Data cleaning with regular expressions

Illustrating data cleaning with regular expressions

One of the most common problems with free-form data entry is that the data is not submitted in a standard form. This makes it hard to identify duplicated records and even harder to integrate data from a number of different sources to ensure data integrity.

For example, email addresses should always be in the form xxxx@yyy.zz and telephone numbers in the US should always have 10 digits. If you’re matching two datasets and one has +1 202-456-1111 and the other (202) 456 1111 you wouldn’t know that they are the same phone number unless you pre-process them.

At Spotless, we use regular expressions to ensure data validation so that a particular record is in the right format and if it isn’t then we automatically clean the data to put it into the right format.

For a great tutorial on regular expressions, try this link:

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