Please share your most effective data cleaning hacks and how they’ve significantly saved you time while enhancing the quality of your data.
To kick things off, I’ll share one of my go-to strategies. Whenever I load a new data source, I make it a point to run the ‘Data Profiling’ feature in inFlow, located under the ‘Common’ tab. This feature provides a comprehensive statistical analysis of each column in your dataset. It highlights key aspects like minimum and maximum values, uniqueness of the data, and the range of data lengths. This initial overview is immensely helpful, allowing me to spot potential issues and address them early in the cleaning process, saving time and effort down the line.
I’m eager to hear about your methods and the tools you rely on to keep your data clean and reliable!
