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In this tutorial, you’ll use Rosetta Stone to normalize your data. Normalization maps your unique schema to Narrative’s standard format, enabling queries across datasets from different sources.
This tutorial is part of the First Steps series. Complete Upload Your Data before starting.

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Prerequisites

  • A Narrative I/O account
  • At least one uploaded dataset (from the previous tutorial)

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Steps

1

Confirm your workspace

Open the workspace menu to confirm you’re normalizing data in the right account.
2

Go to Normalized Datasets

Navigate to Normalized Datasets to manage the standardized datasets used across your workflows.
3

Select your dataset

Select the dataset you want to review and improve its attribute mappings.
4

Switch to the Normalize tab

Go to the Normalize tab to generate and apply suggested column-to-attribute mappings.
5

Start the analysis

Click Start analysis to get AI-suggested normalization strategies for your fields.
6

Review suggested expressions

Open expression mappings to verify how your data will be transformed. For example, address parts might be combined into a postal code, or values like “M” might be standardized to “male”.
7

Accept individual mappings

Click Accept to apply mappings that look correct. This ensures downstream workflows work from consistent fields.
8

Review high-confidence mappings

Use View to quickly review all high-confidence mappings before applying them at scale.
9

Accept all high-confidence mappings

Click Accept all high confidence to apply the best matches and save manual review time.

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Next steps

Write Your First Query

Query your normalized data with NQL

How Rosetta Stone Works

Understand the normalization model in depth

Mapping Schemas Guide

Advanced mapping techniques and API usage

Edge Cases

Handle complex mapping scenarios