Data Sources

Overview

Data sources provide reference inputs that improve scope quality and reporting consistency.

A clear source-of-truth model reduces conflicting updates across systems.

Key Concepts

• Source of truth: system responsible for the authoritative value
• Reference dataset: upstream data used for lookups, identifiers, or classifications
• Mapping: alignment from source fields to Transform fields

How It’s Used

Use data sources to seed Projects with consistent Systems, Processes, and reference lists.

Use them to keep identifiers consistent for cross-system reporting.

Benefits

• Reduced duplicate and inconsistent records
• Cleaner reporting through stable identifiers
• Faster onboarding through reusable reference datasets

Setup Steps

1

Define the source-of-truth model

Define which system owns:

• identifiers
• ownership and teams
• status signals
• reference lists, such as Systems and classifications

2

Map into Transform concepts

Map source data into:

• Systems and System Types
• Processes and Process Hierarchy
• Segment Types and Segments (Organization scope)

3

Validate data quality

1. Confirm required fields are populated.
2. Confirm naming conventions match governance standards.
3. Create Save Views that surface missing or inconsistent values for cleanup.

Best Practices

• Keep mappings documented and versioned.
• Avoid field proliferation without reporting value.
• Standardize Segment Types to protect analytics continuity.

Summary

Data sources are most valuable when ownership is explicit and mappings are stable.

Related:

• Connectors
• Blueprints