Writing · Essay
Models are only as good as their inputs
Garbage in, garbage out is the oldest cliché in computing, and it has never been more relevant. Larger models are better at sounding confident, which makes bad inputs more dangerous rather than less.
In most projects I have worked on, the modelling was perhaps a fifth of the effort. The rest went into definitions, reconciliation and plumbing: agreeing what counts as an active customer, finding why two systems disagree on a balance, tracing a field that silently changed meaning after a migration.
A few habits pay off every time. Write a metric dictionary before building the dashboard. Put automated checks on volumes, nulls and ranges at every hop of the pipeline. Assign a named owner to each critical dataset. Keep a small set of known-answer test cases and run them on every refresh. None of this is glamorous, and all of it is cheaper than explaining a wrong number to leadership.
If you want better AI, start by making your data boring: consistent, documented and trusted.
If you want better AI, start by making your data boring: consistent, documented and trusted.
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Attention is not the same as value