Once you have acquired your data, the next step is to inspect it. The primary goal at this stage is to sanity check the data, and the best way to accomplish this is to look for things that are either impossible or highly unlikely. As an example, if the data has a unique identifier, check to see that there is indeed only one; if the data is price-based, check that it is always positive; and whatever the data type, check the most extreme cases. Do they make sense? A good practice is to run some simple statistical tests on the data, and visualize it. The outcome of your models is only as good as the data you put in, so it is crucial to get this step right.