Data Derivation

The Data Derivation feature within the DOCK Integration Framework is focused on processing and preparing member data after it is initially captured. It involves transforming raw data into formats that are more conducive to efficient data management and comparison, which is essential for maintaining data quality and integrity.

Functionality:

• Standardized Data: Attributes from source systems are standardized to ensure uniformity, which involves alphabet normalization, punctuation removal, anonymization checks, and data ordering.
• Bucket Data: The system groups standardized data into 'buckets' for efficient candidate selection during data comparison.
• Comparison Data: The system compiles attributes into a single, delimited string for easy access and comparison.

Benefits:

• Increases the accuracy of data comparison algorithms by providing standardized and structured data.
• Enhances the speed and efficiency of the member identification process through bucketing strategies.
• Supports the overall matching process by preparing data in a form that's readily consumable by operational algorithms.

Purpose:

• To optimize the selection and matching of member records by converting disparate data into a consistent and comparable format.
• To aid in the accurate and quick identification of member data for operational use within the MDM system.

Operation:

• The process begins with the standardization of raw data to create a uniform format across all attributes.
• Standardized data is then segmented into buckets based on shared characteristics to facilitate the matching process.
• Finally, comparison data is organized into a streamlined format that the matching algorithms use to identify and link member records.

This feature ensures that the core member data is accurately processed and prepared for the subsequent matching and linking processes, which is crucial for the effective functioning of the MDM system.