Blog: Revolutionizing Supplier Data Cleanup in 45 minutes with GenAI and Python

Introduction

At Epsilon3 LLC, we understand the pain points procurement professionals face with supplier data management. Duplicate records, inconsistencies, and outdated information can significantly hinder your source-to-pay processes. In just 45 minutes, leveraging Generative AI and Python, we built and tested an advanced data de-duplication tool from scratch, showcasing our expertise and the value we can bring to your organization. Specifically, we tackled the long-standing challenge of identifying hard-to-find duplicates in large datasets.

Tackling the Challenge

One of the most persistent challenges in procurement is managing supplier master data. Duplicate records, often caused by variations in supplier names, addresses, and contact details, can lead to inefficiencies, increased costs, and compliance issues. Traditional methods like manual reviews or simple fuzzy matching algorithms are time-consuming and often insufficient.

At Epsilon3 LLC, we leveraged cutting-edge techniques and the power of Generative AI to develop a comprehensive solution that addresses these issues effectively and efficiently. Here’s a detailed look at how we did it.

Our Approach

Data Preparation and Cleaning: We began by generating synthetic supplier data, ensuring it was realistic and varied enough to test our solution comprehensively. We included:
- Exact Duplicates: Identical records with different formatting.
- Subtle Variations: Examples include “GE” vs. “General Electric”, and “Smith-Jones Inc.” vs. “Smith Jones Inc.”
- Complex Inconsistencies: Variations in addresses, abbreviations, and typographical errors.
Advanced Matching Techniques:
- Exact Matching: We used multiple identifiers, such as tax IDs, phone numbers, and contact emails, to identify exact duplicates.
- Fuzzy Matching: By employing fuzzy string matching algorithms like Levenshtein Distance and the FuzzyWuzzy library, we caught subtle name variations, identifying matches with high similarity scores.
- Phonetic Matching: Soundex and Double Metaphone algorithms helped us match names that sound similar but are spelled differently.
- Clustering: Using TF-IDF vectorization and agglomerative clustering, we grouped similar records, capturing duplicates based on combined features like names and addresses.
Performance Metrics: We evaluated our solution using precision, recall, and F1 score to ensure it met the highest standards of accuracy and efficiency.
- Precision: Measures the proportion of true positive matches out of all matches identified. High precision indicates that most of the identified duplicates were correct.
- Recall: Measures the proportion of true positive matches out of all actual duplicates. High recall indicates that most of the duplicates were successfully identified.
- F1 Score: The harmonic mean of precision and recall, providing a balanced measure of accuracy and completeness.

Results

Our solution demonstrated excellent performance, with precision and recall scores indicating high accuracy in identifying duplicates. Here’s a summary of the results:

Precision: 1.00
Recall: 0.95
F1 Score: 0.97

These metrics highlight the effectiveness of our approach in identifying both obvious and subtle duplicates in large datasets.

What This Means for You

Imagine the impact of implementing such a solution in your organization. With Epsilon3 LLC, you can:

Improve Data Accuracy: Eliminate duplicate records and inconsistencies in your supplier database.
Increase Efficiency: Streamline your source-to-pay processes, reducing time spent on manual data cleaning.
Enhance Compliance: Ensure data integrity, supporting better decision-making and compliance with regulatory requirements.

In just 45 minutes, using advanced AI and Python techniques, we showcased how quickly and effectively we can address a long-standing challenge in procurement data cleanup. This is just a glimpse of what Epsilon3 LLC can bring to the table.

Why Choose Epsilon3 LLC?

Our team combines deep procurement knowledge with advanced technical expertise. We stay ahead of the curve, leveraging the latest technologies to deliver innovative solutions tailored to your needs. Whether it’s supplier data management, strategic sourcing, or spend analysis, we have the skills and experience to drive your procurement transformation.

Contact Us

Ready to revolutionize your procurement processes? Contact Epsilon3 LLC today and discover how we can help you achieve your goals. Our advanced data de-duplication tool is available for free and includes full instructions. Contact us at hello@epsilon-three.com to get your copy.

Appendices

Appendix A: Data Preparation

To ensure our testing was rigorous, we generated synthetic supplier data designed to mimic real-world complexities. Our datasets included a mix of exact duplicates, subtle variations, and complex inconsistencies.

Exact Duplicates:
- Example 1: “General Electric” with tax ID “12-3456789” vs. “General Electric” with the same tax ID but different formatting.
- Example 2: “Tech Solutions Inc.” with phone number “234-567-8901” vs. “Tech Solutions Incorporated” with the same phone number but slightly different name formatting.
Subtle Variations:
- Example 1: “GE” vs. “General Electric”, identified through their common tax ID and address.
- Example 2: “Smith-Jones Inc.” vs. “Smith Jones Inc.” identified through phonetic similarity and similar contact details.
Complex Inconsistencies:
- Address variations: “1 Electric Ave” vs. “1 Electric Avenue”.
- Abbreviation differences: “St” vs. “Street” and “Blvd” vs. “Boulevard”.
- Typographical errors: “Catherine’s Catering” vs. “Katherine’s Catering”.
- International addresses: Including non-US records with varied address formats, postal codes, and contact details.

Appendix B: Matching Techniques

Our approach incorporated multiple advanced matching techniques to ensure thorough de-duplication of supplier records.

Exact Matching:
- We used multiple identifiers such as tax IDs, phone numbers, and contact emails.
- Example: “General Electric” with tax ID “12-3456789” and “GE” with the same tax ID, phone number, and contact email.
Fuzzy Matching:
- Leveraged Levenshtein Distance and the FuzzyWuzzy library to identify matches with high similarity scores.
- Example: “Tech Solutions Inc.” vs. “Solutions Tech Inc.” identified through high similarity score using Levenshtein Distance.
Phonetic Matching:
- Applied Soundex and Double Metaphone algorithms to match names that sound similar but are spelled differently.
- Example: “Smith-Jones Inc.” vs. “Smith Jones Inc.” matched using phonetic algorithms.
- Phonetic algorithms were particularly useful for catching variations in pronunciation that might not be obvious through text-based matching alone.
Clustering:
- Combined TF-IDF vectorization and agglomerative clustering to group similar records based on names and addresses.
- Example: Grouped “General Electric” with slight variations in names and addresses.
- Clustering allowed us to identify groups of records that, while not identical, shared enough similarities to be considered potential duplicates.

Appendix C: Performance Metrics

To ensure our solution was both accurate and efficient, we evaluated its performance using three key metrics: precision, recall, and F1 score.

Precision:
- Measures the accuracy of the duplicates identified.
- Formula: Precision = True Positives / (True Positives + False Positives)
- High precision indicates that the duplicates identified were mostly correct.
- Our precision score of 1.00 means every duplicate our tool identified was indeed a duplicate.
Recall:
- Measures the completeness of the duplicates identified.
- Formula: Recall = True Positives / (True Positives + False Negatives)
- High recall indicates that most of the actual duplicates were identified by the tool.
- Our recall score of 0.95 means we identified 95% of all actual duplicates in the dataset.
F1 Score:
- The harmonic mean of precision and recall, providing a balanced measure of accuracy and completeness.
- Formula: F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
- An F1 score of 0.97 indicates excellent overall performance, balancing both precision and recall effectively.

By demonstrating our capability to quickly develop and implement an advanced supplier data management solution using Generative AI and Python, we at Epsilon3 LLC show how we can bring innovation and efficiency to your procurement processes. Let us help you transform your procurement strategy and achieve excellence.