meta data management · web viewmaster data management has the objective of providing processes for...

Project: Metadata ManagementTitle: Metadata Definitions Working Group:

Emerging TechnologiesVersion: 0.32 Date: 197th May June 2013

PhUse Emerging Technology Working Group

Metadata definitions

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http://www.phuse.eu/splash.aspx




Table of Contents1 INTRODUCTION: PURPOSE OF THIS DOCUMENT....................................................................42 SCOPE........................................................................................................................................... 43 DEFINITIONS................................................................................................................................. 4

3.1 METADATA MANAGEMENT....................................................................................................43.1.1 Metadata..................................................................................................................... 43.1.1 Structural metadata.....................................................................................................53.1.2 Descriptive metadata...................................................................................................73.1.3 Study-Instance Metadata or Study specific metadata..................................................83.1.1 Metadata repository.....................................................................................................93.1.2 Metadata registry.........................................................................................................93.1.3 Data element...............................................................................................................93.1.4 Attribute.....................................................................................................................113.1.5 Class.......................................................................................................................... 113.1.6 Data type................................................................................................................... 11

3.2 MASTER DATA MANAGEMENT............................................................................................113.2.1 Master Data...............................................................................................................113.2.2 Master Data Management.........................................................................................113.2.3 Master Reference Data.............................................................................................123.2.4 Master Data Source System......................................................................................123.2.5 Reference Data.........................................................................................................123.2.6 Reference Data Management...................................................................................12

3.3 CONTROLLED TERMINOLOGY, CODE SYSTEMS & VALUE SETS...................................123.3.1 Concept.....................................................................................................................123.3.2 Code.......................................................................................................................... 133.3.3 Code system.............................................................................................................133.3.4 Concept definition......................................................................................................133.3.5 Concept designation..................................................................................................133.3.6 Concept domain........................................................................................................133.3.7 Concept identifier.......................................................................................................133.3.8 Concept representation.............................................................................................133.3.9 Value set.................................................................................................................... 13

3.4 INTEROPERABILITY.............................................................................................................133.4.1 Interoperability...........................................................................................................143.4.2 Technical interoperability (“machine interoperability”)...............................................153.4.3 Semantic interoperability...........................................................................................153.4.4 Process Interoperability.............................................................................................16

3.5 DATA AGGREGATION, INTEGRATION.................................................................................173.5.1 Data pooling..............................................................................................................173.5.2 Data aggregation.......................................................................................................173.5.3 Data integration.........................................................................................................17

4 INPUT (DRAFT MATERIAL THAT CAN BE USED – TO BE DELETED IN FINAL DOCUMENT)18

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4.1 METADATA MANAGEMENT..................................................................................................184.2 MASTER DATA MANAGEMENT............................................................................................194.3 CONTROLLED TERMINOLOGY...........................................................................................204.4 INTEROPERABILITY.............................................................................................................224.5 DATA AGGREGATION...........................................................................................................23

5 REFERENCES & RELATED DOCUMENTS................................................................................246 APPENDICES.............................................................................................................................. 24

6.1 CDISC GLOSSARY...............................................................................................................24

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1 INTRODUCTION: purpose of this documentThis document provides agreed definitions within the PhUse CSS working group 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 either directly with the definition or in the reference section. .

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 Synonyms contained in the CDISC glossary; these are not repeated here.

2 SCOPEThe 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 terminologyy, 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 DEFINITIONS3.1 Metadata management

3.1.1 Metadata

SynonymDefinition & source

Wikipedia . The term metadata refers to "data about data". The term is ambiguous, as it is used for two fundamentally different concepts (types).

o Structural metadata is about the design and specification of data structures and is more properly called "data about the containers of data";

o Descriptive metadata, on the other hand, is about individual instances of application data, the data content. In this case, a useful description

ISO 11179. “Descriptive data about an object [ISO/IEC 20944-1]”. Thus, metadata

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is a kind of data.

