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As data becomes increasingly central to the success of organizations, automatic data mapping is proving instrumental in the realization of this success.

Traditionally, data mapping was a meticulous process that involved identifying data elements in one data format and manually translating them to the corresponding data elements in another format. Manual methods, such as spreadsheets or entity relationship diagrams (ERDs), were often used to create and document mappings between data sources and destination systems. With growing data volumes and increasingly complex business logic, manual data mapping has become a tedious and error-prone process.

Automatic data mapping leverages software or tooling to automatically or semi-automatically map fields from one system or format to another. Data teams rely on a variety of automatic data mapping techniques to transfer and synchronize data among two or more systems so that data can be seamlessly integrated and modeled, enabling organizations to make the most of their data.

In this article, we’ll explain the value of automatic data mapping and how it differs from traditional manual data mapping, illustrate common use cases, show where data mapping fits in the modern data stack, and describe various data mapping methodologies. Finally, to conceptualize some of the topics introduced in this article, we’ll provide a brief, real-life application of automated data mapping.

Summary of key concepts 

Concept Description
What is automatic data mapping? The automated or semi-automated process of matching data fields from one or more sources to their related data fields in a destination.
Data mapping use cases Data integration, migration, transformation, and synchronization.
Automated vs. manual data mapping Manual data mapping is a time-consuming and error-prone process that relies on human interpretation and input, while automatic data mapping uses software tools and algorithms to streamline and accelerate the process with greater accuracy.
Where mapping fits in the data pipeline In the modern, cloud-based data stack, automatic data mapping is typically leveraged in the integration layer of the data pipeline. Here, disparate data sources are combined to form a single, unified view of your data.
Typical methods used to automate data mapping
  • Rule-based mapping
  • Schema-based mapping
  • Metadata-based mapping
  • Machine-learning-based mapping
  • Template-based mapping
  • Hybrid mapping
Mapping steps that can be automated
  • Data source identification
  • Data structure comparison
  • Field matching
  • Data mapping
  • Testing
  • Updating
The value of automated data mapping to data transformation Once data is mapped, data transformation rules can be applied to the accurately mapped data elements to transform the data from one format to another.

Automated data mapping streamlines the data transformation process by reducing the risk of human error, ensuring that mapped data is accurate and consistent across disparate systems. 

Data mapping use cases

There are several circumstances that might indicate a need for data mapping, whether manual or automated. These include data integration, migration, transformation, and synchronization.

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Data integration

It’s not uncommon for organizations, particularly mid-sized to large ones, to store and manage their data in a variety of disparate systems. This includes customer data in customer relationship management (CRM) systems, financial models and forecasting data in enterprise resource planning (ERP) systems, and order-level details in supply chain management (CSM) systems, all of which may be siloed and decentralized. Data mapping can resolve the siloed nature of these systems by mapping data elements from each system to a common data mart, enabling integration across systems and supporting downstream analytics software.

Data migration

Data migration is the process of moving data from one system to another heterogeneous or homogeneous system. A typical example of this is the migration from on-premises legacy databases to cloud-based data warehouses. When migrating data, it’s critical to ensure that it is mapped correctly and consistently from the old system to the new system. This means accurately mapping data fields, schemas, and business rules to ensure that data is harmonized and consistent. 

Data transformation

Data transformation is the process of converting or manipulating data from one format or structure to another format or structure. Simplistic examples include modifying a date column from one standard date format to another or converting currency values from one currency to another. 

Data mapping specifies how data elements from the source system should be transformed to match the data elements in the target system. For example, let’s say you have data stored in a database that uses a different format for dates than the format used in your target system. Data mapping methodologies can specify how the date values should be transformed to accommodate the data structure in the target system. This may involve mapping the date field in the source system to a new field in the target system and specifying a transformation rule that converts the date values to the correct format.

Data synchronization

Data synchronization is the process of keeping data consistent across multiple systems or applications. When organizations rely on their data to draw compelling insights or solve business problems, synchronization ensures that the data is up to date and accurate.

