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.
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.