🧭 Overview

Mapping in cofi.ai defines how values from different data sources align with one another — ensuring consistency across dimensions like Department, Region, Account, or Product Line.

Mappings serve as the “translation layer” between disparate datasets.

They allow your workspace to recognize that values like “US,” “United States,” and “U.S.A.” all refer to the same entity.

Proper mapping keeps your data unified, your metrics accurate, and your dashboards consistent — even when data comes from multiple systems.

🧩 What Is Mapping?

Mapping connects and standardizes categorical data across different databases and sources.

It is most commonly used for:

• 🧱 Aligning dimensions across datasets (e.g., Department, Region, Company)
• 🔄 Reconciling variations in naming conventions between systems
• 📊 Ensuring consistency in reporting and filtering across dashboards

In cofi.ai, mapping operates at the dimension level — making it a key part of schema design and multi-source integration.

💡 Mappings act like a universal dictionary that keeps categorical values consistent across your workspace.

⚙️ How Mapping Works

Mappings link source values (as they appear in uploaded data) to standardized workspace values used throughout dashboards and models.

Example: Regional Mapping

Once mapped, all values appear uniformly as “United States” across dashboards and filters — regardless of origin.