules – making it easier to process, report on and use across systems.

Seamless delivery

You’ll receive structured, cleaned data in a format that fits your system – tailored to your workflows and ready to use straight away.

Support every step of the way

From setup to final checks, we’re on hand to help. Clear communication, quick answers and a flexible, friendly approach – every step of the way.

Ongoing quality control

Need your data to stay in great shape? We offer regular updates and quality monitoring to help you stay compliant and campaign-ready.

Data quality rules for CRM migration

Data Quality Rules, or DQRs, define the conditions a record must meet before it enters your new system. They turn decisions about what your organisation needs into checks that can be applied consistently across your data.

For example, an active customer record might need an email address or telephone number. A contract might need a valid start date, with an end date that does not fall before it.

The rules depend on how your organisation works and what the destination system requires. Agreeing them early helps identify records that need attention before they cause problems with reporting or automated processes.

Examples of data quality rules

Check	Example rule	What it identifies
Completeness	An active customer must have an email address or telephone number.	Records missing the contact information required for that use.
Format	Dates must follow the format accepted by the destination system.	Values that need conversion or correction before import.
Uniqueness	A supplier tax identifier must be unique where the business requires one record per identifier.	Potential duplicate supplier records.
Logical consistency	A contract end date must not precede its start date.	Conflicting dates that need investigation.

These are examples. The checks used for your migration should reflect your own records, processes and system requirements.

Read our guide to data quality rules.

Data quality solutions
shaped around how you work

A field can be correctly formatted and still contain information that is unsuitable for your organisation. An incomplete record may also be usable for one purpose but unsuitable for another.

We work with your team to understand those differences. Your operational requirements inform the rules, while the destination system determines how the data needs to be structured. This gives everyone an agreed basis for deciding what needs correcting and what is ready to move.

Don't just take our word for it...

"❝It was great to work with the team at Migrate Data on our move from Raisers’ Edge to Salesforce - we simply couldn't have done it without them. Mikaela bought a high level of expertise and in-depth understanding to our data, helping us extract the key information from Raisers’ Edge and map it onto the new structures in Salesforce. No detail was too small, or problem too complicated! Alongside the technical expertise, we had clear communication throughout, and a helpful and flexible attitude in meeting our moving deadlines. The migration has been clean and comprehensive, and it has been a big relief to start on Salesforce knowing that our data is in excellent state.❞"

"In our experience, Migrate Data (we worked with Chris and George), were knowledgeable about both Raisers Edge and Salesforce. They were calm, responsive, spoke in our (non-techy) language, kept to budget and timelines. They were easy and friendly to work with, we had regular check in meetings which were very reassuring. Nothing was insurmountable for them and so from our experience we would recommend Migrate Data to support your CRM move from Raisers Edge to Salesforce and would be prepared to chat this through with any potential clients."

"Im writing to convey our thanks to you and your colleagues for the excellent support BWC received from Migrate during our recent Salesforce implementation project. Feedback from our project manager Nadine throughout the project was that the Migrate team provided an unfailingly expert and responsive service, that on occasion went above and beyond reasonable expectations. The success of our programme is due in no small part to your contributions. Thank you. I would be grateful if you would convey our thanks to your colleagues on my behalf."

Discuss your data quality requirements

Tell us which system you use, where your data is causing problems and whether you’re planning a migration. We’ll discuss what needs attention and how we can help.

Frequently asked questions.

What is data quality?

Data quality describes how suitable information is for its intended use. It includes whether records are accurate, sufficiently complete and consistent with one another. The required standard depends on what your organisation needs to do with the data.

What is the difference between data quality and data cleansing?

Data cleansing addresses problems in existing records, such as outdated contact details or duplication.

Data quality also considers the standards information must meet, how it is structured and how those standards are maintained over time.

What is data standardisation?

Data standardisation puts information into a consistent format. For example, different spellings or abbreviations for the same category can be mapped to one agreed value, helping reports group records correctly.

Who decides the data quality rules for a migration?

Business teams define the information they need, system administrators explain the destination platform’s requirements, and migration engineers translate those decisions into checks. The rules should be agreed together so they reflect both operational and technical needs.

What happens when a record fails a data quality rule?

It should not enter the new system unchanged if it fails an agreed entry requirement. Depending on the problem, the record may need correcting, investigating or holding back. Any decision to exclude information should follow the agreed migration scope.

Can data quality be improved without changing CRM?

Yes. Standardising fields and correcting inconsistent records can improve data within an existing system. Regular checks can also help identify recurring problems and show where data entry processes need attention.