From a given dataset for analysis, it is extremely important to sort the information required for data analysis. Data cleaning is a crucial step in the analysis process wherein data is inspected to find any anomalies, remove repetitive data, eliminate any incorrect information, etc. Data cleansing does not involve deleting any existing information from the database, it just enhances the quality of data so that it can be used for analysis.

Some of the best practices for data cleansing include –

☛ Developing a data quality plan to identify where maximum data quality errors occur so that you can assess the root cause and design the plan according to that.

☛ Follow a standard process of verifying the important data before it is entered into the database.

☛ Identify any duplicates and validate the accuracy of the data as this will save lot of time during analysis.

☛ Tracking all the cleaning operations performed on the data is very important so that you repeat or remove any operations as necessary.