Data Mapping

Overview

Every system structures data in its own way. One calls a field "Employee_ID", another "WorkerNumber"; one stores a date as day-month-year and another as an ISO timestamp; one has a single name field and another splits it into first and last. Data mapping defines how each source field corresponds to a target field and what happens on the way: format conversions, code translations, splitting or combining fields, defaults and validation rules. Data maps underpin integration, data migration, data warehousing and privacy compliance.

Why It Matters

Mapping errors are among the most common causes of failed integrations and migrations. A wrong mapping silently corrupts data in the target system, and those errors spread to every report and process that relies on it.

Types of data mapping transformations

• Direct mapping: a field is copied as-is (source email → target email).
• Format conversion: dates, numbers, currencies or units are converted.
• Value translation: codes are translated between systems ("FT" → "Full-time").
• Splitting and combining: one field becomes several, or several become one.
• Lookups and enrichment: values are fetched from another source during mapping.
• Defaults and validation: missing values are filled or flagged, and invalid ones rejected.

The data mapping process

• Identify the source and target systems and the records to move.
• Profile the source data to understand formats, gaps and quality.
• Match fields and define each transformation.
• Test the map on real records and compare the results.
• Document the map and maintain it as either system changes.

Where data mapping is used

• Integration: keeping data consistent between live systems.
• Data migration: moving records from a legacy system to a new one.
• Data warehousing and ETL: loading data from many sources into one model.
• Privacy compliance: GDPR and similar laws require knowing where personal data lives and flows, often recorded as a data map.

How New Odyssey Helps

New Odyssey uses AI-assisted data mapping to propose field matches and transformations between systems, which an engineer reviews and approves, shortening the slowest part of building an integration.

Frequently Asked Questions

What is an example of data mapping?

Mapping a CRM's "Account Name" field to an ERP's "Customer Name" field, and translating the CRM's country names into the ERP's two-letter country codes, so that new customers sync correctly.

What is the difference between data mapping and ETL?

Data mapping defines how fields correspond and transform between source and target. ETL (extract, transform, load) is the process that executes those mappings to move data.

Can data mapping be automated?

Partly. Tools and AI can suggest matches based on field names, formats and sample data, but a person should review the mappings, especially transformations and business rules, before they go live.