The third phase of your data analytics project is where you actually put your data to use. Start exploring your resources to discover what you have and how you may connect them all for the sake of achieving your first objective. To comprehend the meaning of all your variables, start making notes on your initial analyses and asking questions of businesspeople, the IT team, or other groups.

Your data cleaning is the next, and by far the most hated, stage. You've probably observed that, despite having a nation feature, for example, you may have misspellings or incomplete information. It's time to examine each of your columns to make sure your data is clean and homogeneous.

Warning! The longest and most frustrating step of any data analytics project is almost always this one. It will hurt for a while, but as long provided you remain focused on the end result, you will be able to get through it.

Making sure that your project and your data are compatible with data privacy laws is a key component of data preparation that should not be overlooked. Personal data privacy as well as protection are becoming more and more important, and you should prioritise them right away as you embark on your data journey. You must consolidate all of your data activities, sources, as well as datasets into one location or platform to assist governance in order to carry out privacy-compliant projects. Then, you must explicitly label any datasets as well as projects that include sensitive or personally identifiable information and that should be handled differently.