Why Analysts Ship Imperfect Data — and How They Keep It Credible
Data quality, according to decades-old research, is not an absolute property but a measure of fitness for a specific use — meaning the same dataset can be reliable for one purpose and unsuitable for another. Professional analysts acknowledge this by documenting every known flaw, deliberate tradeoff, and boundary condition rather than waiting for data that is never fully clean. Real-world projects, such as analyses of Billboard chart history and streaming platforms, include dedicated sections outlining excluded records, unresolved issues, and the limitations of cleaning decisions. For example, duo artist names containing '&' were intentionally left unsplit to avoid creating fictitious solo entries, with the known cost clearly recorded. This practice of transparent, defensible documentation — published as 'Limitations,' 'Caveats,' or 'Scope & Assumptions' — is what keeps analytical conclusions credible and prevents findings from being stretched beyond what the data can actually support.