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Metadata definitions
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Table of Contents
1
INTRODUCTION ............................................................................................................................. 4
2
SCOPE ............................................................................................................................................ 4
3
DEFINITIONS .................................................................................................................................. 4
3.1 METADATA MANAGEMENT .................................................................................................... 4
3.1.1 Metadata ...................................................................................................................... 4
3.1.1 Structural metadata ...................................................................................................... 5
3.1.2 Operational metadata................................................................................................... 6
3.1.3 Data element ................................................................................................................ 7
3.1.4 Attribute ........................................................................................................................ 8
3.1.5 Class ............................................................................................................................ 8
3.1.6 Data type ...................................................................................................................... 8
3.1.7 Metadata management ................................................................................................ 8
3.1.8 Metadata repository ..................................................................................................... 8
3.2 MASTER DATA MANAGEMENT .............................................................................................. 8
3.2.1 Master Data .................................................................................................................. 8
3.2.2 Master Data Management ........................................................................................... 9
3.2.3 Master Reference Data ................................................................................................ 9
3.2.4 Master Data Source System ........................................................................................ 9
3.2.5 Reference Data ............................................................................................................ 9
3.2.6 Reference Data Management ...................................................................................... 9
3.3 CONTROLLED TERMINOLOGY, CODE SYSTEMS & VALUE SETS .................................. 10
3.3.1 Concept ...................................................................................................................... 10
3.3.2 Code ........................................................................................................................... 10
3.3.3 Code system .............................................................................................................. 10
3.3.4 Concept definition ...................................................................................................... 10
3.3.5 Concept designation .................................................................................................. 10
3.3.6 Concept domain ......................................................................................................... 10
3.3.7 Concept identifier ....................................................................................................... 10
3.3.8 Concept representation .............................................................................................. 10
3.3.9 Value set .................................................................................................................... 10
3.4 INTEROPERABILITY .............................................................................................................. 10
3.4.1 Interoperability ............................................................................................................ 10
3.4.2 Technical interoperability ........................................................................................... 10
3.4.3 Semantic interoperability ............................................................................................ 10
3.5 DATA AGGREGATION, INTEGRATION ................................................................................ 10
3.5.1 Data pooling ............................................................................................................... 10
3.5.2 Data aggregation........................................................................................................ 10
3.5.3 Data integration .......................................................................................................... 10
4
INPUT (DRAFT MATERIAL THAT CAN BE USED – TO BE DELETED IN FINAL DOCUMENT)11
4.1 METADATA MANAGEMENT .................................................................................................. 11
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4.3
4.4
4.5
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MASTER DATA MANAGEMENT ............................................................................................ 12
CONTROLLED TERMINOLOGY ............................................................................................ 13
INTEROPERABILITY .............................................................................................................. 15
DATA AGGREGATION ........................................................................................................... 16
5
REFERENCES & RELATED DOCUMENTS ................................................................................ 17
6
APPENDICES ............................................................................................................................... 17
6.1 CDISC GLOSSARY ................................................................................................................ 17
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INTRODUCTION: purpose of this document
This document provides agreed definitions around meta-data management and related aspects across
the industry. It is expected that these definitions will be re-used in the FDA guidelines as agreed cross
industry definitions.
To be of operational value, the document contains not only definitions but also a short description and
example of use. Whenever possible, the definitions are built from those existing definitions from FDA
guidance's, CDISC glossary, check cross industry definition (e.g. Gartner). Reference to the source
definition is provided.
This document does not intend to be extensive and complete. It is intended to bring clarification on the
most commonly used (and misused !) definition in our industry around metadata and master data
management;
The CDISC glossary [CDISC1] (and document in attachment) is heavily used as reference in this document;
It is expected that the reader of this document is familiar with the abbreviations and acronyms
contained in the CDISC glossary; these are not repeated here.
2
SCOPE
The following topic areas are in scope of this document
• Metadata management: metadata (structural & operational), data elements, attributes, classes..
• Master data management: Master data, reference data, master reference data
• Controlled terminology, code systems, value sets, permissible values
• Data pooling, data integration, data aggregation
• Interoperability, semantic interoperability
Definitions are provided per topic area to ease reading and structure of this document.
3
DEFINITIONS
3.1
Metadata management
3.1.1
Metadata
Acronym
Definition
source
& Metadata is often described as data about data.
The term metadata –"data about data” - is an ambiguous term which is used for two
fundamentally different concepts (http://en.wikipedia.org/wiki/Metadata ).

