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

graphs of performance analytics on a laptop screen
graphs of performance analytics on a laptop screen

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.