What Is Data Matching? Techniques and Applications

The volume of data that you have to handle to make your business workflow efficient is increasing continuously. Such large-scale data records are usually related but scattered across different storage systems. This results in data silos, increasing the cost and complexity of data management. To overcome these issues, you can utilize the data matching procedure. It enables you to eliminate duplicate data records and create a unified database that conveys meaningful information.

Let’s learn about data matching in detail, including its techniques and real-world applications. By knowing this, you can develop a high-quality dataset that provides useful insights to enhance your business growth.

What is Data Matching?

Data matching is the process of comparing two or more different datasets to identify fields or attributes that represent the same entities. Sometimes, the data records in these attributes do not appear identical but convey the same information. After matching such data records, you can merge or eliminate the duplicate records to ensure data consistency.

Suppose you store order and sales data in different databases in your business organization. The data record ‘Jill Smith’ in the orders dataset and ‘J.Smith’ in the sales dataset refers to the same person. To ensure accurate business insights during data analysis, it is crucial to match these disparate records.

How Does Data Matching Work?

The data matching process consists of several steps. Some of these steps are:

Data Blending

Start by blending your data to create a central repository. This process involves combining data from various sources and loading it into a data blending solution like Google Sheets. Further, you can utilize join, aggregation, or union operations to merge datasets based on common attributes.

Data Standardization

After blending, convert the data into a uniform format through cleaning and transformation techniques, such as normalization or string parsing. You can also use data profiling tools to examine any inconsistencies in your enterprise data.

Selecting Less Changing Attributes

You should match those data records that do not usually change over time, such as customer IDs or names. Fields like addresses or phone numbers are highly probable to change, leading to discrepancies in data matching.

Sorting Data into Blocks

While matching high-volume data, you should sort it into blocks for better management. You can do this by grouping data with common attributes such as product category or order date.

Matching the Data Records

After sorting, you can start matching data using one of the two widely used techniques: deterministic and probabilistic. Deterministic matching allows you to link exactly similar attributes within datasets. Alternatively, probabilistic matching enables you to compare data records based on predefined rules or criteria.

Assigning Value to Matches

During the data matching, you should assign values or weights to potential matches based on their relevance or probabilities. You can then calculate the similarity score between the matched data records using cosine similarity, Euclidean distance, Jaccard index, or Hamming distance methods.

Calculating Total Weights

Lastly, calculate the total weight obtained after matching different attributes like names, dates, or IDs. You may decide a threshold score that represents the accuracy of the data matching process. The matched data can be further merged or eliminated to avoid duplication.

How does Airbyte Take Care of Data Matching?

You have seen above that data blending is a prominent step in matching data. To effectively combine data into a unified format, you can utilize Airbyte, a robust data movement platform. It offers an extensive library of 550+ pre-built connectors to extract data from relevant sources and load it into a destination system of your choice.

Once the data is blended, Airbyte provides several ways to support data matching through the following approaches:

1. Schema Synchronization for Consistent Data Structures

Airbyte allows schema synchronization, which aligns the structure (columns or attributes) of source and destination data systems schemas to maintain consistency. This process ensures that the data from disparate sources can be matched accurately.

Steps for schema sync in Airbyte:

• Login to your Airbyte account and set up a connection using source and destination connectors of your choice.
• Click on the Schema tab on your connection page. Each stream (a group of related records) consists of numerous fields or columns. Choose which streams you want to sync and how you want to load them into the destination.

• You can select or deselect streams by toggling the checkbox on or off in front of the stream.

• You can select the sync mode while creating a connection. Airbyte enables you to modify the sync mode of each stream.

2. Incremental Stream Synchronization

Airbyte supports various sync modes. This includes:

• Incremental Append, 
• Incremental Append + Deduped, 
• Full Refresh Append, 
• Full Refresh Overwrite, 
• Full Refresh Overwrite + Deduped.

For effective data matching, you should opt for Incremental Append + Deduped mode, as it facilitates syncing of updated streams without duplication. To understand the different sync modes offered by Airbyte in detail, click here!

