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Data Mapping

July 14, 2025

Data Mapping

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What is Data Mapping?

What is Data Mapping? Data mapping is a fundamental process in data management that defines the relationship between data fields in different systems or datasets. It essentially creates a blueprint for how data moves, transforms, and is understood across diverse platforms.
Review the below graphic.
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tracking and documenting the flow of data…
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sample data mapping sheet
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Data Mapping Tools
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Why is Data Mapping important?

  • Data Integration and Consolidation: It enables combining data from multiple sources (databases, applications, APIs) into a coherent data model, creating a unified view of information and eliminating data silos.
  • Data Migration and Transformation: It facilitates the smooth transfer of data between systems (e.g., from old systems to new platforms) while maintaining its integrity and consistency. It defines how data needs to be converted, formatted, or manipulated to meet the requirements of target systems.
  • Data Quality and Accuracy: By standardizing data and defining how it should be handled, it helps improve data quality, reduces errors and inconsistencies, and ensures data reliability for analysis and decision-making.
  • Business Intelligence and Analytics: This exercise is crucial for preparing it for analysis, reporting, and deriving valuable insights, according to Flatfile.
  • Regulatory Compliance: It plays a vital role in meeting privacy regulations like GDPR and CCPA by providing transparency and traceability of flows, aiding in identifying sensitive files, and ensuring that protection measures are in place.

How It Works

  1. Identify Sources and Targets: Determine the origin and destination of the data.
  2. Define Mapping Rules: Establish the relationships and transformations between the source and target data elements.
  3. Perform Data Transformation (if needed): Modify, clean, or aggregate data to align with the target format and requirements.
  4. Validate and Test: Verify that the mappings are accurate and produce the desired results.
  5. Execute and Monitor: Implement the mapping process and continuously monitor for performance and accuracy.
  6. Maintain and Update: Regularly review and update mappings as systems and requirements evolve.

Data mapping techniques

  • Schema mapping: Matching the structure and attributes of data schemas or models between source and target systems, like mapping “First Name” and “Last Name” to corresponding fields in another schema.
  • Transformation mapping: This means defining how data should be transformed (converted, formatted, calculated) when moving from the source to the target. For example, merging first and last names into a single “Full Name” field is addressed here.
  • Value mapping: Translating specific data values or codes from one system to another, such as mapping country codes or product codes.
  • Relationship mapping: Defining relationships between its entities like customers and orders.
  • Hierarchical mapping: Handling nested or layered data formats like XML or JSON.
  • Attribute-based mapping: Connecting information based on values or characteristics, used in deduplication or customer identity resolution.
This manual work is traditionally performed using spreadsheets or code. Increasingly, people leverage the help of automated tools that leverage AI and machine learning. Doing so can certainly simplify and streamline the process.
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