Data migration & cleaning services
Data Migration & Cleaning
Automated data cleansing, deduplication, and field mapping before your data ever touches the new system, so migration doesn’t just relocate years of accumulated errors.
Dirty data doesn’t get cleaner by moving it to a new system.
Legacy data cleansing services exist because every database accumulates errors over years: duplicate customer records, inconsistent formatting, orphaned references. Migrating that data as-is just gives the new system the same problems with a fresh coat of paint.
- Duplicate customer or vendor records accumulated over years
- Inconsistent formatting across date, currency, or address fields
- Orphaned records referencing data that no longer exists
- No documented mapping between legacy fields and their meaning
What a data migration & cleaning engagement covers
Data is validated and cleaned before it ever reaches the destination system.
Data audit & profiling
Full assessment of data quality issues: duplicates, inconsistencies, and orphaned records.
Automated cleansing & deduplication
Fuzzy matching and business rules applied to merge duplicates and standardize formats.
Field mapping & transformation
Documented mapping from legacy fields to the destination schema, validated on sample data.
Integrity verification
Record counts, checksums, and key totals compared before and after migration.
Talk to a senior migration engineer before you commit to a build.
Free 15 minute discovery call. No resellers, no sales script.
How we clean data without losing anything that matters
Data profiling
We assess data quality issues across the full dataset before deciding on cleansing rules.
Cleansing & deduplication
Automated rules merge duplicates and standardize formats, with edge cases flagged for review.
Validated field mapping
Every field mapped and tested against a sample set before the full migration runs.
Post-migration verification
Integrity checks confirm the destination system matches the source, record for record.
Why data migration vs data integration is the wrong framing for most projects
Automated data scrubbing and legacy database cleaning are often treated as a technical afterthought to the “real” migration work, when in practice they usually consume more effort than the migration itself. Data mapping and transformation done properly requires understanding what each legacy field actually meant to the business, not just its data type, before deciding how it maps to the new schema.
The distinction between data migration and data integration matters here too: migration is a one-time transfer that needs to be right the first time, while integration is ongoing sync that can be corrected iteratively. That difference is exactly why data cleansing deserves the bulk of the project timeline, not a rushed pass in the final week before cutover.
Trusted by founders and operations leaders
They found duplicate records going back over a decade that we didn’t know existed.
Our new system finally has clean, consistent customer data across every field.
The integrity verification gave us confidence to cut over without a single surprise.
They found duplicate records going back over a decade that we didn’t know existed.
Our new system finally has clean, consistent customer data across every field.
The integrity verification gave us confidence to cut over without a single surprise.
Questions US founders ask about this service
Migrating dirty data, duplicates, inconsistent formats, orphaned records, just moves the same problems into the new system. Cleaning before migration ensures the new system starts with accurate, usable data instead of inheriting years of accumulated errors.
Through fuzzy matching algorithms that identify likely duplicate records based on similar names, addresses, or identifiers, flagging them for merge or removal according to defined business rules.
Documenting every source field’s meaning and format before mapping it to the destination schema, handling data type mismatches explicitly, and validating a sample set of mapped records manually before running the full migration.
By comparing record counts, checksums, and key financial or operational totals between the source and destination systems, and spot-checking a statistically meaningful sample of individual records.
By catching malformed records, broken references, and invalid data before they reach the new system, where they would otherwise cause application errors or failed transactions after go-live.