Data mapping is a technique used in data synchronization to ensure that data is properly translated and transferred between systems. It involves creating a mapping between the data elements in one system to corresponding data elements in another system, enabling seamless synchronization between the two systems. 

Suppose you have an online store that uses two different systems to manage customer data and order data: The customer data is stored in a CRM system, while the order data is stored in an order management system. To keep the customer data and order data synchronized, you would need to create a data mapping between these two systems. This mapping would ensure that the customer data is properly translated and transferred to the order management system and that any updates made to customer data are reflected in both systems.

Automated vs. manual data mapping

Until the recent advent of automatic data mapping software, data mapping was a highly manual exercise that would require analyzing source data fields and matching them to their target fields. Documenting the matching of source to target fields and any required transformation logic applied to these fields was often done in spreadsheet tools, like Microsoft Excel, or visually, using ERD tools. This process is time-consuming, expensive, and can lead to careless mistakes. Conversely, automatic data mapping uses software tools to analyze source data fields and automatically match them to their corresponding target fields. 

When comparing manual and automatic data mapping, the following should be considered. 

Time and cost

It requires a significant amount of time and effort to manually analyze and map source fields to target fields and to interpret typically complex business logic. With the help of automated data mapping software, the analysis and mapping of source and target formats can be done automatically and much faster than humans can do it. 

Since manual data mapping requires a significant amount of time and effort from human resources, it can be expensive. Automated data mapping is typically a more cost-effective approach as it reduces the need for human resources. 

Accuracy

Manual data mapping is a predominately human-driven process, making it prone to errors and potential inaccuracies. Human error can result in the incorrect mapping of data fields or even a misinterpretation of business logic, which can lead to erroneous or corrupted data in the target system. 

Software tools used in automated data mapping are designed to analyze source and target data formats accurately and precisely, eliminating the risk of human error.

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Scalability

Due to the time-consuming and labor-intensive nature of manual data mapping, it is difficult to map large volumes of data with complex schemas at scale. Moreover, this process can become overwhelming for larger datasets. Software tools used for automated data mapping are highly scalable and able to handle large volumes of data.

Where mapping fits in the data pipeline

Data mapping is an important part of the data pipeline because it ensures that data is properly transformed, standardized, and prepared for use in downstream processes. In the modern cloud-based data stack, data mapping typically occurs after data ingestion into a data lake and before data transformation in the semantic or integration layer of the data warehouse.

During data mapping, data analysts and engineers identify the source and target data structures and then create a mapping between the two structures. This involves identifying the fields in the source data that correspond to the fields in the target data as well as any data transformations that may be required to convert the source data into the desired format for the downstream process or application.

How data mapping traverses the modern data stack (represented by the arrows)

How data mapping traverses the modern data stack (represented by the arrows)

In the example above, flat files mounted to a staging area are ingested in their raw format into corresponding tables in the data lake of the data warehouse. Once ingested, data from the data lake tables needs to be modeled and transformed in the semantic layer of the data warehouse. To support downstream reporting and the derivation of business insights, data needs to be mapped to target sources and transformed to include business logic not present in the preceding raw data tables in the data lake. Finally, data in the semantic layer is mapped to a single table in the analytical layer and aggregated to enable the efficient querying of data by the organization’s reporting and visualization tool. 

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Typical methods used to automate data mapping

Depending on the automatic data mapping software you deploy, there are a variety of automatic data mapping techniques for you to choose from: 

  • Rule-based mapping is a set of predefined rules based on data types, values, field names or taxonomies, business logic, and other criteria that define how to map data from your source to target destinations automatically or semi-automatically.
  • Schema-based mapping requires analyzing the makeup of source and target schemas, including their attributes and structures, to automatically identify both similarities and differences that can aid in identifying corresponding data elements between fields or systems.
  • Metadata-based mapping uses descriptive, administrative, and structural characteristics from your data’s metadata to define how to map data fields from one system to another with high precision and accuracy. 
  • Machine-learning-based mapping leverages algorithms trained on a set of sample mappings or on past mapping activities to automatically predict how new data fields should be mapped to their target destinations. 
  • Template-based mapping uses predefined templates that contain a variety of permutations based on data types, values, and business logic to automatically map data fields from one system to another.
  • Hybrid mapping is an amalgamation of two or more of the above-mentioned approaches. For example, the hybrid method may leverage both a rule-based approach for simplistic data type mapping and a machine-learning-based approach for complex, nuanced mapping. Another hybrid approach may train its models using the machine-learning approach from the metadata discovered in the metadata-based mapping approach.

