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Data quality · 6 min read
Factory-to-PIM data quality
How to keep product, warranty and spare-parts records consistent when every factory sends data differently.
Onix Automation teamfield notes · 3 sections
Factory formats are operational reality
Global standardization is the goal, but factories often have local systems and practical constraints. A good pipeline accepts that reality while enforcing a common downstream model.
Validation should happen before import
Once incomplete data lands in PIM or service systems, it becomes harder to trace. Validation, enrichment and exception handling should happen before the record becomes trusted.
Data lineage builds confidence
Teams need to see where a record came from, what rules touched it and why an exception was raised. That visibility turns automation into an operational control layer.
Living with this problem in your own warranty or factory data flow?
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More operational thinking.
Strategy · 5 min read
Warranty as brand promise
Why premium manufacturers should treat warranty data as a customer experience asset, not a back-office artifact.
Read insightData quality · 6 min readThe 60 percent problem
A premium brand assumed its warranty numbers were complete. The systems said otherwise. What partial warranty coverage really costs, and how to close the gap.
Read insightModernization · 7 min readRescue vs rebuild: automation workflows
A practical decision framework for teams stuck between spreadsheet workarounds and a large platform replacement.
Read insightProject triage
Bring the messy process. We will map the path to automation.
Share the workflow, systems and data sources. We turn it into a clear next step: rescue, integration, admin panel, or a fuller automation build.
Reply within 1 business dayNDA-friendly reviewFixed scope before code
Typical first audit
free · senior-led- System and file-flow map with named owners
- Automation opportunity score for each manual step
- Delivery plan, risk list and a fixed-scope next step