Data cleansing is an important step to prepare data for analysis. It is a process of preparing data to meet the quality criteria such as validity, uniformity, accuracy, consistency, and completeness. Data cleansing removes unwanted, duplicate, and incorrect data from datasets, thus helping the analyst to develop accurate insight.
Organizations today are sitting on a pile of data. Most of them use advanced data tools to collect a variety of data in large volumes. The raw data often contains inaccuracies, which if not removed, can result in false outcomes. Data cleansing or data scrubbing is a fundamental step in data analysis to arrive at the right context and conclusion.
