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Also known as: Six Types of Data Taxonomy
A conceptual taxonomy categorizing enterprise data into distinct layers to clarify meaning, stewardship, and usage.
DESCRIPTION
Malcolm Chisholm proposed a six-layer taxonomy to classify enterprise data and reduce ambiguity between data types. By separating categories like identifiers, reference, master, and transactional data, the taxonomy clarifies governance responsibilities and modeling patterns. It helps align stewardship, quality rules, and integration approaches to data semantics. The taxonomy is often used in conjunction with master and reference data programs. Organizations tailor the layers to their domains for practical adoption.
ALTERNATIVE DEFINITIONS
DATAVERSITY (Chisholm articles)
Malcolm Chisholm describes a taxonomy that distinguishes core data types—such as identifiers, reference, master, and transaction data—to assign appropriate governance and integration practices.
Emphasizes governance alignment via clear data type distinctions.
RELATED TERMS
Master Data Reference Data Transactional Data Metadata Unstructured Data Data Classification Semantic Layer Governance
COMPETING TERMS
DIKW Pyramid
RELATIONS
- Broader: Taxonomy
KNOWLEDGE AREAS
Data Management Semantic & Knowledge Modeling Metadata Management Reference & Master Data Data Architecture
TARGET AUDIENCE
Data Management Consultant Data Architect Data Modeler Data Steward Information Analyst
EXTERNAL SOURCES
- Wikipedia - Data classification
- DATAVERSITY - Articles by Malcolm Chisholm
- TDAN - Data governance articles and taxonomies
TOP QUESTIONS
Why use Malcolm Chisholm’s Six-Layer Taxonomy?
It clarifies differences among data types, guiding stewardship, quality rules, and integration patterns for each category.
Category: Relevancy
How does the taxonomy support MDM and reference data?
It distinguishes master from reference data, enabling appropriate governance, versioning, and distribution mechanisms.
Category: Example
Is the six-layer taxonomy a standard?
No, it is a thought-leadership taxonomy used as a practical guide, not a formal standard.
Category: Misunderstanding
How should organizations adopt the taxonomy?
Start by mapping existing datasets to layers, assign owners, and define layer-specific policies and integration methods.
Category: GettingStarted
What is a common mistake with the taxonomy?
Treating categories as rigid instead of a guide, causing misclassification and governance inflexibility.
Category: Mistakes
How does the taxonomy interact with metadata?
Metadata documents classifications and drives enforcement of layer-specific rules in catalogs and pipelines.
Category: Management
How does this taxonomy compare to DIKW?
DIKW addresses data-to-wisdom abstraction; Chisholm’s taxonomy classifies enterprise data types for governance and integration.
Category: Comparison
Can the taxonomy help with data contracts?
Yes, contracts can specify layer-aligned semantics, quality expectations, and versioning policies for exchanged data.
Category: BestPractice
What outcomes indicate successful adoption?
Clear ownership, fewer semantic conflicts, and better reuse of curated data assets across domains.
Category: Measurement