01 / Context
Operational data has to be correct before the business can trust it.
In my Data Administrator role at Trophy Foods, I maintain and validate product data in JD Edwards ERP and work with product specifications, labeling information, and approvals across several teams. My résumé documents more than 300 Product Specification Sheets reviewed and updated as part of this work.
02 / Problem
Manual review creates friction when the same checks happen again and again.
Product data work involves information coming from multiple sources and stakeholders. When checks are repetitive, important differences can be harder to notice consistently and reviewers spend time on tasks that software can help with.
The opportunity was not to replace judgment. It was to make the routine parts of the review more structured so attention could go to the exceptions that actually needed investigation.
03 / Approach
Understand the workflow before automating it.
I treated the work as a systems problem rather than starting with a macro. First, I identified the information being reviewed, the recurring decisions, the statuses people needed to track, and the points where discrepancies or missing information created rework.
Design principles
- Automate deterministic checks; leave ambiguous decisions visible for human review.
- Use clear statuses so the next action is obvious.
- Surface discrepancies instead of burying them inside large tables.
- Keep the tools practical for the people already using Excel in their workflow.
04 / Solution
Lightweight tools around the existing business system.
I developed Excel trackers, validation logic, and automations that supported the review process around ERP and product data. The tools standardized recurring checks, highlighted information that needed attention, and made the status of the work easier to follow.
I also collaborated with Product Development, Quality Assurance, Production Planning, Regulatory, and Packaging teams to resolve specification, labeling, and product data issues. That meant the solution had to fit a process involving several teams, not just my own spreadsheet.
05 / Result
A more consistent review process with less repetitive work.
The outcome described in my résumé is a workflow that reduced manual effort, standardized reviews, and surfaced data discrepancies more efficiently. I also coordinated and reviewed the work of two interns, which made clarity and repeatability in the process even more important.
06 / What it demonstrates
Practical automation requires both technical and operational thinking.
A technically clever solution is not useful if it ignores how people actually complete the work.
Automation is especially valuable when it moves routine checks out of the way and makes unusual cases easier to see.
Correct data often depends on communication between teams, not only validation rules.
Using familiar tools well can deliver more value than introducing complexity for its own sake.