Data Mapping

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Data Mapping is the process of linking or matching data fields from one source to another. It ensures that data from different systems, databases, or formats can be accurately transferred, integrated, or transformed.

Key Aspects of Data Mapping:

• Source Data Identification – Identifying where the data is coming from.
• Target Data Definition – Determining where the data needs to go.
• Field Mapping – Matching data fields between the source and target systems.
• Transformation Rules – Applying rules to convert data into the required format.
• Validation & Testing – Checking for accuracy, consistency, and correctness.
• Execution & Automation – Implementing the mapping process for data transfer.

Some of key Business use cases of Data Mapping:

• Data Migration (e.g., transferring data from an old system to a new one).
• Data Integration (e.g., merging or collecting data from multiple sources into a single system).
• ETL/ELT (Extract, Transform, Load) Processes (e.g., preparing data for analysis).
• Data Synchronization (e.g., keeping records updated across different platforms).
• Data Quality: By establishing clear mappings, data mapping helps prevent errors and ensures data integrity
• Business Intelligence: It allows analysts to easily query and analyze data from multiple sources.

Data Migration is the process of transferring data from one system, storage, or format to another. It ensures that data remains accurate, consistent, and usable during the transition.

Types of Data Migration:

• Storage Migration – Moving data between storage devices (e.g., HDD to SSD or cloud storage).
• Database Migration – Transferring data & scripts from one database to another (e.g., MySQL to PostgreSQL).
• Application Migration – Moving data between applications (e.g., CRM system upgrade).
• Cloud Migration – Transferring data from on-premises systems to the cloud (e.g., AWS, Azure).
• Business Process Migration – Moving data related to company processes or workflows.

Key Steps in Data Migration:

• Planning & Assessment – Understanding source and target systems, by defining goals.
• Data Extraction – Pulling data from the source system.
• Data Cleansing & Transformation – Removing errors, standardizing formats.
• Data Mapping – Aligning data fields between source and target systems.
• Script Migration - Changing script/codes from source to target database which will be running based on target database's objects.
• Testing & Validation – Ensuring accuracy and integrity after migration.
• Deployment & Monitoring – Implementing migration with minimal disruption.