🧭 Overview

As data flows into cofi.ai from multiple systems — CRMs, ERPs, HR platforms, and CSV uploads — categories like Department, Region, or Account often appear under different names.

Mapping solves this problem by creating a consistent, shared vocabulary across your workspace.

Mappings act as the translation layer between your data sources and cofi.ai’s unified structure, ensuring that dashboards, filters, and metrics all interpret data the same way — no matter where it comes from.

💡 Example: “US”, “U.S.A.”, and “United States” can all be mapped to a single standardized value — “United States.”

🔆 Why Mapping Matters

Without mappings, even small naming inconsistencies can cause fragmented dashboards or incorrect totals.

Mapping ensures that:

• All sources refer to the same entities.
• Filters and drill-downs return complete, accurate results.
• Data models stay stable when new systems are added.
• Your workspace maintains a single source of truth for categorical data.

⚙️ How Mapping Works

At its simplest, mapping links original source values to standardized workspace values through a mapping table.

Each table defines how raw data from different systems translates into unified categories that cofi.ai uses across databases and dashboards.

Core concept:

Original Value → Mapped Value → Standardized Workspace Dimension

Once applied, these standardized values flow automatically through your data models, metrics, and visualizations — keeping everything aligned.