Overview

I redesigned the product master data structure in SAP by introducing well-defined User Defined Fields (UDFs). This redesign improved data clarity across departments, enabled faster and cleaner data sharing with customers, and created a more flexible and scalable foundation for future growth.

Context / Problem

Key product attributes—such as dimensions (W × D × H) and weight—were stored together in a single free-text field. This led to several challenges:

• Inconsistent data entry across teams

• Misinterpretation of product specifications

• Limited ability to filter, validate, or automate workflows

• Significant manual effort when sharing data with customers and external platforms

Because the database structure did not align with how the business actually used the data, these issues created bottlenecks that delayed projects by days to weeks.

Objective

Design a scalable database with input validation that ensures accurate data, cross‑department alignment, clean customer exchange, and future flexibility.

Solution: Redesign Product Master Data

To fully understand how product data flowed through the organization, I shadowed the shipping and sales teams to observe how the fields were used across documents and workflows. I noted down pain points, common errors, and desired improvements.

I also analyzed external data requirements, including eCommerce integrations (e.g., Wayfair), to ensure the new structure met customer and platform standards.

Based on these insights, I proposed a redesigned product master data structure and cleaned existing data to align with the newly defined fields.

My Role

• Collaborated with shipping and sales teams to understand real-world data usage

• Designed and implemented new UDFs in SAP product master data with input validation

• Defined field structure, naming conventions, and data types

• Ensured the solution meets both the technical and business needs
  

Outcome

• Eliminated non-value manual steps and improved operational performance

• Increased Wayfair sales by 200%, reducing product onboarding time from weeks or months to just a few days

• Reduced shipping errors and improved the accuracy of shipping quotes

• Established a scalable foundation for future automation and system integrations
  

Solution Approach (Detailed):

1. Data Usage Analysis

I reviewed how teams entered, consumed, and exported product data. Dimensions and weight were used independently in multiple workflows (logistics, reporting, customer communication), despite being stored together in a single unstructured field.

2. Database Design Decisions Examples

I decomposed the combined field into atomic attributes, following database normalization principles.

Before (Unstructured):

**Assembled Dimensions:** WDH: 100x50x40, 12kg
**Assembled Dimensions:** WHD: 100 x 40 x 50 12kg
**Assembled Dimensions:** WDH: 12lbs 100x50x40

After (Structured):

For multi-box products, SKUs were broken down into individual components so shipping and eCommerce teams could accurately capture:

• Box-level dimensions

• Box-level weight
  

Before (Unstructured):

**SKU Code:** GIA001Q
**Item Name:** GIA Queen Bed

**Shipping Dimensions:**
										Box 1 of 4: 69.5 x 37 x 4.5
										Box 2 of 4: 66 x 8.5 x 9.5
										Box 3 of 4: 83 x 8.5 x 8 
										Box 4 of 4: 54 x 10 x 5

After (Structured):

To improve consistency and prevent data entry errors, controlled dropdowns (e.g., packing type) were introduced to eliminate spelling and formatting inconsistencies.

These choices ensured:

• Each field had a single responsibility

• Values were machine-readable

• Data could be validated and reused consistently