Matching Inconsistent Company Names Across Snowflake Tables Using AI-Powered Similarity Keys
In Snowflake environments, company and organization names often arrive from multiple source systems—CRMs, ERP platforms, partner feeds, and third-party datasets. These names rarely match exactly. Variations such as “IBM”, “International Business Machines”, “I.B.M. Corp”, or “Intl Business Machines Inc.” break joins, fragment analytics, and otherwise drastically reduce data ROI.
Interzoid has specialized Snowflake capabilities that solve this problem by generating a canonical similarity key for each company name directly inside Snowflake data tables. Once similarity keys are attached, matching across tables becomes a simple, deterministic join instead of a fragile, likely inaccurate string comparison.
/company-name-matching/snowflake-examples/insert-simkeys.sql
Key technologies used:
- Snowflake SQL
- Snowpark Python User-Defined Function (UDF)
- Interzoid Company Name Matching API
Why Company Name Inconsistencies Break Snowflake Analytics
Without normalization, inconsistent organization names cause:
- Failed joins across fact and dimension tables
- Duplicate entities inflating counts and revenue metrics
- Inaccurate rollups and reporting
- Manual, error-prone cleanup logic embedded in SQL
Traditional approaches—UPPER/LOWER, regex cleanup, fuzzy string functions, or Levenshtein distance—operate purely at the character level. They cannot reliably recognize acronyms, abbreviations, reordered tokens, or semantic equivalence.
How the Snowflake Script Works
The insert-simkeys.sql script uses a powerful but simple pattern:
- Create a Snowpark Python UDF inside Snowflake
- The UDF calls Interzoid’s Company Name Matching API
- The API returns a deterministic Similarity Key (SimKey)
- The script inserts those keys back into your Snowflake table
Because the logic runs natively inside Snowflake via Snowpark Python, the matching process fits naturally into existing SQL pipelines and ELT workflows.
Snowpark Python UDF + Interzoid API
At the core of the script is a Snowpark Python user-defined function. This UDF:
- Receives a company name from a Snowflake SQL statement
- Calls Interzoid’s API using Python
- Applies AI-powered normalization and matching algorithms
- Returns a similarity key to Snowflake
That similarity key represents a canonical, normalized version of the organization name. Any variation referring to the same real-world company produces the same key.
For example:
- IBM → same key as International Business Machines
- GE → same key as General Electric Company
- BOA → same key as Bank of America
Matching Across Tables Using Similarity Keys
Once similarity keys are stored in your tables, matching becomes trivial:
SELECT a.company_name,
b.company_name,
a.simkey
FROM table_a a
JOIN table_b b
ON a.simkey = b.simkey;
This replaces brittle string joins with a clean, scalable key-based join strategy that works across datasets, time, and source systems.
Why This Outperforms Fuzzy Matching in Snowflake
Interzoid’s similarity keys are generated using:
- AI- and ML-driven matching models
- Organization-specific normalization logic
- Knowledge bases of real-world company naming patterns
- Semantic equivalence detection
This approach understands that acronyms, abbreviations, punctuation, and corporate suffixes often represent the same entity—something Levenshtein distance and basic fuzzy functions cannot do reliably.
More Snowflake Scripts and APIs Available
Interzoid provides additional Snowflake-ready SQL scripts and examples for:
- Individual name matching
- Street address matching
- Organization name standardization
- Business and company enrichment
- Parent company/subsidiary data
- Email trust and validation
All follow the same core pattern: generate normalized keys or structured enrichment data using Interzoid APIs, then leverage Snowflake for large-scale analytics and joins.
Matching inconsistent company names inside Snowflake no longer requires complex SQL hacks or unreliable fuzzy matching logic. By using Interzoid’s ready-to-run Snowflake script with a Snowpark Python UDF, you can generate AI-powered similarity keys directly in your warehouse and match organizations accurately across tables.
The result is cleaner data, better joins, more accurate analytics, and higher data ROI from every downstream system that depends on consistent company and organization names.