Which Approach to Choose: Mapping vs. Other Alignment Methods

📌 Overview

In cofi.ai, there are several ways to align data across systems — Mapping, Database Joins, and Relationships.

Each serves a different purpose depending on your data structure and how consistently your dimensions are named.

This article helps you choose the right approach for your situation — balancing flexibility, accuracy, and ongoing maintenance needs.

Approach	Primary Use	Description
Mapping	Standardizing categorical values	Translates similar values (e.g., “US”, “U.S.A.”, “America”) into one consistent label.
Database Join	Combining data from different tables	Links related datasets through a common key (e.g., Account_ID, Company_ID).
Relationship	Structuring data hierarchically	Connects one-to-many or parent-child datasets (e.g., Department → Sub-department).

💡 Tip: Mapping focuses on data consistency (naming), while Joins and Relationships handle data connectivity (structure).

You have naming inconsistencies between systems (e.g., “HR” vs. “Human Resources”).
Data uses non-standardized text fields for dimensions.
You need categorical alignment across CSVs, connectors, or manual uploads.
You want to clean and unify data without altering original source values.

Example: Unifying “U.S.”, “United States”, and “America” under “United States.”

Two databases share a common identifier (e.g., Company_ID, Account_Code).
You want to combine related metrics or dimensions from different tables.