Adrienne Tannenbaum, Metadata Solutions : "Metadata: The descriptive details of metadata; metadata qualities and locations that allow tool-based processing and access; the basic attributes of metadata solutionsthe detailed description of the instance data; the format and characteristics of populated instance data; instances and values depending on the role of the metadata recipient." and "Instance data: That which is input into a receiving tool, application, database, or simple processing engine".

Description Metadata describe instance data. Instance data are data stored in a computer as the result of data entry by a

person or data processing by an application.

A metadata can become an instance data described itself by a level 2 metadata (or meta metadata) As an example Marcelina ??

1) Each CDISC standard or instance of a standard defined could be considered an object in a metadata repository. That object will have properties that describe the operations that can be performed on it and by whom; i.e, Global SDTM objects -standard template definitions for SDTM standard domains for each version of the standard- can be copied and a few properties adjusted (instantiated at a compound level or study level to force the inclusion of PERM variables and define some of them them or some EXP variables as Mandatory). The available "Copy" operation and the available "properties that can be changed" and associated "values permitted to change (from x to y)" are metadata elements to be used by the corresponding MDR processing tool to instantiate that object.Metadata needed for the effective management of version control for structural metadata. In the context of CDISC standards – also considered as structural metadata in clinical research – we need metadata to control version: who modified, what when ..

2) The relationships among standards can be considered meta-metadata so that "conversion" or "visualization" tools can relate data elements as they move from one instance of data to other data instance of the data.

There are 2 types of metadata (see below for more details description and examples) Structural metadata Descriptive metadata

Example See structural metadata and descriptive metadataRecommended definition

See structural metadata and descriptive metadata

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3.1.1 Structural metadata

Synonym Standard metadataData StandardI would not say that "Standard metadata" and "Data Standard" are synonyms of "Structural metadata". Instead, I would say those terms are included in "Structural metadata".

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.

Octagon. Standards metadata is the metadata that is defined, maintained, and governed as the standard description of the data that will facilitate clinical software re-use and thus process efficiency. It is metadata that describes the standard, not a study built per the standard. Both industry standards such as CDISC and sponsor-defined standards are commonly thought of standards metadata.

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.”Structural metadata, or standards metadata, is the source from which the sStudy instancespecific metadata (see below) is built. Key components of standards structural metadata often include data domains, data elements, terminology, data mappings and transformations, and data derivations.The successful usage of standards structural metadata requires sufficient data standards governance that should include: workflows to address the creation of and/or revision of the standards version control of standards structural metadata and study specific instance

metadata access control to the metadata, by user role

Example 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)

CDISC SDTM is the structural metadata – or data standard - approved across the industry for clinical data to be transferred to the FDA. 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

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http://en.wikipedia.org/wiki/Metadata




SDTM, … The metadata for the AE (Adverse Event) SDTM domain that is compliant with the

CDISC SDTM Implementation Guide (Version 3.1.3) consists of attributes such as Variable Name, Variable Label, Type, Controlled Terms, Role, etc.

A data model - describing the classes, attributes, relationships and hierarchies – constitutes the structural metadata of the underlying data base.

Recommended definition

Structural metadata is the metadata that is defined, maintained, and governed as the level of an organisation (pharma company, CRO, CDISC, ..). In our world, describes the data to be collected and derived during clinical research across different processes and systems. As such they facilitate clinical software re-use and thus process efficiency.The scope of structural metadata the complete organisation across all projects, at the study level, it is the study instance metadata - extracted from the structural metadata – which is of application.

3.1.2

[3.1.3] Descriptive metadata

Synonym Process metadata (subset more than synonym)Semantic metadata (subset more than synonym)

M. Hungria (2013-06-18): After reviewing again the main Wiki categorization of metadata, I think that we are fine considering “process metadata” a part of “descriptive metadata”; however, I would not say that they are synonyms.

Definition & source

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".

