📌 Overview

Mappings for Standardization in cofi.ai ensure that data from different sources speaks the same language.

When systems label the same entity differently — such as “U.S.”, “United States”, and “America” — mappings unify them into one consistent value.

This standardization is essential for accurate metrics, reliable drill-downs, and clear dashboards across your workspace.

🧱 Why Standardization Matters

Without mapping, inconsistent naming leads to fragmented data and reporting errors.

For example, totals may appear under multiple labels, or filters may not apply consistently across sources.

Standardization ensures that:

• All dashboards display unified categories.
• Filters return complete and accurate results.
• Cross-database joins recognize equivalent values.
• Dimensions remain consistent across time and systems.

💡 Think of mapping as your organization’s data dictionary — a single source of truth for categorical data.

📏 How Mappings Standardize Data

Mappings standardize categorical fields (dimensions) by replacing variable source values with a consistent workspace-defined value.

Process:

1. Identify differences – find inconsistent values across data sources.
2. Create a mapping table – define Original Value → Standardized Value.