Data quality issues, such as missing, duplicate, inaccurate, valid, and inconsistent values, cause headaches in finding and using data sets. Having a suitable data cleansing procedure handles this bad data and makes it suitable for other people and systems.
A helpful data cleansing process standardizes data, fixes, or removes erroneous values, and formats records to be readable. You get these adequate results from data cleansing when you know your data’s original purpose and visualize the good data you require to meet new goals. You need to create a good foundation and run through the essential data cleansing checklist in this article to achieve your objectives.