Data migration and cleansing

Every implementation or modernization has to bring existing data with it: customers, products, suppliers, open orders, ledgers, student records. Migration is the mapping and transfer from the old schema to the new. Cleansing is the work done before and during that transfer: removing duplicates, fixing inconsistent formats, filling mandatory fields, reconciling codes and archiving records that should not move at all.

The process usually runs as repeated trial loads. Extract, transform, load into a test environment, reconcile counts and totals against the source, review exceptions with business owners, fix rules, repeat. The final load happens at cutover, with a reconciliation report signed off before users are let in. AI helps with fuzzy matching of names and addresses and with classifying free-text fields, but the acceptance rules stay with the business.

It is not a technical afterthought, and it is not something the new platform vendor can do alone. Deciding which of three duplicate customer records is correct is a business decision. Migrations fail on ownership, not on tooling.

• Operations head: dirty data in the new system means users stop trusting it in the first week, which kills adoption.
• CFO: opening balances and open invoices must reconcile exactly, or the new ledger is wrong from day one.
• Data lead: this is the one chance to fix master data at scale; afterwards the mess is inherited by every report and AI feature.
• Compliance officer: migration is a good moment to apply retention rules and drop personal data that should no longer be held under DPDP or GDPR.

New systems inherit old data, and old data is usually worse than anyone admits: duplicate customers, products with three codes, addresses in free text, orders left open for years. Loading it as-is moves the problems into a system with stricter validation, where they block transactions and erode trust. Cleansing before migration exists so the new platform starts from a defensible baseline. The trade-off is time and business attention: rules must be written, exceptions reviewed and results signed off by people who have day jobs. Skipping that work is the most common cause of a rough go-live.

• Deduplicating a distributor's customer master before loading it into a new ERP, with GSTINs used as the matching key.
• Migrating twenty years of a university's student records with programme and fee-category codes remapped to the new structure.
• Reconciling a lender's loan book, including closed and written-off accounts, into a modernized core with exact balance matching.
• Cleaning a retailer's product catalogue with AI-assisted attribute extraction before a new commerce platform goes live.
• Consolidating patient records from three hospital systems into one HIS with ABDM-aligned identifiers.

Technique / practiceA practice that combines engineering and business ownership. Eazyware runs it as a defined workstream in Enterprise Platform Implementation and ERP work: trial loads, reconciliation reports and business sign-off before every phase's cutover.

Eazyware service that covers it: Enterprise Platform Implementation. Starting prices are on the pricing page.