Data Cleaning

Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.

What is the process of data cleaning?

Data cleaning, also called data cleansing, is the process of ensuring that your data is correct, consistent and useable by identifying any errors or corruptions in the data, correcting or deleting them, or manually processing them as needed to prevent the error from happening again.

Why is data cleaning important?

Data cleansing is also important because it improves your data quality and in doing so, increases overall productivity. When you clean your data, all outdated or incorrect information is gone – leaving you with the highest quality information.

What is data cleaning in statistics?

‘Cleaning’ refers to the process of removing invalid data points from a dataset. Many statistical analyses try to find a pattern in a data series, based on a hypothesis or assumption about the nature of the data. In the process, we ignore these particular data points, and conduct our analysis on the remaining data.

What is data cleansing in ETL?

In data warehouses, data cleaning is a major part of the so-called ETL process. We also discuss current tool support for data cleaning. 1 Introduction. Data cleaning, also called data cleansing or scrubbing, deals with detecting and removing errors and inconsistencies from data in order to improve the quality of data.

Why does data cleaning play a vital role in analysis?

Regardless of the type of analysis or data visualizations you need, data cleaning is a vital step to ensure that the answers you generate are accurate. When collecting data from several streams and with manual input from users, information can carry mistakes, be incorrectly inputted, or have gaps. See the full article for more details.