Data Normalization Tips: How to Weave Together Public Datasets to Make Sense of the World

Data normalization is crucial when combining public datasets or merging them with proprietary data. This process involves organizing data to reduce redundancy and improve data integrity, often by dividing databases into smaller related tables and defining relationships between them. Normalizing data can be challenging in the realm of publicly available open datasets due to differences in data formats and potential gaps or NULL values. Techniques such as reformatting dates using tools like Excel or awk, and filling in data gaps with functions like Timescale's time_bucket_gapfill, can help overcome these challenges. Public datasets offer valuable resources for understanding the world, and proper data normalization is essential to make sense of them.