Let me address both questions comprehensively since they’re interconnected with our overall implementation strategy.
Error Reporting and Remediation Workflow:
We built a three-tier error reporting system. Pre-validation scripts generated Excel reports categorizing errors by severity: Critical (blocking import), High (data quality issues), and Low (warnings). Each error included the supplier ID, field name, current value, expected format, and correction guidance. Our workflow had data stewards review reports, correct source data, then re-run validation before import. We tracked all corrections in a remediation log - this audit trail was invaluable for understanding data quality patterns. Post-import, we had reconciliation reports comparing record counts and key field values between legacy and IBP systems.
Bulk Data Import Implementation:
For bulk import, we used Data Integration with custom XML templates that mapped all legacy supplier attributes to IBP master data structures. Our validation scripts checked data completeness and format compliance before any import execution:
<validation>
<rule field="supplierID" type="unique" />
<rule field="paymentTerms" type="referential" />
<rule field="currency" type="masterData" />
</validation>
Batch sizing was critical for performance. We settled on 50 suppliers per batch after testing showed this balanced throughput with error isolation. Smaller batches (25) were too slow; larger batches (100) made error diagnosis difficult. Each batch took 8-12 minutes including validation and import. We scheduled jobs during off-peak hours to minimize system impact.
Performance Optimization:
We encountered initial performance bottlenecks around database commits and index updates. Working with our basis team, we temporarily disabled non-critical indexes during bulk loads, then rebuilt them afterward. This cut import time by 40%. We also implemented parallel processing for independent supplier groups (different regions) which further improved throughput. Memory allocation was increased for Data Integration jobs to handle larger XML payloads.
Key Success Factors:
The 75% efficiency gain came from three integrated elements working together. First, bulk data import eliminated repetitive manual entry - our Data Integration jobs processed 50 suppliers in the time it took to manually enter 2-3. Second, pre-validation scripts caught 89% of errors upfront, preventing the expensive cycle of import-fail-diagnose-correct-retry. Third, comprehensive error reporting gave data stewards clear, actionable correction guidance rather than cryptic system errors.
Lessons Learned:
Start with data profiling - we spent a week analyzing legacy data quality before designing validations. This upfront investment paid off enormously. Build validation rules incrementally, testing with small datasets first. Document everything, especially field mappings and transformation logic. Finally, plan for data steward training - they needed to understand both legacy and IBP data models to make informed corrections.
For your 200-supplier project, I’d recommend starting with a 20-supplier pilot to refine your validation rules and mappings before full-scale migration.