Data Validation

Data analysis directly depends on the accuracy of the data being analyzed. And while it doesn't have to have high accuracy when it's initially ingested, it needs to be improved until a minimum standard is reached.

And because of this, data validation is critical in ensuring that the inputs to an analysis are accurate, consistent, and within expected ranges.

Without validation, there's a risk of basing insights and decisions on flawed/biased data. Validation includes applying rules, such as checking for duplicates, range checks, and data type verification, to catch errors early.