Alteryx Day 15 – Fuzzy Matching Introduction Explained

Welcome to Day 15 of the 100 Days of Alteryx Learning Series!
Today, we introduce one of Alteryx’s most powerful data quality features — Fuzzy Matching.

Fuzzy Matching helps you identify records that are similar but not exactly the same, making it extremely useful for duplicate detection, data standardization, and customer matching scenarios.

What You Will Learn Today
🔍 What is Fuzzy Matching?

• Exact match vs fuzzy match
• Why fuzzy matching is needed
• Common real-world data quality problems

🧩 Fuzzy Matching Tools in Alteryx

• Fuzzy Match Tool overview
• Match styles like Name, Address, Company
• Match threshold and confidence score
• Understanding match groups and outputs

🛠 Key Concepts

• Similarity scoring
• Tokenization and phonetic matching
• Handling spelling variations
• Managing false positives and negatives

Real-World Use Cases

• Identifying duplicate customer records
• Matching vendor names with spelling differences
• Cleaning CRM and master data
• Address and company name matching
• Improving data quality before analytics

By the End of This Video, You Will Be Able To:

• Understand how fuzzy matching works
• Know when to use fuzzy matching instead of joins
• Identify common fuzzy match scenarios
• Prepare for hands-on fuzzy matching examples in upcoming lessons

Perfect For

Data analysts, BI developers, data engineers, and anyone dealing with messy or inconsistent datasets.


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