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The data entered into the system should be automated using technology and tools like Trifacta can eliminate errors by removing duplicates.
Teams should be trained on hoebto maintain data quality, team members should understand the importance of data quality and skills to maintain it, a culture of continuous learning shod be promoted.
Mastery Data Management can be deployed to ensure that product information is consistent across all regions and departments so their will be uniformity in data.
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Implement data validation rules. For example ensure that data fields are correctly formatted & within expected ranges
Ensure that data is collected from consistent & reliable sources. Make sure the data is in sync
Regularly review and audit your data to identify & correct discrepancies
Where possible automate data collection & reporting processes to minimize human error
Have role based access to sensitive data
Ensure standard policies for data management, including guidelines for data entry, updates & handling discrepancies
Store data backups securely & test them periodically to ensure they can be restored successfully
When discrepancies are identified, investigate their causes & make necessary corrections. Document the issues
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I like to view this challenge through a lens of: make it easy for a user to input data correctly the first time and prevent cleanup before it has to be done.
For example: Think about what kind of data fields you're using and limit what kind data can be to be put into them. One of the big cleanup projects my team worked on which had a big impact was cleaning up Billing addresses.
When our CRM was set up; Billing State was an open text field. So we had many names misspelled. So we converted to a picklist with correctly spelled States (and cleaned up and re-uploaded the existing data). We no longer have misspelled State names.
It's a simple example; but one that once you start thinking along those lines - you find other ways to apply it.