Difference Between Data Matching And Data Mapping

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In the world of data management, data matching and data mapping are two concepts that are often used interchangeably, but they actually refer to two distinct processes. While both processes are critical for managing data effectively, they serve different purposes and require different techniques. In this article, we’ll explore the differences between data matching and data mapping in detail.

What is Data Matching?

Data matching is the process of comparing two sets of data to determine whether they refer to the same entity or not. It involves comparing different attributes of the data sets to identify similarities and differences. Data matching is typically used to identify duplicate records within a single data set, or to match records from different data sources.

For example, let’s say that you have two customer databases, one from a point-of-sale system and the other from an e-commerce platform. You want to merge the two databases to create a single, unified view of your customers. However, there may be duplicate records in each database, and you need to identify them before merging the data. Data matching can help you identify records that refer to the same customer across both databases, even if the data is not identical. As you can see, there are many data matching use cases such as in governmental sector, in financial, health and business.

Data matching can be done using a variety of techniques, including exact matching, fuzzy matching, and probabilistic matching. Exact matching involves comparing data attributes exactly, while fuzzy matching involves comparing data attributes that are similar but not identical. Probabilistic matching involves using statistical algorithms to determine the likelihood that two records refer to the same entity.

What is Data Mapping?

Data mapping, on the other hand, is the process of defining the relationship between two sets of data. It involves identifying the attributes of each data set and how they correspond to each other. Data mapping is typically used when integrating data from multiple sources into a single database or application.

For example, let’s say that you have two databases, one with customer information and the other with product information. You want to create a new application that allows customers to view their purchase history. In order to do this, you need to map the customer information to the product information, so that the application can display the correct information for each customer. Data mapping involves identifying the attributes of each data set (e.g., customer ID, product ID) and how they correspond to each other.

Data mapping can be done using a variety of techniques, including manual mapping and automated mapping. Manual mapping involves identifying the relationship between attributes manually, while automated mapping involves using software tools to identify the relationship automatically. Automated mapping can be faster and more accurate, but it may require more upfront investment in terms of software and training.

Key Differences Between Data Matching and Data Mapping

Now that we have a basic understanding of what data matching and data mapping are, let’s look at some of the key differences between the two processes:

Purpose

The purpose of data matching is to identify whether two sets of data refer to the same entity or not. The purpose of data mapping is to define the relationship between two sets of data.

Data Sources

Data matching is typically used to compare data from two different sources, such as two different databases. Data mapping is typically used to integrate data from multiple sources into a single database or application.

Techniques

Data matching can be done using a variety of techniques, including exact matching, fuzzy matching, and probabilistic matching. Data mapping can be done using manual mapping or automated mapping techniques.

Level of Detail

Data matching typically involves comparing individual attributes of the data sets, such as name, address, and phone number. Data mapping typically involves identifying the relationship between attributes at a higher level, such as customer ID and product ID.

Output

The output of data matching is a list of records that match, along with a confidence score indicating how likely it is that the records refer to the same entity. The output of data mapping is a mapping between the attributes of the two data sets, indicating how they correspond to each other.

Challenges

Both data matching and data mapping come with their own set of challenges. Data matching can be challenging because data is often inconsistent, incomplete, or ambiguous. It can be difficult to determine whether two records refer to the same entity if the data attributes are not identical. Data mapping can be challenging because different data sets may use different terminology or have inconsistent data structures, making it difficult to map the attributes accurately.

Both data matching and data mapping are important for managing data effectively. Data matching helps to ensure data accuracy and completeness by identifying duplicate records and eliminating them. Data mapping helps to integrate data from multiple sources into a single application, enabling better decision-making and a more complete view of the data.

FAQ:

Q: What is data matching? 

A: Data matching identifies and links records that refer to the same entity across different data sources. For example, data matching can help you find and merge duplicate customer records in your database.

Q: What is data mapping? 

A: Data mapping matches data fields or elements from one source to their related data fields in another destination. For example, data mapping can help you transfer and transform data from a spreadsheet to a data warehouse.

Q: What is the difference between data matching and data mapping? 

A: Data matching and mapping are important steps in data integration, migration, and analysis, but they have different purposes and outcomes. Data matching is about finding and linking records that belong to the same entity, while data mapping is about matching and transforming fields that belong to the same schema.

Conclusion

In summary, data matching and data mapping are two distinct processes that serve different purposes in data management. Data matching is used to compare two sets of data to determine whether they refer to the same entity, while data mapping is used to define the relationship between two sets of data. Both processes are critical for managing data effectively, but they require different techniques and approaches. By understanding the differences between data matching and data mapping, organizations can make better decisions about how to manage their data and improve data quality.

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