Rohan Alexander

Data cleaning is a huge pain. If it's done by a coder then it takes a huge amount of their time, but if it's done by someone who can't code then the analysis isn't reproducible and they have to redo their same steps the next time there is a data update. Sift bridges this gap by using Opus 4.7 to check for a bunch of data issues and then proposing changes which the non-technical analyst can approve/decline which removes any black-box concerns. At the end both the cleaned data and the underlying generated Python script can be downloaded so that the same cleaning steps can be run again when the data update and it's easy for a coder to add the changes into a process that can run automatically.