What is Data Cleaning? Data cleaning, also known as data cleansing, refers to the meticulous process of identifying and correcting errors, inconsistencies, and inaccuracies within a dataset.

Data validation in machine learning plays a critical role in ensuring that data sets adhere to specific project criteria and affirming the effectiveness of prior cleaning and transformation efforts.

This can involve data cleansing, mapping, normalization, and other transformation processes to ensure the data is accurate and consistent.

This infographic looks at the dangers of dirty data, the different types of dirty data, and the steps you should take to clean your data.

NEW YORK, Aug. 6, 2025 /PRNewswire/ -- BigID, the leading platform for data security, privacy, compliance, and AI governance, today announced Data Cleansing for AI, a new capability designed to ...

Cast Iron developed the data-profiling and conversion functionality on its own. But the company is not looking to compete head-to-head with heavy-duty data-cleansing tools sold by the likes of ...