Data cleaning is taking longer than model building at this point

This is one of the least glamorous truths in AI work: messy data keeps winning the battle for time. Teams imagine model building as the hard part, then discover that cleaning labels, fixing schema drift, aligning sources, and resolving duplicates consume far more effort than expected.

That does not mean something is broken. It usually means the team is meeting reality. Real business data was not created to make models happy, so it arrives inconsistent, incomplete, and full of hidden assumptions that only show up during implementation.

The smartest response is to treat data cleaning as a core capability instead of an annoying pre-step. Good pipelines, validation checks, ownership rules, and repeatable preprocessing often create more long-term value than yet another model experiment built on unstable inputs.