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Structural metadata, the design and specification of data structures (e.g. format,
semantic, ..), cannot be “data about data”, because at design time the application
contains no data. In this case the correct description would be "data/information
about the containers of data".
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Descriptive metadata, on the other hand, is about individual instances of
application data, the data content (e.g. patient population for a specific study,
audit trail). In this case, a useful description would be "data about data content" or
"content about content". 
Process metadata (Marcelina To further define with example)

Description
See structural (or descriptive) metadata and operational metadata
Example
See structural metadata and operational metadata
Recommended
definition
3.1.1
Structural metadata
Acronym
Definition & source

http://en.wikipedia.org/wiki/Metadata
The design and specification of data structures (e.g. format, semantic,
..), cannot be “data about data”, because at design time the application
contains no data. In this case the correct description would be
"data/information about the containers of data".

[FDA1]
Structural metadata is structured information that describes, explains,
or otherwise makes it easier to retrieve, use, or manage data.
Description
Structural metadata is what most of people mean by metadata. Structural
metadata is said to “give meaning to data” or to put data “in context.”
Data about a library book such as author, type of book, and the Library of
Congress number, are structural metadata and were once maintained on
index cards. SAS labels and formats are a rudimentary form of structural
metadata, although they have not historically been referred to as metadata.
There are different subtypes of structural metadata with different scope
(global , study, compound, ..)
Example
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
Study instance metadata

Standard metadata
The number 120 itself is meaningless without structural metadata such as
- The name of the variable (e.g. Systolic Blood Pressure) with its
definition
- The unit related to this physical quantity (e.g; Systolic Blood
Pressure Unit = mmHG)
The CDISC SDTM data standard is the structural metadata of all the data
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collected with that standard. For instance the variable “Sex” is described by
a set of structural meta data such as the label, data type (char) and
associated value sets (male and female, ..), role in SDTM, …
A data model - describing the classes, attributes, relationships and
hierarchies – constitutes the structural metadata of the underlying data
base.
Recommended definition
3.1.1.1
Structural metadata: study instance metadata
Acronym
Definition & source
Description
Example
Recommended definition
3.1.1.2
Structural metadata: standards metadata
Acronym
Definition & source
(note from Isabelle – would prefer “enterprise metadata” .. but may be
wrong
Description
Example
Recommended definition
3.1.2
Descriptive/Operational metadata
Acronym
Definition
source
Description
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& 
http://en.wikipedia.org/wiki/Metadata
The individual instances of application data, the data content. In this case, a useful
description would be "data about data content" or "content about content".
Descriptive metadata are also called operational metadata.
It is used in different contexts

Data operations and statistical analysis. Additional content on the data that
support further analysis of the data. For instance patient population in the context
of a clinical trial study is operational metadata

Software implementation: all information needed to support data lineage &
traceability including data on origin, usage… such as
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Different subtype
semantic metadata
Example

Study related metadata: patient population, indication, therapeutic area

Software related metadata:
o What is the source of the data and in which system is it authored
o Who can use a piece of information different roles for access and action
they can perform: who can edit it in which system, who has read access to
it
o Which transformation happen to the data, how and when
o Audit trail: who access which information, when
Recommended
definition
3.1.3
Data element
Acronym
DE
Definition
[FDA1]
A data element is the smallest (or atomic) piece of information that is useful for
analysis (e.g., a systolic blood pressure measurement, a lab test result, a response
to a question on a questionnaire).
[CDISC1]
1. For XML, an item of data provided in a mark-up mode to allow machine
processing. [FDA - GL/IEEE]
2. Smallest unit of information in a transaction. [Center for Advancement of Clinical
Research]
3. A structured item characterized by a stem and response options together with a
history of usage that can be standardized for research purposes across studies
conducted by and for NIH. [NCI, caBIG]
NOTE: The mark up or tagging facilitates document indexing, search and retrieval,
and provides standard conventions for insertion of codes.
[ISO1]
Description
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unit of data for which the definition, identification, representation and permissible
values are specified by means of a set of attributes
A Data Element is the most elementary unit of data that cannot be further subdivided
from a semantic point of view, as it is linked with a precise meaning.
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A data element has:

An identification such as a data element name

A clear definition/ semantic description

A data type

Optional enumerated values (value sets)

One or more representation terms (synonyms)
Synonyms
Example

In the context of SDTM a variable is equivalent to a Data Element

In the context of BRIDG, an attribute is equivalent to a Data Element
Birth Date is a Data Element

DE name: BirthDate

Definition: date and time on which the subject is born

Data type: date (mm/dd/yyyy – hh/mm/ss – time zone)

Value sets: not applicable

Synonyms: BRTDTC in CDISC SDTM, birthdate in BRIDG
Recommended
definition
3.1.4
Attribute
3.1.5
Class
3.1.6
Data type
3.1.7
Metadata management
3.1.8
Metadata repository
3.2
Master data management
3.2.1
Master Data
Acronym
Definition
source
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& [Gartner – Magic Quadrant for Master Data Management of Customer Data Solution]
http://www.gartner.com/technology/reprints.do?id=1-1CK9UDO&ct=121019&st=sb
Master data is the consistent and uniform set of identifiers and extended attributes
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that describes the core entities of the enterprise, such as customers, prospects,
citizens, suppliers, sites, hierarchies and chart of accounts.

Description
o
Example
Recommended
definition
3.2.2
Master Data Management
Acronym
Definition
source
& [Gartner – Magic Quadrant for Master Data Management of Customer Data Solution]
http://www.gartner.com/technology/reprints.do?id=1-1CK9UDO&ct=121019&st=sb
MDM is a technology-enabled discipline in which business and IT work together to
ensure the uniformity, accuracy, stewardship, semantic consistency and accountability
of the enterprise's official, shared master data assets.
Description

o
Example
Recommended
definition
3.2.3
Master Reference Data
3.2.4
Master Data Source System
3.2.5
Reference Data
3.2.6
Reference Data Management
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3.3
Controlled Terminology, code systems & value sets
3.3.1
Concept
3.3.2
Code
3.3.3
Code system
3.3.4
Concept definition
3.3.5
Concept designation
3.3.6
Concept domain
3.3.7
Concept identifier
3.3.8
Concept representation
3.3.9
Value set
3.4
Date: 17th May 2013
Interoperability
3.4.1
Interoperability
3.4.2
Technical interoperability (“machine interoperability”)
3.4.3
Semantic interoperability
3.5
Working Group:
Emerging Technologies
Data aggregation, integration
3.5.1
Data pooling
3.5.2
Data aggregation
3.5.3
Data integration
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INPUT (draft material that can be used – to be deleted in final document)
4.1
Metadata management
Term
Acronym
Definition
attribute
Description of a property of an object. An attribute may be further described as a data element stored in a
metadata repository and in implementation, becomes one or more variables.
For example: in BRIDG, raceCode is an attribute of class Person (i.e. Person.raceCode), and value is an
attribute of DefinedObservationResult.
class
Set of Data Elements describing a logical “thing”
A class has:
• An identifier such as an class name
• A clear object definition / semantic description
• One or more representation terms
• A list of DE (also known as attributes)
• A list of related classes and a description of the relationship type(s)• Any description – in addition to DE –
that allow to map the object with an application vertical
Data Type
A data type is a classification identifying one of various types of data, such as real-valued, integer or
Boolean, that determines the possible values for that type; the operations that can be done on values of
that type; the meaning of the data; and the way values of that type can be stored.
Metadata
Management
MEM
Meta Data MDR
Repository
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Metadata Management is a worldwide infrastructure composed of policies, procedures, standards, models,
skills, tools and training needed to promote the shareability of data throughout the enterprise and to our
customers.
Repository composed of Descriptive Meta Data.
Within the clinical research world, there is around 30.000 to 50.000 different data elements covering all
potential data that can be collected for a patient.
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Master data management
Term
Acronym
Definition
Master Data
Master Data is business data that has a consistent meaning and definition to ne shared across systems; this
applies particularly to data such as site identification, investigator identification, and study identification. It
is produced into a “master system” as part of a transaction and is used for reference and validation in
transactions within other systems.
Master Data – as any other data – are defined with structural Meta data
Master Data MDM
Management
Master Data Management comprises a set of processes and tools that consistently defines and manages the
non-transactional data entities of an enterprise which is fundamental to the company’s business operations
(may include reference data). Master Data Management has the objective of providing processes for
collecting, aggregating, matching, consolidating, quality-assuring, persisting and distributing such data
throughout the enterprise to ensure consistency and control in the ongoing maintenance and application
use of this data. This is sometimes known as Reference Data Management.
Master
Reference
Data
A combination of Master Data and Reference Data. The governance of these 2 components is however quite
different:

reference data are often defined by external organizations and are defined at design time; they are
generally managed within a terminology server (or a meta data repository) as part of all the code
systems

master data are created during application run time through a transaction and are stored into the
source system considered as the source of truth.
Master Data
Source
System
Master Data Source System is the application that houses a master data “dimension” (or type of master data
such as site or investigator) for Perceptive Informatics. The system is available to all applications
(operational and information provisioning, including the Data Warehouse) across the enterprise.
Reference
Data
In context of Master Reference Data Management this corresponds to the set of code systems that are
commonly used across many different systems and attributes
Reference
Data
Management
Management of Reference Data
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Controlled terminology
Term
Acronym
Metadata Management
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Date: 22 April 2013
Definition
Concept
A concept is a “unit of thought” within a particular domain – a unitary or atomic mental representation of a
real or abstract thing
Concepts, as abstract, language- and context-independent representations of meaning, are important for
the design and interpretation of static information models. They constitute the smallest semantic entities1
with which models are built. The authors and the readers of an information model use concepts and their
relationships to build and understand the models.
code
Code’ is the machine-processable part of a Concept Representation, published by the author of a code
system as part of the code system.
It is the preferred unique machine-readable identifier for that concept in that code system and is used in the
'code' property of an ISO 21090 CD data type.
Codes are sometimes meaningless identifiers, and sometimes they are mnemonics that imply the
represented concept to a human reader; meaningless identifiers are advised particularly in larger vocabulary
systems
Code system
A Code System is a managed collection of concept representations, including codes and/or designations (or
human readable text/decode), but sometimes with more complex sets of rules, references (definitions), and
relationships.
Although things may be differentially referred to as terminologies, vocabularies, or coding schemes, or even
classifications, the ISO 21090 CD datatype considers all such collections ‘code systems’.
A code system is typically created for a particular purpose; they may consist of finite collections, such as
concepts that represent individual countries, colours, or states, or they may represent broad and complex
collections of concepts across a particular domain, e.g., SNOMED-CT, ICD, LOINC, and CPT. A code system
should be uniquely identifiable; for ISO 21090conformant uses, this identifier shall take the form of an ISO
OID.
1
As models are layered and developed, the size and description of the smallest semantic entity may change, to best meet the use case(s) and requirements, and to
show different views on reality
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Concept
definition
A concept definition is the explanation of the meaning of the concept. The concept definition may be
provided wholly by the concept designation, with or without additional text etc. (see concept
representation), but particularly in large code systems that employ description logic or similar ontological
functionality, the full definition of the concept may require knowledge of its relationship to other concepts
within the code system.
Concept
designation
A concept designation is a language symbol for a concept that is intended to convey the concept meaning to
a human being. A concept designation may also be known as an appellation, symbol, or term, this latter
being
the
most
common
synonym.
A concept designation is typically used to populate the 'displayName' property of an ISO 21090 CD data
type.
Concept
domain
A concept domain is a sentence or paragraph that defines the semantic space (the totality of meaning that
can be expressed by the concepts that can be used) for the “thing" that a coded attribute in an information
model
is
to
encompass,
plus
examples
of
these
“things”.
For example: an information model class is “car” and the coded attribute is “manufacturer”; the concept
domain is “The company that makes/markets the car to the general public; examples include General
Motors, Ford Motor Company and Mercedes-Benz”.
Concept
identifier
A concept identifier is a vocabulary object that unambiguously and globally uniquely represents a concept
within
the
context
of
a
code
system
in
a
machine
readable
way.