Ralph Kimball's "Process metadata describes the results of various operations in a data warehouse."

Metadata that describes process or domain-specific information about instance data. It provides conceptual, contextual, and processing information for instance data. It can also provide greater depth and more insight about the "container" of the data, whether it is a file, document, or representation.metadata that describes relevant or domain-specific information about content. It provides conceptual, contextual, and processing information for data elements. It can also provide greater depth and more insight about the "container" of the data, whether it is a file, document, or representation.

Description It is used in different contexts Data operations and statistical analysis (semantic metadata). 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

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http://en.wikipedia.org/wiki/Metadata




Software implementation (process metadata): describes the results of various operations happening in an application, be it in a data warehouse or any other application. This includes

o processes used to reformat (convert) or transcode content.o all information needed to support data lineage & traceabilityo details of origin and usage (including start and end times for creation,

updates and access).

Descriptive metadata is often a key enabler in deriving business value from data through both direct relationships and indirect relationships between data elementsinstance data. In effect, it creates the “how”, “where”, “who”, and “when” for the data elementsinstance data. “How” - how the instance data is used within the info flow “Where” - source of the data elementinstance data “Who” - who created, modified and approved the data elementinstance data “When” - versioning info of the data elementinstance data

Example Data operations and statistical analysis (semantic metadata)Study related metadata: patient population, indication, therapeutic area

Software implementation (process metadata):

o Process metadata:o metadata needed for the effective management of version control for

standards structural metadata: the UserID that who executed the last modification, the date of the last modification, and the UserID who approved the last modification.

o metadata needed for the effective management of instance data: What is the

o what is source of the data, and in which system(s) is it authoredo w hich transformation happened to the data, how, when, by whom

o metadata needed for managing access control: Who can use a piece of information different roles for accessing information and which action can they can perform (create, read, update, delete)

o : who can edit it in which system, who has read access to ito Which transformation happen to the data, how and wheno Audit trail: who access which information, wheno


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[3.1.4] Process metadata

(suggest to combine with descriptive metadata !!!!) SynonymDefinition & source

Ralph Kimball's "Process metadata describes the results of various operations in a data warehouse."

Description Process metadata describes the results of various operations happening in an application, be it in a data warehouse or any other application. This includes processes used to reformat (convert) or transcode content. all information needed to support data lineage & traceability details of origin and usage (including start and end times for creation, updates and

access).Example What is the source of the data and in which system is it authored

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

Which transformation happen to the data, how and when Audit trail: who access which information, when Version control


[3.1.5] Structural metadata: standards metadata

Synonym OUT – included in structural metadataDefinition & sourceDescriptionExampleRecommended definition

[3.1.6] Study I-Instance Metadata or Study specific metadata

Synonym Study Data StandardsStudy Specific Structural metadata

Definition & source [No source] Study -Instance metadata is a defined grouping of metadata that serves as

the most complete representation of the metadata that defines an individual study.

It is commonly thought of as the set of metadata that is actually consumed by the clinical technology platform to facilitate processes that are more automated and consistent.

It consists of Structural and Descriptive metadata

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deZegher, Isabelle, 05/06/13,

we need to decide if we want to include descriptive metadata in there.. I think this is different




Description The Study Instance Metadata is extracted from the Structural metadata, maintained within an organisation within a Within the context of a Metadata store, Study -Instance Metadata is stored separately from the Standards Metadata, either as a set of relationships back to the Standards Metadata or as a copy of the Standards Metadata. This is dependent on the Metadata store tool in use.The Study I-Instance Metadata (the most complete representation of the metadata that defines an individual study) is exported to and consumed by the clinical data platform to ensure maximal automation and consistency of the processes for trial design, execution, storage, analysis, and submission.Because the Study -Instance Metadata can consist of Structural, Standards, and Operational Metadata, there exists a wide range of purposes that can be served as Study -Instance Metadata. Trial-definition metadata per the PRM Trial-definition metadata per SDTM Trial Design Study CRFs metadata Data-definition metadata Submission Define.xml

Example During the set-up of a clinical trial collection database, the Oncology project team decides to use the AECAT variable in anticipation of grouping the multitude of adverse events at the time of analysis. This project team has been granted the option to select AECAT from a subset of the Permissible datastructural metadata ( elements of the SDTM standard) by the standards governance group for the sponsor’s organization. This project choice is stored within the Study -Instance Metadata for use by the relevant CDMS data collection tools to accurately construct the collection database.


