Cleaning up dirty data – how AI identifies and resolves issues in datasets
Data cleansing, also known as data cleaning or data scrubbing, is the process of identifying and rectifying (or removing) errors, discrepancies, and inaccuracies from a dataset. This can involve detecting duplicate records, handling missing or incomplete data, validating and correcting values, formatting, standardizing, or normalizing data, and dealing with outliers.
The main objective of data cleansing is to improve the quality and reliability of your data, ensuring it is accurate, consistent, and in a format suitable for your purposes. This is crucial as dirty or messy data can interfere with your analysis, lead to inaccurate insights and conclusions, and negatively impact decision-making processes.
It’s important to note that data cleansing should be a regular part of data management – it’s not a one-time task, as new errors can be introduced when new data is added or existing...
