How do you avoid repeating past mistakes?
If your current ERP system has given you data headaches, how do you prevent the same issues from resurfacing when you move to a new one? When replacing an ERP system, it is natural to assume that relevant data should be converted and transferred. In most cases, however, this is not a simple like‑for‑like conversion but a transformation that can be extensive and complex. The scale depends on the age, design, architecture and functionality of your existing system.
The key question is this: if your current ERP system has contributed to data problems, how do you stop those structures, limitations and inconsistencies from following you into the new one? And how do you ensure that you fully leverage the capabilities of your new platform?
Your new ERP system and the data migration process
Before data migration begins, a number of questions typically arise. Data migration is the process of transferring data between formats, applications and systems. The first step is to understand which data you have, where it is located and how much needs to be transferred. From there, the process moves on to data profiling, cleansing, validation, enrichment and, critically, quality assurance testing in the new system.
Data migration is not a linear exercise. It is iterative: you gradually develop a deeper understanding of your legacy data, your new system and your transformed data.
The typical activities include
1. Create an overview of your data
Databases, tables, entities, data types (e.g. enums) and other structures.
2. Select relevant data for migration
Identify the tables, fields and records that should be transferred.
3. Select the relevant records from key tables, for example:
- Master data: customers, suppliers, products, Intrastat codes, BOMs, routes, resources, warehouses, locations, projects, chart of accounts, drawings, documents.
- Operational data: open purchase, production and sales orders; open financial entries; historical records; opening balances.
- Setup data: product hierarchies, product classes, project settings, payment methods and terms, customer groups, VAT settings, financial dimensions and more.
4. Profile your data
Understand formats, value types (numeric or alphanumeric), field lengths, number of decimals, languages, abbreviations, special characters, references to other tables and whether those references are still relevant.
5. Cleanse your data
Remove irrelevant data, adjust formats, ensure consistency and eliminate ambiguity.
6. Validate your data
Identify empty values, check that all values make business sense and verify that logical fields (e.g. 0/1) are used correctly.
7. Enrich your data
New ERP systems typically support more functionality, more transactions, more processes, more compliance requirements and stronger data governance. This means your new system may require additional or different data than your old one ever captured.
8. Migrate the data
If the migration requires more than simple conversion, it is worth establishing a structured process for data enrichment and transformation. SQL often plays a central role, as queries can be rerun repeatedly as you refine rules and gain insight into business needs and new system capabilities.
Many organisations use this opportunity to rethink how they work with projects, sales or products. These changes introduce new rules, new attributes and new structures — all of which require data that may not exist in the current system.
9. Quality‑assure the data
Once the first migration is complete and data has been loaded into the new ERP system, testing begins.
- Start with visual validation: does the data look right?
- Then test whether data behaves as expected through structured test cases.
- Test both newly created data and migrated data to uncover potential configuration issues in the new system.
Set yourself up for success
Want to know more?
Ready to take the next step?
Are you preparing for an ERP replacement and looking for guidance on a secure and structured data migration? kaastrup|andersen can help with data extraction, data analysis, pre‑studies, business analysis and the practical initiation of your digitalisation journey.
