Data cleansing is when a computer program detects, records, and corrects inconsistencies and errors within a collection of data.

Find out what data cleaning is, its benefits and pieces, how it compares against data transformation and how to clean your data.

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

As data becomes everything to everybody, it’s crucial to oversee the quality of information that runs through an organization.

Cast Iron Systems, maker of an appliance for integrating SaaS and on-premise applications, is introducing a new version that adds data cleansing and migration tools, along with a library of ...

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

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 ...