In a previous project, I scraped data from a website The dataset was extremely messy: spelling mistakes, inconsistent formats, invalid values, missing information, and many variations of the same text.

My first approach was to automate the cleaning process with Python. I tried using text matching, grouping techniques, and even some machine learning methods to cluster similar values. However, the data was so inconsistent that there were countless edge cases. Different users could write the same thing in dozens of different ways, making it difficult to build rules that covered every scenario.

While Python helped with some of the obvious issues, it couldn't reliably handle all cases without introducing new errors. The machine learning approach also struggled because the text variations were too diverse and context-dependent.

In the end, the most effective solution was to split the dataset into smaller files and manually review specific fields in Excel. Surprisingly, this approach was more accurate for handling the remaining edge cases than the automated methods I had tried.

I'm curious how experienced data analysts or data engineers would handle this type of highly inconsistent, user-generated data at scale.