Master Data

Master data is a core concept of traceability. The frameworks described in this knowledge base build on top of the EPCIS 2.0 definitions for this concept.

Master data refers to data that changes less frequently than event data or remains static across supply chain events. Master data is further categorized into static master data and instance lot master data (ILMD).

Understanding Master Data

Master data is the essential set of information used to define and describe products, batches, locations, or entities within a traceability system. These are stable, low-volatility data points that act as the foundation for interpreting any operational record.

While events in the system represent actions, such as production, movement, processing, or inspections, master data provides the context needed to understand what is being tracked and how each component should be interpreted.

This includes general product characteristics, batch-specific details, location attributes, and other information that remains consistent over time. Without this structured foundation, operational records become ambiguous, making analysis and end-to-end traceability significantly more difficult.

Types and Categories of Master Data

In a traceability system, master data is structured to organize stable information about products, batches, locations, and entities. It is divided into two main types, which define how these details apply within operations flows:

Static Master Data

This includes information that remains the same for all units of a product. These are general attributes, such as name, dimensions, species, category, composition, or packing type, which serve as the foundational reference for any process involving that item. This data is usually registered once and reused throughout the system.

Instance/Lot Master Data (ILMD)

This type consists of information specific to a particular batch or individual instance. It covers attributes that change across productions, such as lot number, manufacturing date, expiration date, origin, catch area, net weight, or processing conditions. ILMD enriches operational records with detailed context.

Beyond these two types, master data can be applied across multiple information categories, depending on what needs to be described within the monitored chain:

• Products: General and technical characteristics of the item.
• Batches/Instances: Variable attributes tied to each production run.
• Locations: Details about where activities take place (farms, warehouses, processing units, etc.).
• Parties: Organizations, suppliers, operators, or carriers.
• Certifications: Labels, authorizations, production standards, or compliance data.

This structure enables the framework to clearly represent what an item is, who is involved, where activities occur, and which specific characteristics apply to each batch.

How Master Data Is Used in the Traceability Framework

In a traceability framework, master data serves as the reference layer that gives meaning and consistency to all operational records. It ensures that every event, movement, or update is interpreted correctly, regardless of the source system or data origin.

There are three primary ways it is used:

1. Event Enrichment: Every action in the system, such as production, shipping, receiving, transformation, or inspection, is contextualized with information derived from master data. This prevents repetitive structural data in each event and keeps the overall flow lighter and more standardized.
2. Consistency Across Processes: Because master data acts as the single source of truth for products, batches, and locations, it prevents discrepancies when multiple modules or services interact with the same information. This ensures that all parts of the chain rely on the same reference set, reducing errors and inconsistencies.
3. Querying and Validation: The system relies on master data to validate inputs, interpret codes, verify attributes, understand batch characteristics, and ensure that operational records align with pre-defined information. This strengthens data integrity and simplifies audits.

By centralizing and structuring these stable attributes, master data enables the framework to operate efficiently, providing clarity, reliability, and interoperability throughout the entire monitored chain.

Common Examples of Master Data

Master data covers different types of information used to describe products, batches, locations, and entities involved in a monitored supply chain. Below are typical examples found in traceability frameworks:

Examples of Static Master Data:

• Product Name: "Salmon Fillet", "Roasted Coffee Beans".
• Physical Specifications: Dimensions, gross weight, volume, capacity.
• Classification or Category: Food type, species, regulatory category.
• General Technical Information: Composition, ingredients, packing type, brand.

These attributes remain the same across all units and batches of the product.

Examples of Instance/Lot Master Data (ILMD)

• Production Date
• Expiration Date

These attributes vary by batch and help enrich and contextualize operational records.

Example Table of Master Data Components

Product

Batch / Instance (ILMD)

Location

Entity / Party

Certification

Quality Attributes

Key Rules

To ensure consistency and reliability across the traceability framework, the use of master data must follow a set of essential guidelines:

1. Static and ILMD must not overlap: General product attributes belong in Static Master Data, while attributes that vary by batch or instance belong in ILMD. Mixing these layers leads to duplication and inconsistency.
2. ILMD should only contain variable attributes: Any information that can change with each production run, such as net weight, origin, expiration date, or processing conditions, must be stored exclusively as ILMD to prevent discrepancies between batches.
3. All operational records must reference valid Master data: Events, movements, or transactions that involve products, batches, locations, or entities must be linked to existing master data entries to ensure integrity and avoid orphan records.
4. Master data must act as the single source of truth: When different services or modules access the same information, they must consult master data directly rather than duplicating fields locally. This minimizes errors and supports auditability.
5. Updates must be traceable or versioned: Because master data impacts the entire system, changes should be documented with history or version control. This is crucial for compliance reviews and investigations.
6. Categories must be applied consistently: Product, batch, location, party, and certification data should follow the same structural and naming conventions. Consistency makes integration and automation more reliable.
7. Attributes must follow internal data standards: Units of measure, date formats, code models, and classifications must adhere to the framework´s internal standards to avoid ambiguity or misinterpretation between systems ot teams.