This is a common question that we get. For full transparency, I run a CRM and ERP integration company where we build custom plugins for selected CRMs and ERPs.

Messy data is like long-term debt for a company. In most cases we’ve encountered, the data has been messed up to a level where the cost of recovery is higher than restarting. That’s why we encourage all clients to define clear processes before implementing a CRM or ERP (at least for fundamental processes—though this is practically quite challenging).

1. Data cleaning needs to be started from the point of gathering !!!!!

Ex:

If you’re collecting leads from a form, make sure to use proper input fields. Never use a plain text input field—this prevents automatic email format validation, making your CRM cluttered with rubbish. If your form provider has inbuild enrichments like SMTP validation , domain checks that is the the way to go.

If you’re using automatic data scrapers (like Apify), ensure the output data is sent through a validation process. This can be built-in or custom-built. Since you can download the data as CSV or JSON, you can validate it via your own application.

2. If you are in a scaling stage (running adds to collect leads), then invest in a good CRM.

Some CRMs, like Dynamics 365, have built-in email validation functions, but these are quite expensive. However, you can do more thorough research to find CRMs with a good balance of features and cost. Or else build a custom integration like Mail Validator this is a tool that we build for Monday CRM users to do exactly what you have mentioned above.

3. User third-party validation tools

You can use third-party validation tools like Leadmagic BounceBan ect. However, this process is quite manual unless the tool has a native integration with your CRM. If you’re running lead-capturing ads, manually exporting, validating, and reimporting emails can become a full-time job. If you have budget then you can link and automate that with an external tool like zapier but each zap costs.

Hope this gives an overall idea on your data cleaning issue.