Master Data vs Datapoints

All data that flows through the YuzeData platform falls into one of two categories: master data or datapoints. Every workflow, connector, and schema you configure ultimately works with one or both of these data types. Understanding the difference between them is essential for designing your integrations.

Master Data (Reference Data)

Master data represents the entities in your organization — the things that exist and are referenced across systems.

• Stable, long-lived entities: Equipment, locations, products, suppliers
• Centrally managed: Single source of truth for organizational entities
• Reusable: Referenced across many workflows and systems
• Structured with schemas: Well-defined types and fields
• Mutable: Updated over time with version tracking
• Example: A piece of equipment, a building, a supplier

Learn more: Master Data

Datapoints (Runtime Data)

Datapoints represent the events and measurements that occur over time — the facts your systems generate.

• Transient, event-based data: Sensor readings, transactions, logs
• Workflow-generated: Created during workflow execution
• High volume: Potentially thousands per day
• Time-series nature: Captures events and measurements over time
• Immutable: Once created, datapoints are not modified
• Example: A temperature reading, an API response, a calculation result

Learn more: Datapoints

Side by Side

	Master Data	Datapoints
Represents	Things (entities)	Facts (events, measurements)
Lifecycle	Long-lived, updated over time	Created once, never modified
Volume	Hundreds to thousands of items	Thousands to millions of records
Change tracking	Version-based (incremental sync)	Position-based (checkpoint tracking)
Managed by	Users and import workflows	Workflow execution
Typical sources	ERP, CRM, asset management systems	IoT platforms, APIs, sensors, logs

How They Work Together

In a typical integration, master data and datapoints complement each other:

1. Master data provides context — the equipment, locations, and organizational structure
2. Datapoints capture what happens — readings, events, and transactions linked to that context
3. Workflows connect the two — enriching datapoints with master data references, or using datapoints to trigger updates to external systems

For example, a sensor reading (datapoint) might reference a specific piece of equipment (master data) and be routed to the correct external system based on the equipment's location (master data).

Rule of Thumb

If it represents a "thing" in your organization (equipment, location, product), use master data. If it represents a "fact" or "event" (reading, transaction, log), use a datapoint.