What are the best practices for data cleaning? | Eklavya Online

What are the best practices for data cleaning?

There are 5 basic best practices for data cleaning:

  • Make a data cleaning plan by understanding where the common errors take place and keep communications open.
  • Standardise the data at the point of entry. This way it is less chaotic and you will be able to ensure that all information is standardised, leading to fewer errors on entry.
  • Focus on the accuracy of the data. Maintain the value types of data, provide mandatory constraints and set cross-field validation.
  • Identify and remove duplicates before working with the data. This will lead to an effective data analysis process.
  • Create a set of utility tools/functions/scripts to handle common data cleaning tasks.