Data Normalization & Categorization from ERP Systems
Cleaned and categorized messy ERP data using AI. Delivered structured, insightful reports to support better decisions and future planning.
PROJECT CASE
6/16/20241 min read
Cleaning, Normalizing, and Categorizing ERP Data for Business Insights
Many companies struggle with messy, inconsistent, or incomplete data coming from their ERP systems — especially when dealing with product data, customer records, or transactional logs. In this project, we helped a company transform chaotic ERP data into structured, analyzable information ready for decision-making.
The challenges:
Data from multiple sources with different naming standards
Empty or duplicated fields
Lack of clear categorization or usable analytics
Our solution combined data cleaning, normalization, and AI-assisted categorization:
We built automated pipelines to clean and standardize product names, codes, and attributes.
Used AI models to fill in missing information based on context and learned patterns.
Applied multi-level categorization using a blend of company-defined taxonomy and AI predictions.
Finally, we produced dashboards and Excel outputs with ready-to-use insights.
Results:
The company gained a clean, structured database of its products and operations.
Data became suitable for internal tools, reporting, and pricing analysis.
Time spent manually fixing or decoding ERP exports was reduced by over 80%.
This case shows how even legacy systems can be upgraded with smart pipelines — enabling business teams to actually use their data instead of fighting with it.