Question

Provide an example of dirty data, and explain the importance of cleaning data as part of the data analytics life cycle.

Answer 1

One of the examples of dirty data is incomplete data.  For instance, the value of month field should range from 1 to 12, or an individual's age has to be less than 130. So, if a month field has a value of 16, it is invalid and hence dirty.

Data cleansing is also important because it improves your data quality and in doing so, increases overall productivity. When you clean your data, all outdated or incorrect information is gone – leaving you with the highest quality information.