When importing data into your data warehouse, you will almost certainly encounter data quality errors at many steps of the ETL pipeline. How do you catch these errors proactively, and ensure data quality in your data warehouse?
A seemingly good machine learning model may still be wrong. We’ll show how you can evaluate these issues by assessing metrics of bias vs. variance and precision vs. recall, and present some solutions for such scenarios.
Customer intelligence requires segmenting customers by their company’s properties, such as web traffic, app performance, technology adoption, ad spend, and company size. But how do you identify companies with these properties?