Why must data be cleaned before it is analyzed?

In the context of data mining and broader data analysis, data must be cleaned before it is analyzed to minimize errors and inconsistencies.1

Raw data often originate from multiple sources and may be disorganized or contain inaccuracies. Cleaning is a critical part of the transformation process—often referred to as the "transform" step in ETL (extract, transform, and load)—which ensures that the information fits the specific analytical needs and the schema of the destination database or data warehouse.1

Key reasons for cleaning data include:
* Improving Accuracy: Removing errors and inconsistencies ensures that the insights derived from the data are reliable.1
* Standardization: It allows for the adjustment of formats across different fields, ensuring consistency.1
* Data Integrity: The process involves removing duplicate records and renaming fields to create a clean, usable dataset.1

By ensuring the data is clean, researchers and businesses can effectively apply statistical and computational techniques to generate accurate insights, identify trends, and build reliable predictive models.1