Data profiling is the first step of any data initiative. It’s a series of checks and analyses to increase your understanding of the data in your possession. Some things you can reveal through data profiling include the shape of the data, any missing entries, relationships, anomalies, and more.
If you wanted to use a dataset for analysis but weren’t sure if it was complete (containing all the necessary data points/elements), then you could run it through a data profiler to confirm it has everything you need.
Data profiling is usually done through a data profiler, an application that can generate information about data patterns, numeric statistics, and other characteristics like the ones we mentioned above. Companies can use this tool to evaluate their data sets, determining if they’re fit for the data initiative at hand (like data analysis, building a data quality program, data migration, etc.).
It’s hard to understand whether or not a data set is useful or usable without profiling it first. Whatever you want to use your data for, having a more profound, structural understanding of it will put your project in a better position to succeed.