Data mapping is assigning or mapping one collection of data, known as the source, to another set of data, known as the target. The objective is to improve the data organization, coherence, and accessibility to your team or customers. Since different platforms have distinct data languages, data mapping fills the language gap and enables seamless data migration, integration, or transformation from one platform to another. Additionally, data mapping finds personal and non-personal information across systems before collecting it in one location for simple access, tracking, and security.
How to Conduct Data Mapping
Below are five quick steps to complete the data mapping process.
Determine Which Data Fields Need to Be Mapped
Step one in data mapping is to identify which data needs to be transferred or reformed. Since only one technique works for some, what you hope to achieve with your data mapping will determine everything.
- Integration - Examine your data sources to determine the quantity of data that needs to be merged, the sources from which they originate, and the frequency of your integrations.
- Migration - Evaluate the source data to determine the target site’s requirements. Additionally, automated software will be more beneficial for the migration if a lot of data is involved.
- Transformation - Consider your data source when choosing the format for your final destination. While automated tooling is required for the majority of modern transformations, smaller tasks can be completed manually.
Ensure Consistency in Naming Practices Among Sources
You need to determine the structure and format of the data in each data source, then specify a layout and design for the target data. For example, when merging information from your marketing team's email list into your sales team's contact list, sales records use a different date format than marketing records. When the data reaches its destination, you must decide what form you want it to be in.
Make Logic for Schemas and Data Transformation Rules
This stage will be significantly influenced by the way your data is being mapped:
- Automated – In automated systems, drag-and-drop user interfaces handle your work. Even non-technical staff members can map out complex data in minutes without coding with the correct data mapper.
- Semi-automated – Here, you connect your data sources to their final destination using software, and then a knowledgeable developer or data scientist manually verifies the connections are functional.
- Manual – Manual data analysis involves hiring a skilled technician to manually complex code the rules or schemas that connect your data sources to their destinations.
Assess Your Logic
You need to verify that your data and data systems are sound to maintain the highest possible level of data quality. You can manually verify any mistakes by moving a tiny sample of the mapped data and checking for any mistakes or errors in the system. Automated data mapping software makes the process easier, as these solutions have built-in verifications and real-time alerts.
Finalize the Transformation, Integration, or Migration
Once you have tested your logic, you can finish your migration, integration, or change. The difficulty of the entire process will depend on the outcomes you want and the instruments you are using.