Mapping steps that can be automated

 The steps in automatic data mapping vary depending on the technologies and tooling used, but common tasks that are typically automated or semi-automated include the following:

  • Data source identification: Identifying data sources that need to be mapped, which is done by scanning databases, flat files, and other ingestible data sources
  • Data structure comparison: Analyzing schemas and the structures of the data source and target destination to identify compatibility in fields and their characteristics, including data type, length, format and sample values
  • Field matching: Comparing fields from the data source to the fields in the destination to determine if the fields are equivalent or similar in meaning
  • Data map generation: Executing the data mapping based on the relationships between the fields and specifying how the data should be mapped from one data source to another
  • Testing: Validating the mappings to ensure that they are accurate, functional, and compliant with the predefined matching rules
  • Updating: Ensuring that changes to source structures are automatically applied in the mapping to target destinations

Data mapping put into practice

Let’s put the concepts behind automated data mapping into practice with a real-life scenario.

Let’s say you work for a large online retailer that sells products on multiple platforms, including Amazon, eBay, and Shopify. Each platform has its own unique data structures and data formats, making it difficult to manage customers, inventory and sales across all platforms.

To solve this problem, you decide to use an automated data mapping tool to integrate these disparate retail platforms into a single, master inventory management system. Here’s how it might work:

  1. Data analysis: First, you need to analyze the data structures of each platform and identify the corresponding fields that need to be mapped. For example, Amazon might use a field called ASIN to identify a product, while eBay might use a field called ItemID. Your selected software tool would leverage the metadata mapping approach to read the metadata of each platform and compare the structure, taxonomy, data types, and nullable policies of the data.
  2. Matching: Once you’ve identified the corresponding fields, you would use field matching algorithms to match them together. For example, you might create a mapping between ASIN and ItemID so that the tool can identify the same product across the eBay and Amazon platforms. Mapping disparate product tables into a unified, master product table

    Mapping disparate product tables into a unified, master product table

  3. Validation: After the mapping is complete, the tool validates the data to ensure that the mapped fields contain the correct data. This is typically done by comparing sample data sets from each platform and verifying that they match up correctly. Human intervention by a QA analyst might also be used to certify that the mapping completed by the software tool was accurate. 
  4. Transformation: Once the mapping is validated, the tool can start transforming the data. For example, you might need to convert data types, change the date format, or apply some arithmetic on a numbered column. The tool would automatically transform the data to match the format required by the destination system.
  5. Output: Finally, the transformed data would be output in the format required by the master inventory management system. This could be a file, a table (as in the example above), or an API endpoint. The inventory management system can then use the transformed data to track inventory across all platforms.
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Conclusion

Automated data mapping offers numerous benefits for organizations looking to streamline their data management processes. By automating the mapping of data from various sources, organizations can save time and resources, reduce errors, and focus on gaining valuable insights from their data. Automated data mapping tools also offer scalability, enabling data teams to handle large data volumes, as well as flexibility in meeting their unique and often complex business requirements.

Nexla, a no-code data unification platform, automates data mapping through the unification and transformation of disparate data sources. Through its Universal Bidirectional Connectors, Nexla automatically scans your data sources on ingest and enables you to integrate, model, enrich, and validate your data for a variety of downstream use cases and applications. 

As data continues to play an increasingly important role in decision-making processes, automated data mapping is becoming an essential tool for businesses looking to stay competitive and make the most of their data.

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