In addition to @ParindDhillon, I’d like to say a few points;
In the Business world, explaining the conclusions formed by your analyses to the concerned party is as important as building an accurate predictive model. If the feature your model uses does not have business value, your model cannot be relied upon. .So it is recommended to do proper analysis in every project you take.
In Competitions, features are basically spoon-fed to you. But still it is a best practice to understand your data before getting on with the problem. The purpose of doing analysis here is to find out which features are more informative, try to make the extract useful information from the less informative ones and make sure your model gets this information. That is why you can see that the top contenders for the prize always do extensive analysis and feature engineering.
Also, you have to understand that most of the machine learning models are dumb. They rely on features given to them, to give the outputs. Therefore the more better features you give, the better your model performs.
For the categorical feature engineering, you should look at this webpage for ideas.
PS: Here’s a general article on feature engineering.