AI analysis is only as reliable as the data underneath it. In organizations that operate across regions, systems, and languages, that data is almost never clean.
This project addresses the problem that comes before operational analytics: making fragmented, inconsistent operational data ready for AI to use.
The Problem
Cross-regional organizations typically manage data across multiple systems that were never designed to talk to each other:
- ERP exports in different formats across business units
- Vendor updates arriving as emails, spreadsheets, and portal reports
- Operational records in different currencies, date formats, and languages
- Inventory and shipment data labeled inconsistently across US and Japan teams
- Manual reconciliation as the primary method of aligning records
The result is that analytical tools — whether AI-powered or not — either refuse to run on the raw data or produce unreliable output because the inputs are structurally incompatible.
What It Does
It takes in messy, multi-source operational data and produces a single unified dataset that downstream AI tools can reliably use.
Core capabilities:
- Field mapping — identifies equivalent fields across incompatible source formats
- Currency and unit normalization — standardizes USD/JPY and other unit mismatches
- Date format reconciliation — aligns US and Japanese date conventions
- Deduplication — identifies and resolves duplicate records across sources
- Language normalization — handles bilingual field values in operational records
- Audit trail — records the source and transformation history of every field
The output is a clean, structured dataset ready to feed into operational analytics, forecasting models, or AI advisory tools like Komon.
Relationship to the Portfolio
I am building this as the data foundation layer for the operational analytics interface. Together, these projects represent the practical infrastructure that makes AI advisory tools trustworthy:
Harmonize the data → Query the operation → Advise on strategy
A tool that answers “what is our inventory exposure?” is only as credible as the inventory records it reads. This project ensures those records are worth reading.
Status
In active development. I am building this as part of the same AI operations portfolio as Komon and Noroshi. A working prototype and live demo will be available shortly.
I will publish this project under a Japanese name reflecting its function in organizing and preparing foundational operational data.