Data Cleaning
Data cleaning is a structured method for identifying, correcting, and removing inaccurate, incomplete, or inconsistent records in datasets. It includes validation, standardization, deduplication, data profiling and missing-value treatment as well as rule-based transformations. The goal is a reliable, documented data foundation for analytics and operational use; it reduces risk and improves decisions.
This block bundles baseline information, context, and relations as a neutral reference in the model.
Definition · Framing · Trade-offs · Examples
What is this view?
This page provides a neutral starting point with core facts, structure context, and immediate relations—independent of learning or decision paths.
Baseline data
Context in the model
Structural placement
Where this block lives in the structure.
No structure path available.
Relations
Connected blocks
Directly linked content elements.