Understanding Fuzzy Data Deduplication

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Fuzzy data deduplication is a process used to identify and remove duplicate records from a dataset. This process is beneficial when dealing with datasets that contain errors, variations, or inaccuracies. Fuzzy data deduplication uses advanced algorithms and machine learning (ML) techniques to compare records and determine if they are duplicates, even if the data is not an exact match.

Fuzzy data deduplication is used to identify records that refer to the same real-world entity despite variations in the data. For example, a record that lists a hotel’s name as “APP PI MT. RESORT” and another record that lists it as “APPLE PIE MOUNTAIN RESORT” would likely be considered duplicates, even though the name is not an exact match. Similarly, a record that lists a city as “Georgia” and another record that lists it as “GE” would likely be considered duplicates.

Fuzzy data deduplication can be applied to text, numbers, and dates. It can also deduplicate data across different fields, such as name, address, and phone number. The process involves comparing records using various techniques, such as string, phonetic, and numeric matching. These techniques allow the system to identify duplicates even when the data is not matched.

Use Cases of Fuzzy Deduplication

Fuzzy data deduplication is needed in various applications because duplicate records can cause numerous problems, negatively impacting the effectiveness of these applications.

  • Customer relationship management (CRM): In CRM, duplicate records can lead to inaccurate customer information, resulting in ineffective marketing campaigns and poor customer service. For example, customers who receive multiple promotions for the same product may become annoyed and less likely to engage with the company.
  • Fraud detection: In fraud detection, duplicate records can lead to false alarms and wasted resources. For example, suppose a fraud detection system flags the same transaction multiple times as suspicious. In that case, it can lead to a loss of time and resources in numerous investigations for the same transaction.
  • Data integration: In data integration, duplicate records can lead to inconsistencies and errors. For example, if the same customer is represented in two different systems with slightly different information, it leads to confusion and mistakes while merging the data.
  • Data warehousing and master data management: In data warehousing and master data management, duplicate records can lead to inaccuracies in reporting and analysis. For example, a sales report that includes the same transaction multiple times can lead to inflated sales figures and incorrect conclusions.

Fuzzy Deduplication Process Demystified

The process of fuzzy data deduplication can be broken down into several steps.

Data Cleaning

The first step in the process of fuzzy data deduplication involves removing errors, inconsistencies, and outliers from the dataset.

  • If a dataset contains invalid characters/special characters like !, @, #, $, %, ^, &, *, (,) in any field, these characters would be removed during the data cleaning step.
  • If a dataset contains multiple records for the same person with different name spellings, these records would be consolidated into a single record during the data cleaning step.

Data Standardization

This step involves converting the data into a consistent format.

  • If a dataset contains phone numbers in different formats like 555-555-5555, 555.555.5555, or (555) 555-5555, these phone numbers would be converted to a standard format like 5555555555.
  • If the dataset contains dates in different formats, they would be converted to a standard format like YYYY-MM-DD.
  • Ordinal numbers in the address field would be converted to cardinal numbers, such as First to 1, Third to 3, and Seventy-Ninth to 79.
  • White spaces across all the attributes will be stripped.


This step involves grouping similar records to reduce the number of comparisons that are required to carry out. For example, if we have 100 records in our dataset, the total number of comparisons would be n (n-1)/2. So, 100 (99)/2 = 4950 comparisons. This is acceptable for small datasets, but it is not favorable for large volumes of data. So, we minimize the number of comparisons for faster processing and lesser computation resource utilization, thereby helping organizations reduce their operating expenditure. For example, in a dataset containing customer records, we can perform blocking by grouping by attributes like zip code and city.


This step involves comparing the records within each block to identify duplicates. For example, string matching, phonetic matching, and numeric matching techniques would be used to compare names, addresses, and phone numbers, respectively. These techniques allow the system to identify duplicates even when the data is not matched.

Active Learning Model Creation

Active learning can help create an ML-based deduplication model by selectively choosing records the model is uncertain about. This can lead to improved accuracy, reduced human labeling effort, and faster convergence of the model.

Clock-Triggered Operationalizing

This step involves integrating the deduplication process into the data pipeline so that it can be run automatically regularly. For example, the deduplication process could be scheduled to run every night at midnight, and the number of duplicates found and removed can be tracked.


Step-by-step fuzzy data deduplication identifies and removes duplicate records. This ensures that the data is accurate and consistent, which helps organizations in taking better decisions, improve customer engagement, and save costs. Fuzzy data deduplication is a valuable method for enhancing data quality, reducing data redundancies, and improving the effectiveness of various applications across industries.

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