So this could be either irrelevant variables or irrelevant cases. Starting with data quality issue number 1: Irrelevant dataĭata can be irrelevant if it is not of interest to the analysis you are trying to do. That’s what I am going to be showing you in this video With that definition, you should have an idea already about what is involved in data cleaning. Well, data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted. In this video, I will point you to data quality issues you need to look out for, and how you can fix them using SPSS. This is why data cleaning is an extremely important step in data analytics.ĭata quality issues are very common in data that has been collected through surveys, or imported from other formats for example databases or Microsoft Excel worksheets. And no one wants to make decisions based on trashy data.
If you use garbage data, you get garbage results. Well, that’s the truth about working with data. You probably have heard the term garbage in garbage out.
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