🧭 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.

Furthermore, this mapping capability extends to allow you to create and define hierarchical structures based on your company’s needs. You can not only 'translate' values (such as "Ventas" and "Sales" into a single term), but also group specific values under higher-level categories.

This gives you the power to model your data exactly as desired, allowing the same information to be visualized under different structures depending on the context or business objective being analyzed.

🧩 Dimension Mapping Alternatives

✅ Column Mapping

Acts as a translation layer to ensure that different names for the same thing are recognized as a single entity.

• What it does: It works to change synonyms, spelling variations, or different naming conventions across systems and converts them into one "official" label.

As an example: Your systems are referring to the same department in three different ways:

• HR System: "HR & Admin"
• Finance System: "Human Resource"
• Payroll System: "People Ops"

The Action: Column Mapping takes these three distinct values and maps them all to "Human Resources." Now, the data is clean and uniform.

Example: Regional Mapping

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