We’re integrating external forecast data from our third-party demand planning system into Infor CloudSuite supply planning module. The source CSV files contain additional columns beyond what our schema expects, and some fields have inconsistent data types (strings where we expect decimals).
When attempting to map this data through Infor Data Lake, the process fails with schema mismatch errors. The error indicates missing required fields, but those fields exist in the source-just under slightly different names or formats.
Has anyone dealt with similar CSV import challenges where the source schema doesn’t perfectly align with CloudSuite’s expected format? We need to maintain the integrity of the forecast data while accommodating these structural differences.
I’ve encountered this exact scenario. The issue is that CloudSuite’s supply planning expects strict schema adherence. You’ll need to implement a transformation layer before the data reaches the mapping stage. Consider using ION’s data transformation capabilities to normalize your CSV structure. Create a pre-processing workflow that strips extra columns, renames fields to match expected schema, and converts data types. This approach has worked reliably for us with multiple external forecast sources.
I’d add that you should implement robust error handling for type conversion failures. When a string can’t be converted to decimal, decide whether to skip the record, use a default value, or flag for manual review. We log all transformation exceptions to a separate error table with the original CSV row data. This has been invaluable for troubleshooting data quality issues from upstream systems and maintaining forecast accuracy.
The transformation overhead is minimal if configured properly. Use ION’s mapper to create explicit field mappings with type conversion rules. For inconsistent types like strings-to-decimals, add validation logic that handles common formatting issues (thousand separators, currency symbols). Document your mapping rules in the transformation template so future CSV changes can be quickly adapted. We process millions of forecast records daily with less than 2% performance impact.
Consider validating your CSV schema before attempting the import. We built a pre-flight check that compares incoming file structure against expected schema and generates a detailed mismatch report. This catches issues early and provides clear feedback to the source system team about what needs correction. The validation script runs in under 30 seconds even for large files.