Your team approves, not types
IDN profiles the source data and its AI proposes field mappings, value crosswalks and transformations. Your team reviews and approves each one, every approval is recorded, and the extraction and conversion jobs are generated from the approved mappings instead of being hand-coded.
| The project way | Field-by-field mapping in spreadsheets and hand-written scripts. |
| The IDN way | AI proposes mappings and transformations, jobs are generated, your team approves. |
The problem
Mapping thousands of fields by hand is where weeks disappear. It depends on a few experts, lives in spreadsheets that drift between teams, and every change means rewriting scripts.
How IDN does it
- Cognitive mapping: AI proposes field mappings with a confidence level, using metadata and profiled values.
- Value mapping: crosswalks translate codes between systems, such as units of measure and material types.
- Approvals: your team approves each mapping; versions and approvals are recorded.
- Generated jobs: extraction and conversion jobs are generated from the approved mappings.
What it means for the program
Faster mapping, fewer hand-offs between experts, and an audit trail of who approved what.
Questions
How does AI field mapping work for SAP migration?
IDN compares source and target fields by name, description, data type and values, and proposes a match with a confidence level. High-confidence matches are suggested for approval; low-confidence ones are flagged for a person to decide.
Is AI data mapping accurate enough for SAP?
AI makes the first proposal; it does not decide. Every mapping is reviewed and approved by your team, and nothing goes live on its own. The benefit is speed: people spend their time checking proposals rather than typing mappings.
Can migration jobs be generated instead of hand-coded?
Yes. Once mappings are approved, IDN generates the extraction and conversion jobs, so changes to a mapping flow into the jobs without rewriting scripts.
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