Mapping Best Practices

Owned by Neha Awalwar
Last updated: Sept 11, 2024
4 min read

Overview:

This section provides a comprehensive guide to help you successfully map fields between source and target systems during data integration. Whether you are dealing with general mapping scenarios or complex transformations, this guide covers essential aspects like data type consistency, field length, null values, and key fields. Following these best practices will ensure data accuracy, minimize errors, and improve the efficiency of your integration projects.

1. Data Type Consistency

Explanation: Ensuring that source and target data types align is crucial for accurate data transfer. Data type mismatches can lead to integration errors or data loss.

Best Practices:

  • Ensure Compatibility: Match the data types of source and target fields. For example, integers should map to integers, strings to strings, etc.

  • Apply Conversions: Where data types differ, apply the correct conversion logic. For instance, use functions to convert string types to date formats when required.

  • Avoid Common Pitfalls: Pay close attention to fields like boolean values, which different systems may store as true/false, 1/0, or even yes/no.

Example:
If a source field OrderAmount is in string format but mapping it to a float field in the target will cause mapping failure.

2. Field Length

Explanation: The length of data stored in source and target systems may vary. Inconsistent lengths can cause truncation or data errors.

Best Practices:

  • Check Field Lengths: Compare the maximum length of each source and target field.

  • Avoid Truncation: For fields where the source exceeds the target’s length, consider truncating or transforming data to fit, but beware of data loss.

  • Encoding Considerations: Different systems may use different character encodings (e.g., UTF-8 vs. ASCII), which could impact field length.

Example:
The CustomerNotes field in the source may allow up to 500 characters, but the target system only allows 255. Set up a rule to trim excess text to avoid data truncation.

3. Mandatory vs. Optional Fields

Explanation: It’s important to understand the difference between mandatory and optional fields in both source and target systems. Failure to map mandatory fields can result in failed transfers.

Best Practices:

  • Identify Mandatory Fields: Ensure that all required fields in the target system are mapped from the source. If the source doesn’t provide the necessary data, define default values.

  • Default Values for Missing Data: Use placeholder values or create rules to handle missing data in optional fields.

Example:
If the CustomerID field is mandatory in the target but missing in the source, create a default value rule to ensure the record doesn’t fail the transfer.

4. Handling Null Values

Explanation: Not all systems handle null values the same way, which can lead to mapping issues if not properly addressed.

Best Practices:

  • Establish Rules for Nulls: Set clear rules for handling null values—whether to leave them as is, replace them with default values, or discard the records.

  • Use Default Values: For mandatory fields, replace nulls with default or placeholder values (e.g., "Unknown" for text, 0 for numeric fields).

Example:
For a PhoneNumber field, if null values are encountered in the source, replace them with a default placeholder like "000-000-0000" in the target.

5. Data Transformation Rules

Explanation: Data from the source may not be in the format that the target system expects. Applying transformations ensures data consistency and accuracy.

Best Practices:

  • Format Transformation: Convert data formats as needed (e.g., dates, numbers, and strings). This might include transforming date formats from MM/DD/YYYY to YYYY-MM-DD.

  • String Manipulation: Apply trimming or case conversions to strings to ensure they meet target system requirements..

Example:
Transform the OrderDate field from a MM-DD-YYYY format in the source to a YYYY-MM-DD format expected by the target system.

6. Field Name Differences

Explanation: Source and target systems often use different field names for the same data. Correct field mapping ensures data is transferred to the right location.

Best Practices:

  • Create a Field Mapping Document: Clearly document the mapping between source and target field names to avoid confusion.

  • Focus on Content, Not Names: Don’t rely solely on field names; map fields based on their purpose and the type of data they hold.

  • Automated Field Matching: Use 'AUTOMAP' functionality on field mapping page that automatically match fields based on similar names, but always manually verify for accuracy.

Example:
A CustomerAddress field in the source might map to BillingAddress in the target. Ensure the mapping reflects the content, not just the name.

7. Unit of Measure Consistency

Explanation: When working with numeric data, especially measurements or financial data, ensure that the units of measure are consistent across systems.

Best Practices:

  • Check Unit Consistency: Ensure that the source and target systems are using the same units.

Example:
If a product’s Weight field is in kilograms in the source but the target system uses pounds, this will cause incorrect data transfer as the units are different. Make sure the units match before mapping the fields.

8. Key Fields & Relationships

Explanation: Primary and foreign keys help maintain relationships between data records. Incorrect mapping of these keys can lead to data integrity issues.

Best Practices:

  • Ensure Key Mapping: Verify that primary keys (PK) and foreign keys (FK) in the source system map correctly to corresponding fields in the target.

  • Maintain Data Relationships: Keep relationships intact by ensuring foreign key values align with the primary keys of related records.

Example:
The OrderID field in the source should map to the OrderID in the target system to ensure that the order details, like items and customer information, are correctly linked.