Snowflake Soundex & Fuzzy Matching

Data quality is essential for reliable sales analysis and reporting. Unfortunately, even meticulously curated datasets can fall victim to the human element: misspellings. Inaccurate customer names, for instance, can lead to duplicate records, missed opportunities, and skewed reporting. Snowflake's SOUNDEX function offers an elegant solution to this challenge by identifying similar-sounding names, even if their spellings differ.

Understanding Soundex

Soundex is a phonetic algorithm that encodes words based on their pronunciation. It transforms a word into a four-character code, consisting of:

The first letter of the word.
Three digits representing similar-sounding consonants.

For instance, "Johnson" and "Jonson" are assigned the same Soundex code, "J525," as they are phonetically similar.

A Practical Example: Sales Data Analysis

Let's explore how to use Snowflake Data Classification in a practical scenario.

⛁ Sample Data Model: salesdb-data-model

Setup the SalesDB sample data model and proceed with the rest of the exercises. This model provides a robust foundation for tracking customers, buyers, clients, and sales opportunities.

>_ SQL

// Assign the database schema
use schema

Let's delve into how this Snowflake feature can supercharge your data workflow:

Similar Last Names Across Tables

>_ SQL

-- +----------------------------------------------------+ 
-- | 1. Identify Similar Last Names Across Both Tables | 
-- +----------------------------------------------------+
SELECT
    c.CustomerID, c.FirstName, c.LastName AS CustomerLastName,   -- Select columns from Customer
    b.BuyerID, b.FirstName, b.LastName AS BuyerLastName,       -- Select columns from Buyer
    SOUNDEX(c.LastName) AS CustomerSoundex,                   -- Calculate Soundex for Customer
    SOUNDEX(b.LastName) AS BuyerSoundex                       -- Calculate Soundex for Buyer
FROM custs.Customer c
FULL JOIN custs.Buyer b ON c.CustomerID = b.CustomerID       -- Join tables to compare all customers with potential buyers
WHERE SOUNDEX(c.LastName) = SOUNDEX(b.LastName)             -- Find matching Soundex codes
    AND (c.LastName != b.LastName OR b.LastName IS NULL);   -- Exclude exact matches and cases where one is NULL

The query will produce a result set where each row contains information about two potentially related individuals – one from the Customer table and one from the Buyer table. If the individuals have the same Soundex code for their last names but the names are not exactly the same or one is NULL, they are likely to be the same person with a misspelling or data entry error.

Row 1:

The last names "Miller" (from Customer) and "Mila" (from Buyer) have different Soundex codes (M460 and M400 respectively). This means that while they might sound similar, Soundex does not consider them a strong phonetic match.

Row 2:

The last names "Tailor" (from Customer) and "Taylor" (from Buyer) have the same Soundex code (T460). This indicates a very strong phonetic match, and it's highly likely that these are the same person with a misspelling in one of the tables.

Important Note:

While the Soundex algorithm is helpful for identifying potential misspellings, it's not foolproof. The first row of "Miller" vs "Mila" demonstrates that Soundex might not always find similar-sounding names if their spellings diverge significantly.

Similar Last Names Within Tables

>_ SQL

-- +----------------------------------------------------+
-- | 2. Identify Similar Last Names Within Each Table   |
-- +----------------------------------------------------+

-- For Customer table
SELECT
    c1.CustomerID AS CustomerID1, c1.FirstName AS FirstName1, c1.LastName AS LastName1,  -- First customer's details
    c2.CustomerID AS CustomerID2, c2.FirstName AS FirstName2, c2.LastName AS LastName2   -- Second customer's details
FROM custs.Customer c1
JOIN custs.Customer c2 ON c1.CustomerID < c2.CustomerID     -- Self-join to compare each customer with others
                     AND SOUNDEX(c1.LastName) = SOUNDEX(c2.LastName); -- Find similar-sounding last names

