Dirty Data -- Quality Assessment & Cleaning Measures - DataScienceCentral.com

In the book'Bad Data Handbook' Q Ethan McCallum has rightly said, "We all say we like data, but it's not the data but the insights that we derive from it are what we care about." Yet, a data analyst gets to dedicate only 20% of her time to the art and science of generating insights out of data. The rest of her time is spent in structuring and cleaning the data. In order to minimize the time investment in data cleaning, there is a need of standardized frameworks and tools that work for the diverse data and business use cases across industries, functions, and domains. This blog aims to equip you with the knowledge you need to build and execute such standardized data quality frameworks that work for your data and use cases.