A Data Product is no Repackaged Shit

Take bad data. Put it in a pipeline. Call it a “Data Product.”

Congratulations. You’ve automated garbage.

This is what happens when organizations treat data products only as a packaging exercise

The box looks the same. The label says “Data Product.” But what’s inside couldn’t be more different.

Left side: raw, unvalidated data shoved into a box. It leaks. It stinks. Nobody trusts it.

Right side: same starting point. But it passed through a quality filter first. What comes out is clean, validated, meta-data labeled and usable.

The difference isn’t the box. It’s what happens before the data enters it.

A real data product requires:

• Quality checks before anything gets packaged
• Clear ownership of the data inside
• Standards that define what “good enough” means
• Feedback loops from the people actually using it

Without that, you’re not building data products. You’re gift-wrapping problems.

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