-- For Buyer table (similar to the Customer query, just change table and column names)
SELECT
    b1.BuyerID AS BuyerID1, b1.FirstName AS FirstName1, b1.LastName AS LastName1,     
    b2.BuyerID AS BuyerID2, b2.FirstName AS FirstName2, b2.LastName AS LastName2
FROM custs.Buyer b1
JOIN custs.Buyer b2 ON b1.BuyerID < b2.BuyerID           
                    AND SOUNDEX(b1.LastName) = SOUNDEX(b2.LastName)

The provided SQL code identifies potential duplicate or misspelled records within the Customer and Buyer tables of your sales data model. It does this by performing a self-join on each table, comparing every last name with every other last name. The SOUNDEX function is used to match last names that sound similar but may have different spellings. The ON clause in the join, specifically c1.CustomerID < c2.CustomerID (and its equivalent for the Buyer table), ensures that each pair of records is compared only once, eliminating redundant comparisons.

The output of this query will show pairs of customer or buyer records with similar-sounding last names, aiding in the identification of potential data quality issues.

Practical Considerations For Soundex

Not Perfect: Soundex isn't foolproof. Words that sound different but have similar spellings may have the same code, while words that sound similar but have different spellings may not.
Language: Soundex is primarily designed for English. If you're working with data in other languages, you might need to consider alternative phonetic algorithms.
Data Cleansing: Use Soundex as part of a broader data cleansing strategy. You'll likely need to combine it with other techniques to ensure data quality.

Soundex vs. Other Fuzzy Matching Functions: A Comparison

Snowflake offers various functions for fuzzy matching, each with strengths and weaknesses depending on your specific use case. Let's explore how Soundex compares to other common options:

Soundex:

Strengths:
- Phonetic Matching: Excellent for identifying names with common misspellings or variations in pronunciation.
- Simple & Efficient: Relatively fast and easy to use.
Weaknesses:
- Limited to English: Primarily designed for English names and may not work well for other languages.
- Not Precise: May generate the same code for words that sound different (false positives) and different codes for words that sound similar but are spelled differently (false negatives).

Example:

>_ SQL

SELECT SOUNDEX('Johnson'), SOUNDEX('Jonson'); -- Both return J525

Other Fuzzy Matching Functions:

JAROWINKLER_SIMILARITY: Measures string similarity based on the Jaro-Winkler distance, which considers character transpositions and matches. Good for names and short strings.
EDITDISTANCE: Calculates the Levenshtein distance, the minimum number of single-character edits (insertions, deletions, substitutions) required to transform one string into another. Useful for detecting typos and minor variations.
N-gram Similarity: Breaks strings into overlapping sequences of characters (n-grams) and compares the similarity of these n-grams. Effective for longer text, like addresses.
Cosine Similarity (for Embeddings): Measures the similarity between word or sentence embeddings in a high-dimensional space. Well-suited for semantic understanding and identifying conceptually related text.

Example (JAROWINKLER_SIMILARITY):

>_ SQL

SELECT JAROWINKLER_SIMILARITY('Johnson', 'Jonson'); -- Returns 0.9583 (higher similarity than Soundex)

Important Note: In the "Johnson" vs. "Jonson" example, Jaro-Winkler provides a more nuanced similarity score that better reflects the small difference in spelling, while Soundex oversimplifies the comparison by treating them as identical due to their similar sounds.

When to Use Which:

Soundex: Best when dealing with potential misspellings in names or words with phonetic variations.
JAROWINKLER_SIMILARITY or EDITDISTANCE: Suitable for general string comparisons, especially with short strings like names or product codes.
N-gram Similarity: Preferred for longer text strings like addresses or product descriptions.
Cosine Similarity (Embeddings): Ideal for capturing semantic meaning and finding related text that may not share similar spellings.

Choosing the right function depends on your specific data and the type of similarity you want to capture. Often, combining multiple techniques yields the best results.