Data curation is essential to obtain any valid results with artificial intelligence. Data preparation also is different for each algorithm. Each machine learning algorithm requires data to be formatted in a very specific way and before finding the right combination of columns and yield useful insights I did several iterations.
Missing data and wrong formatting (when migrating data in Orange in our case) have been issues to deal with. Generally speaking for missing data there are two options, either remove the data points or fill it up with average values (there are a lot more options to consider but this is basically what I did in the various iterations).
Formatting is quite straightforward, we simply want Orange to properly see each informative feature as a number (and not as a string).