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Data mapping

Written by the Fiveable Content Team • Last updated September 2025
Written by the Fiveable Content Team • Last updated September 2025

Definition

Data mapping is the process of creating a correspondence between two distinct data models, often involving the transformation of data from one format to another. It’s essential for ensuring that the data collected from various sources aligns correctly with the structure and requirements of a target system, allowing for accurate data integration and analysis.

5 Must Know Facts For Your Next Test

  1. Data mapping is crucial when migrating data between systems to ensure consistency and integrity of the data being transferred.
  2. Effective data mapping can help identify discrepancies or errors in data quality before integration takes place.
  3. Data mapping can be done manually or through automated tools that facilitate the mapping process by suggesting field correspondences.
  4. It is essential for businesses to regularly update their data mappings to adapt to changes in source systems or business requirements.
  5. Data mapping not only helps with migration but also plays a key role in data warehousing and reporting processes.

Review Questions

  • How does data mapping support effective data integration during migration processes?
    • Data mapping supports effective data integration during migration processes by establishing clear relationships between the source and target data models. This ensures that each piece of information from the source aligns correctly with its corresponding field in the target system. By defining how data should be transformed and integrated, data mapping minimizes errors and helps maintain data integrity throughout the migration.
  • Discuss the importance of maintaining updated data mappings in relation to changes in business requirements.
    • Maintaining updated data mappings is vital as business requirements evolve over time. If changes occur in source systems, such as new fields being added or existing ones being modified, having an up-to-date mapping ensures that all relevant data continues to flow accurately to the target system. This proactive approach helps avoid issues related to outdated mappings, which can lead to incorrect analysis or reporting due to mismatched or missing data.
  • Evaluate the impact of automated tools on the efficiency of the data mapping process.
    • Automated tools significantly enhance the efficiency of the data mapping process by streamlining workflows and reducing manual effort. These tools can automatically suggest field correspondences based on predefined rules or historical mappings, speeding up the mapping phase. Additionally, they help reduce human error, ensuring more accurate mappings are created quickly. This efficiency is especially beneficial during large-scale migrations or integrations where handling vast amounts of data manually would be impractical.