While learning to write code and looking at data seems the natural starting point of any analytics job, doing so leads to a high likelihood of failure.

The data analysis workflow is the fundamental framework for data analytics projects. The first step in the data analysis workflow is always to understand the problem. If you start your project by trying to apply tools to data, before understanding the problem to be solved, you are setting yourself up for failure. This is one of the most important lessons you can learn in your journey to becoming a data analyst.

When approaching problem-solving with data, always start by identifying and breaking down the business problem before working through the rest of the process: getting the right data, cleaning the data, exploring the data, and finally, producing results. This is an iterative process. There are lots of mini-loops in the process, where you might realize in the problem-solving stage that you have to go back to the data-finding and -cleaning steps, and prepare other data to add to what you already have. Or you may get all the way to the results stage, only to discover a new layer of the problem that requires different solutions in order to provide real value to the client. In those cases, you should return to the beginning of the cycle and work through the steps again.

This cycle is just as important as learning coding. In fact, it may be more important than coding now, since there are tools like ChatGPT which can write sufficient code. If you only learn to code without learning this problem-solving process, you will not be able to solve actual business problems and produce maximum value from the project.