Upon data delivery (e.g., by your coach, by a company, or when you collected the data using web scraping or APIs), it’s important you try opening the data to assess its quality. This gives you a chance to follow up with your data provider or debug your extraction code before it’s too late.

The best way to go about is to read in the various data files in your statistical program, and use simple descriptive statistics and plots to inspect the data.

• Inspect your raw data, or aggregations thereof, by looking at the data tables (e.g., View in R, or simply by loading data into Excel if file sizes permit)
• Cross-tabulate data (e.g., table() in R, pivot tables in Excel)
• Plot your data (e.g., plot() in R), or distributions of your data (e.g., hist() in R); many times, it is advisable you first aggregate your raw data by a common unit (e.g., brands, time, playlists, clusters), before plotting.