Dirty source data is the single biggest cause of migration slippage. We cleanse, standardize, and de-duplicate SAP master and transactional data before it ever reaches a load cycle, so your migration timeline isn't spent firefighting bad records.
Data cleaning is the remediation of source data quality issues (duplicates, inconsistent formats, missing mandatory fields, and orphaned records) before extraction and load begin.
Identifying and merging duplicate master records (vendors, customers, materials) that would otherwise load as separate objects.
Correcting inconsistent date formats, unit-of-measure entries, and field-length mismatches across source systems.
Flagging and resolving missing values in fields the target SAP environment requires to accept the load.
Removing records with no valid downstream reference, so migrated data doesn't carry legacy clutter forward.
Dirty data is the #1 cause of migration timeline slippage, not scope, not tooling, not resourcing. Catching data quality issues at the cleansing stage is far cheaper than catching them during a failed mock load, or worse, during cutover weekend.
We run source data through profiling checks to surface duplicates, format inconsistencies, and completeness gaps before touching a single record.
Cleansing rules (merge logic, formatting standards, exclusion criteria) are documented and approved with your functional team before execution.
Rules are applied against the full data set, with an audit trail of every correction for traceability.
Cleansed data is re-profiled against agreed quality thresholds before it's handed off to mapping and load.
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