Stage 1
Profile the source
Read what the data actually is, not what the documentation claims. Volumes, duplicates, orphaned records, broken references, and the fields nobody has populated since 2014.
Solutions · Data Engineering
Data Migration
Enterprise data migration for SAP, Dynamics 365, Oracle and legacy ERP: profiling, mapping, transformation, validation and reconciliation, delivered with BlueGecko.
Data Migration
Moving the data is easy. Proving it arrived intact is the work.
Data migration is the process of moving data from one system to another — typically out of a legacy ERP and into a new one — while cleansing it, reshaping it to fit the target's data model, and proving that nothing was lost or altered along the way. We deliver it end to end across SAP, Microsoft Dynamics 365, Oracle, and legacy platforms, including cross-ERP moves between them.
Migrations rarely fail because the data could not be moved. They fail because the data turned out to be in worse condition than the plan assumed, and that was discovered during the load rather than months before it. Our sequence exists to surface that early, while it is still cheap to fix.
How a migration runs
Five stages, in this order, every time
The order is the method. Every stage that gets skipped or reordered reappears later as a defect found at a more expensive moment.
- Stage 2
Map source to target
Every source field to its target counterpart, with the transformation rules written down and owned by a named person in the business. Falcon Mapping does this roughly 65% faster than a manual pass.
- Stage 3
Cleanse and transform
Apply the rules, deduplicate, enrich, and generate the load-ready extract. Code Cheetah generates the SQL, PySpark or SSIS so the transformation logic is consistent and reviewable.
- Stage 4
Validate before loading
Quality gates run against the transformed data while it is still cheap to fix. Owl Sight monitors completeness, conformity and anomalies continuously rather than at a single checkpoint.
- Stage 5
Load and reconcile
Execute through Orca Migrate, then reconcile the loaded data against the source, record count by record count and balance by balance, so sign-off rests on evidence rather than confidence.
Why migrations slip
In our experience the same five causes account for most delayed go-lives, and none of them is about technology.
- Data quality is discovered during the load, not before it — the single most common cause of a delayed go-live.
- Mapping decisions live in one consultant's head, so nobody can review, repeat, or audit them.
- Business owners are asked to sign off on records they have no way to inspect.
- History is migrated wholesale because nobody decided what actually needs to come across.
- Reconciliation is a spreadsheet exercise done once, rather than a controlled, repeatable process.
Where to start
Profile before you plan.
Two weeks of profiling turns a migration estimate from a guess into a number you can defend. It is also the cheapest point at which to decide how much history is actually worth bringing across.
Migration paths we deliver
Data migration: common questions
What is data migration?
Data migration is the process of moving data from one system to another — typically from a legacy ERP into a new one — while cleansing it, reshaping it to fit the target's data model, and proving that nothing was lost or altered in transit. It has five stages: profiling, mapping, transformation, validation, and reconciliation. The moving is the easy part; the mapping and validation are where the effort actually goes.
How long does an enterprise data migration take?
Most enterprise ERP migrations run three to nine months, and the driver is data quality rather than data volume. A single-entity migration with clean master data can complete in under three months; a multi-entity, multi-country landscape with thirty years of accumulated history and no clear ownership takes considerably longer. A profiling exercise in the first two weeks is what turns that range into a real estimate.
What are the main risks in a data migration?
The dominant risk is discovering data quality problems too late to fix them cheaply. Others follow from it: undocumented mapping decisions that cannot be audited, business owners signing off on data they cannot inspect, and reconciliation treated as a one-off check rather than a repeatable control. Every one of these is addressed by moving validation earlier, which is why profiling comes first.
Should we migrate all our historical data?
Usually not. Migrating everything is the default when nobody makes a decision, and it inflates cost, timeline, and risk while carrying old data quality problems into a clean system. The better approach is to migrate open items and the master data in full, take a defined number of years of closed transactional history, and leave the rest in an archive that remains queryable.
What is the difference between data migration and data integration?
Data migration is a one-time move: data leaves the old system and lives in the new one, and the old system is retired. Data integration is ongoing: two systems both remain live and exchange data continuously. Programmes often need both — a migration to move the history across, and integrations to keep the surrounding systems in step afterwards.
How do you validate that a migration was successful?
By reconciling the loaded data against the source on measures the business already trusts: record counts per object, financial balances per account and period, and open item totals per customer and supplier. Sign-off should rest on that reconciliation being reproducible on demand, not on a one-off spreadsheet check performed the night before go-live.