I did a lot of tech work on the infrastructure underlying my analytics over the past weeks. I am putting my notes here so they don’t get lost and maybe help someone. Here are three stories, unrelated to each other.
I often feel the gap between the mainstream Data Science rhetoric and the true business needs is widening. When I hear of Hyperautomation, Edge AI, AutoML, or GANs, I challenge myself to take a leap back, understand our needs better.
Garbage in, garbage out. Analysis of untrusted or poorly understood data will yield incorrect results. Hence the textbook approach is to clean the data first, and only then proceed with data analytics. For instance, in the data lakes, the data
For the Data Puzzle I posted last week, I received about a dozen of thoughtful and highly relevant answers. THANK YOU. I want to primarily thank to Luis Ruiz Santiago, Chetan Waman and anonymous J for comments under the previous