A concept identifier consists of: cthe OID for Code System + Code (+ Designation/Display name).
To make a Concept Identifier human readable, the “display name” (the designation) is added thus: the OID
for Code System + Code (+ Designation/Display name). The designation (display name) is not mandatory in
the ISO 21090 concept identifier, but it is considered good terminology practice to always have the
designation for safety reasons (data unscrambling etc.)2.
Concept
representati
on
A concept representation is a vocabulary object that enables the description and manipulation of a concept
in
systems
and
applications
(such
as
information
models,
xml
schema).
A concept representation is minimally formed by putting together a code and a designation. However, a
concept representation in a code system may also be augmented with additional text, annotations,
2
Debate as to whether the display name should be carried in a concept identifier continues. There are a significant group who feel that the display name should
not be carried.
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references and other resources that serve to further identify and clarify what the concept is.
Value set
4.4
A value set is a uniquely identifiable set of valid concept identifiers that instantiate a concept domain in use
(in an application, an xml instance etc.) where any concept identifier used can be tested to determine
whether it is a member of the value set at a specific point in time.
Value sets exist to instantiate the permissible content of a concept domain for a particular use in an
information model vocabulary binding, in analysis, in UI data collection - in a pick list (drop-down box), etc.
A value set is useful only in the context of instantiation of an attribute in an information model, not as a
stand-alone object (this is in contrast to a code system, which exists in its own right).
Interoperability
Term
Semantic
Interoperabil
ity
Acronym
Definition
FDA guidance
“Interoperability” means the ability to communicate and exchange data accurately, effectively,
securely, and consistently with different information technology systems, software applications, and
networks in various settings, and exchange data such that clinical or operational purpose and meaning
of the data are preserved and unaltered.
Technical interoperability describes the lowest level of interoperability whereby two different systems
or organizations exchange data so that the data are useful. There is nothing that defines how useful.
The focus of technical interoperability is on the conveyance of data, not on its meaning. Technical
interoperability supports the exchange of information that can be used by a person but not necessarily
processed further. When applied to study data, a simple exchange of nonstandardized data using an
agreed-upon file format for data exchange (e.g., SAS transport file) is an example of technical
interoperability.
Semantic interoperability describes the ability of information shared by systems to be understood, so
that nonnumeric data can be processed by the receiving system. Semantic interoperability is a multi-
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level concept with the degree of semantic interoperability dependent on the level of agreement on
data content terminology and other factors. With greater degrees of semantic interoperability, less
human manual processing is required, thereby decreasing errors and inefficiencies in data analysis. The
use of controlled terminologies and consistently defined metadata support semantic interoperability.
Process interoperability is an emerging concept that has been identified as a requirement for
successful system implementation into actual work settings. Simply put, it involves the ability of a
system to provide the right data to the right entity at the right point in a business process.
4.5
data aggregation
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REFERENCES & RELATED DOCUMENTs
Related Documents
Reference
No.
Document Name
Filename
[FDA1]
Guidance for Industry. Providing Regulatory
Submissions in Electronic Format — Standardized
Study Data - DRAFT GUIDANCE . February 2012
http://www.fda.gov/downloads/Drugs/Guid
ances/UCM292334.pdf
[CDISC1]
CDISC Glossary - 2009
http://www.cdisc.org/stuff/contentmgr/file
s/0/08a36984bc61034baed3b019f3a87139/
misc/act1211_011_043_gr_glossary.pdf
[ISO1]
ISO1179 ISO/IEC 11179 Metadata Registry (MDR)
standard
Accessible on ISO site
[ISO2]
ISO2109
ISO 21090 Healthcare Data Type Standard
Accessible on ISO site (draft version
available on Internet)
Status
Name
Company
Date
Signature
Author
Author
Author
Author
6
6.1
Appendices
CDISC glossary
cdisc_glossaryterms_
version7.1_final_2008.doc
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