What is 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 - Wikipedia

Importance of Data Cleaning.

Data cleaning is an essential process in data analysis that involves detecting, diagnosing, and editing data abnormalities. It is a crucial step in preparing data to meet quality criteria such as validity, uniformity, accuracy, consistency, and completeness. Data cleaning involves detecting, diagnosing, and editing data abnormalities to ensure that the data used for analysis is accurate and reliable.

Data cleaning removes unwanted, duplicate, and incorrect data from datasets, thus helping the analyst to develop accurate and quality insight. In the context of evaluations of safety and efficacy, data cleaning is important because it ensures that the data used for analysis is accurate and reliable. By removing errors and inconsistencies, data cleaning helps to minimize the impact of these errors on study results.

How do I know if my data needs cleaning?

To determine if your dataset needs cleaning, you should check for data abnormalities such as missing values, duplicates, and inconsistencies. Inconsistencies can arise from errors in data entry, measurement, or recording, and can lead to inaccurate results.

Here are some common signs that your dataset may need cleaning:

• Missing values: If there are many missing values in your dataset, it may be an indication that the data was not collected properly or that there were errors in data entry.

• Outliers: Outliers are data points that are significantly different from other data points in the dataset. They can be caused by measurement errors or other factors and can skew the results of your analysis.

• Inconsistencies: Inconsistencies can arise from errors in data entry, measurement, or recording, and can lead to inaccurate results.

If you notice any of these signs, it is a good idea to perform data cleaning before proceeding with your analysis.