Built In: 9 KEYS TO SUCCESSFUL DATA MAPPING

Dimitri Sirota: [Taxonomies] created a huge burden on people who don’t necessarily know the data. They’re saying, “Well, we’re gonna start with the taxonomy and try to lump everything into it.” What if everything doesn’t naturally fit within these guardrails?

We’re an exponent of, and there are others, saying: “Why start with some mythical, almost Olympian model: Here are all the gods, and they have to fit into this? [Instead, I’m] combining the God of wine and the God of war because I didn’t create a separation in the beginning.” Let’s start with the reality, the messy complexity of the data.

If you can know your data up front, let machines recommend what those data definitions should be. Also, reconcile places where you see collisions or potential discrepancies — m.mail is not the same as email, and so forth.

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