Entity Resolution Without ML: A Python Playbook Achieved 100% Match Rate

A founder's practical approach to entity resolution in Python, using off-the-shelf libraries without machine learning or a database, improved a CRM join rate from 58% to 100%. This method offers a repeatable pipeline.

A founder attempting to join a Crunchbase dataset against their CRM initially achieved only 56 matches out of 96 records, a 58.3% success rate. The remaining 40 records, despite referring to the same real-world entities, failed to join due to minor naming discrepancies like "Necker FinTech" versus "Necker FinTech Holdings Inc." This common problem, known as entity resolution, highlights the limitations of exact string matching in real-world data environments.

Instead of deploying machine learning models or complex database solutions, the founder developed a four-step Python pipeline. This process, detailed in a recent post, successfully normalized, deduplicated, and fuzzy-matched records. The result was a 100% join rate for both directions of the dataset comparison, demonstrating a significant improvement over the initial exact-match approach.

The Problem: Inexact Matches

The core challenge in entity resolution is that a single real-world entity, such as a company, often appears with different names across various data systems. Legal names, abbreviations, and slight variations like "Investing.com" versus "Fusion Media Limited" prevent direct matches. A standard SQL JOIN or a Python == operator on raw names will fail to identify these as the same entity. This leads to data fragmentation and operational inefficiencies, such as sales representatives inadvertently cold-pitching existing customers.

For the founder's Crunchbase and CRM data, the initial exact match yielded only 56 successful joins when comparing scraped hub rows to CRM entries. When the comparison was reversed, CRM rows against scraped data, the exact match rate dropped further to 34.8% (48 out of 138 records). This discrepancy underscores the prevalence of naming variants within typical CRM lists, where multiple spellings or legal names often point to the same company.

Choosing Simple Tools

The founder explicitly chose to avoid machine learning, vector embeddings, and a dedicated database for this entity resolution task. This decision focused the solution on readily available, simpler tools. For data scraping, Bright Data was used to extract company names from Crunchbase hubs. The crucial fuzzy matching component was implemented using RapidFuzz, specifically its fuzz.WRatio algorithm. This combination allowed for a lightweight, Python-centric approach without the overhead of more complex infrastructure.

RapidFuzz's fuzz.WRatio function calculates a similarity score between two strings, accounting for various types of differences including transpositions, insertions, deletions, and substitutions. By setting a similarity threshold, the system can identify records that are highly similar but not identical. This method directly addresses the issue of varying company names, allowing for matches where exact equality checks would fail.

The Four-Step Pipeline

The implemented pipeline consists of four distinct stages: scraping, normalization, deduplication, and fuzzy matching. First, company names were scraped from Crunchbase hubs. This step provided the raw input for one side of the matching process. Second, normalization involved standardizing the scraped names and CRM entries. This typically includes lowercasing all text and stripping punctuation or common corporate suffixes like "Inc." or "Limited." Normalization reduces superficial differences that could hinder matching.

Third, deduplication ensures that within each dataset (scraped data and CRM), unique canonical names are established. This step helps in reducing the number of comparisons needed in the final fuzzy matching stage. Finally, the normalized and deduplicated lists were subjected to fuzzy matching using RapidFuzz's fuzz.WRatio with a threshold of 90 or higher. This threshold was critical for identifying strong, but not exact, matches between the two datasets. The founder noted that this process successfully collapsed multiple legal-name variants into single canonical clusters.

Achieving 100% Resolution

The practical application of this four-step pipeline yielded a complete resolution of the entity matching problem for the given dataset. The join rate from scraped hub rows to CRM entries increased from 58.3% to 100% (96 out of 96 records). Similarly, when matching CRM rows against the scraped data, the success rate jumped from 34.8% to 100% (138 out of 138 records). This outcome demonstrates that a targeted, non-ML approach can be highly effective for specific entity resolution challenges.

The founder's decision to forgo machine learning and databases was deliberate. The simplicity of the RapidFuzz library, combined with a clear understanding of the data's characteristics, allowed for a direct solution. This approach bypassed the complexities of model training, feature engineering, and database management, delivering a high-performance outcome with minimal infrastructural overhead.

WHAT WE'D CHANGE

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