> ## Documentation Index
> Fetch the complete documentation index at: https://docs.narrative.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Normalize with Rosetta Stone
> Map your data to a standard schema for seamless collaboration
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](/getting-started/upload-data) before starting.
## Prerequisites
* A Narrative I/O account
* At least one uploaded dataset (from the previous tutorial)
## Steps
Open the workspace menu to confirm you're normalizing data in the right account.
Navigate to **Normalized Datasets** to manage the standardized datasets used across your workflows.
Select the dataset you want to review and improve its attribute mappings.
Go to the **Normalize** tab to generate and apply suggested column-to-attribute mappings.
Click **Start analysis** to get AI-suggested normalization strategies for your fields.
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".
Click **Accept** to apply mappings that look correct. This ensures downstream workflows work from consistent fields.
Use **View** to quickly review all high-confidence mappings before applying them at scale.
Click **Accept all high confidence** to apply the best matches and save manual review time.
## Next steps
Query your normalized data with NQL
Understand the normalization model in depth
Advanced mapping techniques and API usage
Handle complex mapping scenarios