During the data-cleaning process, you might have to deal with missing values, which arise from various reasons, including the data-collection process, human mistakes, and technical issues. For example, if you create a form for job applicants where they can enter their date of birth in various formats (e.g., year, year-month, year/month/day), you might encounter missing values during analysis because some parts of the information are not provided. In another scenario, if you store your data in Excel files by year and inadvertently remove the data for 2012, that data will become missing for future analysis. Alternatively, if you want to use a government index in your prediction model, but the value of that index was not reported for a specific period in the past, it will be treated as a missing value in your analysis.