A CRM migration that leaves existing errors behind
Source: CRM Data Quality project delivered in 2021–2022.
Customer data from eight brands was fragmented and duplicated as the group prepared to migrate to a new CRM that would inherit the errors. Maxxi built a data quality process combining automated cleansing with human validation, brand by brand. It delivered a unified, deduplicated dataset with a decision trail and more than 131,000 customer records processed.
Customer data was about to migrate with its errors embedded
Customer datasets from eight brands were distributed across unstructured environments, with duplicate and unreliable records, and had to move into a new CRM.
Without addressing quality first, duplicates and inconsistencies would enter the new system, where errors cost more to correct. For a luxury group, poor customer data leads to poor customer relationships.
From quality rules to migration-ready data
The work covered the definition of rules through to data ready for migration, connecting two stages.
Maxxi diagnosed the fragmented datasets and, with product owners from all eight brands, defined the quality rules required for the migration.
Data & AI GovernanceMaxxi developed a cleansing engine and a human-in-the-loop platform: automation applies the rule, a person decides ambiguous cases, and the system unifies records by Brazilian taxpayer ID, compares before and after, and maintains an auditable trail for each brand.
Data PlatformsClean, unified and auditable data entered the new CRM
How should data quality be addressed before a CRM migration?
Fragmented and duplicated datasets without a decision trail.
Records unified by Brazilian taxpayer ID, deduplicated and supported by brand-level human decisions.
Business impact
More than 131,000 customer records across the group's eight brands were processed and unified, achieving 92% deduplication and validation by product owners before migration.
Technical impact
Thirty-two cleansing rules were automated with a human-in-the-loop model. Thirty-two input files became 16 remediated datasets and 16 audit reports, with a decision trail for each brand.
Migrating without remediation means migrating the problem
Replacing a CRM or consolidating datasets without first addressing data quality carries duplicates and errors into the new system, where they cost more to correct. Automated rules, with human judgment where it matters, establish a reliable dataset from day one.
The work also drew on Data & AI Governance. Luxury has no industry page yet.
Published 2026.
Will your next migration remediate the data, or carry the errors forward?
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