In my previous blog post, I defined data mapping and its importance. Here, I explore how it works, the most popular techniques, and the common challenges that crop up and that teams must overcome to ensure the integrity and accuracy of the mapped data.

Data mapping establishes relationships and connections between data elements so we can understand how to transform, convert, or transfer data from one format to another to ensure compatibility, accuracy, and consistency. One example where data mapping is particularly important is complying with data privacy regulations, such as the California Consumer Privacy Act (CCPA). Privacy professionals have reported that data mapping is the most difficult CCPA requirement to achieve and that locating unstructured personal data is the most difficult type of data request to handle.

Eight-Step Data Mapping Process

1. Identify source and target data sets	The source might be a database, file, API response, or any other data source and the target data set could be another database, data warehouse, application, or analytics platform.
2. Define mapping requirements	Determine the specific data elements to be mapped from the source to the target, including fields, columns, or entities like customer names, product IDs, dates, etc. Define the mapping requirements based on business rules, data integration goals, transformation logic, and data quality standards.
3. Create data mapping rules	Define specific data mapping rules that specify how each data element from the source maps to its corresponding element in the target. Mapping rules may be simple one-to-one mappings where a source field directly corresponds to a target field (sometimes called “source-to-target mapping”) or involve complex one-to-many or many-to-one transformations, calculations, or lookups.
4. Perform data transformation (if necessary)	Data transformations can be tasks like data type conversions, data formatting, data cleaning (for example, removing duplicates and correcting errors), data aggregation, and deriving new fields. If data transformations are necessary, apply defined mapping rules to transform data as it moves from the source to the target.
5. Handle data mapping challenges	Plan for problems: implement error-handling mechanisms to identify and correct mapping errors or data validation failures to address challenges like handling discrepancies, resolving conflicts, managing data quality issues and ensuring consistency.
6. Validate and test data mapping	Comparing sample data from the source and target ensures that mappings are accurate and produce the expected results. Conduct testing and validation procedures to verify data integrity, completeness, and correctness after applying mapping rules and transformations.
7. Execute the process	Execute the data mapping process using appropriate tools, software, or scripts that support data integration, ETL (Extract, Transform, Load), or data migration tasks. Monitor the process for performance, scalability, and efficiency.
8. Document the details	Document everything, including mapping rules, transformations, source-to-target mappings, data lineage, metadata and any relevant information for future reference, especially regarding data governance, regulatory compliance, and knowledge sharing.

Four Common Data Mapping Techniques

Each data mapping technique has strengths and suits different data integration and transformation requirements. By using these techniques effectively, data teams can ensure accurate and meaningful data mappings that drive success.

• Schema mapping involves transferring the structure and attributes of data schemas or models between source and target systems, including tables, columns, relationships, and data types. For example, mapping fields like “First Name” and “Last Name” from one database schema to corresponding fields in another schema is an example of schema mapping.
• Value mapping aligns actual data values or codes between systems. It encompasses translating codes or identifiers used in one system to their equivalents in another, mapping country codes, product codes, customer IDs, or currency values.
• Relationship mapping maps associations between data entities, such as customer orders, product categories, or employee hierarchies. For example, it maps the relationship between a “Customer” entity and an “Order” entity in different databases.
• Transformation mapping defines the data transformations and business rules applied during data mapping, which includes data conversions, calculations, and validations.

Top Gotchas That Will Derail Data Mapping

Data mapping offers considerable benefits but is hardly foolproof. Teams must overcome common obstacles to maximize the potential of their mapping initiatives.

Data Challenges

Inaccurate or incomplete data will lead to mapping errors and unreliable insights. Data quality is a paramount requirement. Likewise, data security is critical. Protecting sensitive data and ensuring data privacy during data mapping processes is essential for customers, the organization’s reputation, and avoiding data breaches and compliance violations. Handling complex data transformations requires careful planning and execution. Do not shortchange the required prep and strategy when mapping requires data transformations. 

Workflow and Management Challenges

Scaling data mapping processes to handle large volumes of data and real-time data streams can be challenging and requires solid infrastructure and tools. Data mapping documentation should track changes, revisions, and updates over time to ensure proper version control is maintained at all times.

My final blog post in this series will examine common data mapping solutions (including ETL, iPass, MDM, data exchange, and open-source tools) and cover best practices.