How Do You Decide When A Messy Dataset Is “good Enough” To Start Modeling?

Lately I’ve been jumping between different public datasets for a side project, and I keep running into the same question: at what point do you stop cleaning and start analyzing?

Some datasets are obviously noisy – duplicated IDs, half-missing columns, weird timestamp formats, etc. My usual workflow is pretty standard: Pandas profiling → a few sanity checks in a notebook → light exploratory visualizations → then I try to build a baseline model or summary. But I’ve noticed a pattern: I often spend way too long chasing “perfect structure” before I actually begin the real work.

I tried changing the process a bit. I started treating the early phase more like a rehearsal. I’d talk through my reasoning out loud, use GPT or Claude to sanity-check assumptions, and occasionally run mock explanations with the Beyz coding assistant to see if my logic held up when spoken. This helped me catch weak spots in my cleaning decisions much faster. But I’m still unsure where other people draw the line.
How do you decide:

• when the cleaning is “good enough”?
• when to switch from preprocessing to actual modeling?
• what level of missingness/noise is acceptable before you discard or rebuild a dataset?

Would love to hear how others approach this, especially for messy real-world datasets where there’s no official schema to lean on. TIA!