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Definition: Data Cleansing

Data Cleansing (also known as Data Cleaning or Data Scrubbing) is the process of detecting, correcting, or removing corrupt, inaccurate, incomplete, irrelevant, improperly formatted, or duplicate records within a dataset.

Key Aspects in the Helix Context:

  • Purpose: To improve data quality, consistency, and reliability before or during migration, ensuring the accuracy and usability of data in the target system.
  • Importance: While not explicitly detailed as a standalone service in the provided slides, data cleansing is an implicit and critical step in complex migration projects, especially when dealing with data from multiple or legacy sources which may have inherent quality issues.
  • Activities: Can involve validating data against rules, standardizing formats, correcting errors, removing duplicate entries, and handling missing values.
  • Timing: Can occur pre-extraction, during the transformation stage of ETL, or post-load in the target system, depending on the specific migration strategy and tools used.