3. Namespace Mapping to Avoid Overlaps

Airbyte supports namespace mapping, which helps organize data from different sources into separate logical structures (namespaces). In Airbyte, the source namespace refers to the location from which data is replicated. The destination namespace is the location at which the replicated data is stored.

Airbyte allows you to sync source and destination namespaces after setting up a connection. If you are replicating data from multiple sources, you can opt for a custom destination namespace to avoid overwriting or duplication of data.

4. dbt Integration for Data Transformations

You can integrate Airbyte with dbt, a powerful command-line tool for transforming and modeling data. With dbt, you can normalize or standardize data and perform deduplication to ensure high data quality.

Some additional important features of Airbyte are as follows:

• Build Developer-Friendly Pipeline: PyAirbyte is an open-source Python library that provides a set of utilities for using Airbyte connectors in the Python ecosystem. Using PyAirbyte, you can extract data from varied sources and load it into SQL caches. This cached data is compatible with Python libraries like Pandas, enabling you to manipulate and transform data for business intelligence operations.‍
• Change Data Capture (CDC): Airbyte’s CDC feature lets you capture incremental changes made at the source data system and replicate them into the destination. Through this, you can keep the source and destination in sync with each other, maintaining data consistency.‍
• RAG Transformations: You can integrate Airbyte with LLM frameworks like LangChain or LlamaIndex to perform RAG transformations like indexing and chunking. This helps you improve the accuracy of outcomes generated by LLMs.

Data Matching Algorithms You Should Be Aware of

There are several algorithms that you can use to match databases. Some of these are:

Fuzzy Matching

Fuzzy matching algorithms help you identify data records that are approximately similar. Some examples of fuzzy matching algorithms are Levenstein distance, Soundex, and Jaro-Wrinkler distance algorithms.

Exact Matching

Exact matching algorithms assist you in finding exact matches of any data records. You can use these algorithms to match zip or postal codes in enterprise databases. However, exact matching is not a very efficient technique, as slight variations in data format can result in mismatching. Binary search algorithms are an example of exact matching algorithms.

Numeric Matching

Numeric matching algorithms are suitable for matching numeric data records such as prices, ages, or phone numbers. Similar to exact algorithms, numeric algorithms also assist you in finding precise data matches. However, errors can occur if the data records contain decimal values.

Data Matching Use Cases

Some sectors where data matching can be utilized are:

• Banks: Detecting fraud becomes easier by matching data points indicating repeated suspicious financial transactions. By using this information, you can quickly take corrective actions and prevent further losses.‍
• Healthcare: You can consolidate and match patients’ historical health data records to avoid suggesting medicines that conflict with earlier treatments. This enables you to provide the correct medical treatment.‍
• Law Enforcement: Creating a centralized biometric data repository, including fingerprints and DNA, can help you detect crimes easily. You can match the data in this repository with the forensic data records collected from crime locations to identify criminals.

How Data Matching Benefits a Business?

Data matching can help enhance the operational efficiency and profitability of your business. Some of the ways in which it benefits your business include:

Improves Customer Service

By combining and matching data records from CRM, ERP, customer support datasets, and other data systems, you get an overview of customer’s interests and behavior. You can utilize these insights for targeted marketing and quick resolution of customer queries.

For example, you can match customer login frequency with your churn indicators dataset to identify if there is a decrease in engagement. You can then take preventive measures like offering personalized services to avoid customer churn.

Aids in Regulatory Compliance

Data matching helps remove or merge repetitive customer contact data points to prevent errors in communication. In addition, you can match customer activity data with consent data records to send marketing content to only those customers who have given consent. Such capabilities enable you to comply with regulatory frameworks like GDPR, which have strict guidelines for consent management and avoiding misleading communication.

Optimizes Operational Cost

De-duplication of data due to data matching can help minimize the consumption of resources required to store and retrieve the same data records at multiple places. Due to this, the overall operational costs decrease, allowing you to invest money in other important business aspects.

Conclusion

Data matching is essential to create a high-quality dataset for business analytics. This blog gives you a comprehensive overview of data matching and some of its prominent techniques. By matching data records, you can develop a data repository that can be used for diverse purposes across different sectors, including banking and healthcare. In business, you can utilize data matching techniques to improve customer service, optimize data management costs, and increase revenue growth.