[3.1.7] Semantic Metadata

Synonym OUT – included in descriptiveDefinition & sourceDescriptionExampleRecommended definition

[3.1.1] Metadata repository

SynonymDefinition & Repository composed of Descriptive Meta Data.

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source 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.

DescriptionExampleRecommended definition

3.1.3[3.1.2] Metadata registry


ISO 11179 standard and this web page http://datadictionary.blogspot.com/2008/03/metadata-repositories-vs-metadata.html, it seems the definition of "MDR" should be discussed. Is it a Metadata Repository or Metadata Registry? The point that was interesting from that website was a "Registry is a protected back room where human-centric workflow processes are used ensure that metadata items are non-duplicates, precise, consistent, concise, distinct, approved and unencumbered with business rules that prevent reuse across an enterprise". There is quite a good point here.


3.1.4 Data element

Synonym Variable(Note: the term “attribute” is also used interchangeably for DE – but it is a different concept)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]

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NOTE: The mark up or tagging facilitates document indexing, search and retrieval, and provides standard conventions for insertion of codes.

[ISO1]unit of data for which the definition, identification, representation and permissible values are specified by means of a set of attributes

Description 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.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 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

Example 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


3.1.5[3.1.3] 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.

Description Example


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3.1.6[3.1.4] 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

applicationDescription Example


3.1.7[3.1.5] 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.

Description Example Boolean or string are basic data type

PQ (for Physical Quantity) or II (for Internal Identifier) are abstract data type (ISO 21090)


3.2 Master data management

3.2.1 Master Data


[Gartner – Magic Quadrant for Master Data Management of Customer Data Solution]http://www.gartner.com/technology/reprints.do?id=1-1CK9UDO&ct=121019&st=sbMaster data is the consistent and uniform set of identifiers and extended attributes

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http://www.gartner.com/technology/reprints.do?id=1-1CK9UDO&ct=121019&st=sb




that describes the core entities of the enterprise, such as customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.

Description Master Data is business data that has a consistent meaning and definition, shared across systems. 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 dataExample Site identification information such as : Site ID, Site Name, Site Address, …

Investigator identification attributes Study Identification attributes


3.2.2 Master Data Management


[Gartner – Magic Quadrant for Master Data Management of Customer Data Solution]http://www.gartner.com/technology/reprints.do?id=1-1CK9UDO&ct=121019&st=sbMDM 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 Example oRecommended definition

3.2.3 Master Reference Data

SynonymDefinition & sourceDescription A combination of Master Data and Reference Data. The governance of these 2 components is

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.

Example


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http://www.gartner.com/technology/reprints.do?id=1-1CK9UDO&ct=121019&st=sb




3.2.4 Master Data Source System

3.2.5 Reference Data

SynonymDefinition & sourceDescription 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

Example List of Country codes List of Therapeutic areas


3.2.6 Reference Data Management

3.3 Controlled Terminology, code systems & value sets

3.3.1 Concept

SynonymDefinition & sourceDescriptionExampleRecommended definition

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

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3.4 Interoperability

included in document (Source: Coming to Term: Scoping Interoperability for Health Care, HL7 EHR Interoperability WG)

included in document (Source: Coming to Term: Scoping Interoperability for Health Care, HL7 EHR Interoperability WG)

