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.”
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.
Mapping connects and standardizes categorical data across different databases and sources.
It is most commonly used for:
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 dictionary that keeps categorical values and data structures consistent across your workspace.
Without mappings, even small naming inconsistencies can cause fragmented dashboards or incorrect totals.
Mapping ensures that: