3 Types of Data Normalization

Messy data comes in three kinds: how it’s written, who it’s about, and what it means. This explainer shows how the Rosetta Stone Normalization Engine handles all three, so data teams can standardize inconsistent fields across sources without paying AI to re-answer the same question.

Why one rule isn’t enough

You’d think every messy field just needs a rule. But data gets messy in more ways than you can count. Here are three: how it’s written, who it’s about, and what it means.

How it’s written

How it’s written just takes a rule. Month first or day first, a date comes out one way. Capitals or not, an email comes out as the same hash. Every time.

Who it’s about

Who it’s about takes more than a rule, because no record can tell you on its own. Identity Orchestrator links the records that share an email or a phone, and your guest turns out to be someone you already know.

What it means

And then there’s what it means: product names, job titles, anything people type. Your receipts spell the same cola a hundred different ways, and they all mean soft drinks.

Rules can’t keep up. Send them all to AI? You’ll pay for every row, every run. And ask twice, you might get two answers.

Normalizing in steps

Rosetta Stone gives every spelling one of your standard values, in steps. Rules take the easy ones. A classifier trained on your own examples takes what it knows. AI gets only what’s left. When no step is sure, Rosetta Stone says so instead of guessing.

Decided once, remembered

Each spelling is decided once, and remembered. Next time, the same answer is already there, and you only pay for the new ones.

However it’s written, whoever it’s about, whatever it means, your data comes back in one standard.

‍Talk to our team to learn more.

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