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[MUSIC PLAYING] SPEAKER: Weve touched on the basics of cleaning internal and external data, but theres lots more to come. Soon well learn about the common errors to be aware of to ensure your data is complete, correct, and relevant. In this video, well focus on common issues associated with dirty data. These include spelling and other text errors; inconsistent labels, formats, and field length; missing data; and duplicates. This will help you recognize problems quicker and give you the information you need to fix them when you encounter something similar during your own analysis. This is incredibly important in data analytics. OK, lets go back to our law office spreadsheet. As a quick refresher, well start by checking out the different types of dirty data it shows. Sometimes someone might key in a piece of data incorrectly. Other times, they might not keep data formats consistent. Its also common to leave a field blank. Thats also called a null, which we learned about earlier.