These issues can lead to inaccurate results and conclusions if not properly addressed.Īccording to a survey of data professionals conducted by Experian, data cleansing can take up to 15% of a company's time dedicated to data preparation. Data cleansing is a crucial step in ensuring that the analytics performed is based on reliable and relevant data.Įxamples of inaccuracies or inconsistencies that may be found in a dataset include typos, missing values, duplicates, outliers, and non-standardized formats. The main objective of data cleansing is to ensure that data is consistent, accurate, and complete. Data Cleansingĭata cleansing, also known as data scrubbing, is the process of identifying and correcting or removing inaccuracies and inconsistencies from a dataset.

In this blog post, we'll explore those differences, including facts and figures. While they may seem interchangeable, they have distinct differences.

Two commonly used methods to ensure data quality are data cleansing and data normalization.

Maintaining quality data is necessary to ensure accurate analytics and decision-making processes.

Data cleaning vs data dredging