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included in document (Sources: 1. Coming to Term: Scoping Interoperability for Health Care, HL7 EHR Interoperability WG and2. Principles of Health Interoperability HL7 and SNOMED (Health Information Technology Standards), author: Tim Benson, April 2012)

3.4.1 Interoperability


ISO 11179 interoperability concerning the creation, meaning, computation, use, transfer, and exchange of data [ISO/IEC 20944-1]

ISO 1117: capability to communicate, execute programs, or transfer data among various functional units in a manner that requires the user to have little or no knowledge of the unique characteristics of those units [ISO/IEC 2382-1]"

IEEE: ability of two or more systems of components to exchange information and to use the information that has been exchanged. IEEE (Source: http://www.ieee.org/education_careers/education/standards/standards_glossary.html)


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3.4.2 Technical interoperability (“machine interoperability”)


Technical Interoperability: The focus of technical interoperability is on the conveyance of data, not on its meaning. Technical interoperability encompasses the transmission and reception of information that can be used by a person but which cannot be further processed into semantic equivalents by software. Note that mathematical operations can be -- and frequently are -- performed at the level of technical interoperability. A good example is the use of a “check digit” to determine the integrity of a specific unit of transmitted or keyed-in data. The same mathematical formula is performed at each end of a transaction and the results compared to assure that the data was successfully transmitted. Technical interoperability moves data from system A to system B. Synonyms: Functional, Syntactic, exchange(Source: Coming to Term: Scoping Interoperability for Health Care, HL7 EHR Interoperability WG)


3.4.3 Semantic interoperability


Semantic Ineroperability: To maximize the usefulness of shared information and to apply applications like intelligent decision support systems, a higher level of interoperability is required. This is called semantic interoperability which has been defined as the ability of information shared by systems to be understood… so that non-numeric data can be processed by the receiving system. Semantic interoperability is a multi-level concept with the degree of semantic interoperability dependent on the level of agreement on data content terminology and the content of archetypes and templates used by the sending and receiving systems.Semantic Interoperability ensures that system A and system B understand the data in the same way(Source: Coming to Term: Scoping Interoperability for Health Care, HL7 EHR Interoperability WG)


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3.4.4 Process Interoperability


Process Interoperability: Process interoperability is an emerging concept that has been identified as a requirement for successful system implementation into actual work settings. It was identified during the project by its inclusion in academic papers, mainly from Europe, and by its being highlighted by an Institute of Medicine (IOM) report issued in July 2005 which identified this social or workflow engineering as key to improving safety and quality in health care settings, and for improving benefits realization. It deals primarily with methods for the optimal integration of computer systems into actual work settings and includes the following:• Explicit user role specification• Useful, friendly, and efficient human-machine interface• Data presentation/flow supports work setting • Engineered work design • Explicit user role specification• Proven effectiveness in actual useProcess interoperability coordinates work processes, enabling the business processes at the organizations that house system A and system B to work together. Process interoperability is achieved when human beings share a common understanding, so that business systems interoperate and work processes are coordinated.Comment: EU Interoperability framework (EIF) defines organizational Interoperability which might be the same as process interoperability?(Sources: 1. Coming to Term: Scoping Interoperability for Health Care, HL7 EHR Interoperability WG and2. Principles of Health Interoperability HL7 and SNOMED (Health Information Technology Standards), author: Tim Benson, April 2012)


3.5 Data aggregation, integration

3.5.1 Data pooling

POOLING is the act of pulling together different kinds of data on the same patient (or set of patients in a clinical trial) to give a holistic representation of what was observed for each patient during the clinical trial. Observed data are the foundation of the clinical trial and should accurately reflect what

happened during the course of the trial to the patients in the trial. Once a trial is completed and a database locked, the observed data should never change. It

becomes a historical record/fact of what occurred during the trial.

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Observed data is frequently manipulated to transfer it from one system to another or to facilitate analysis and presentation of the data.

Transformations are defined as data mappings to restructure the data format, but leave the data itself unchanged. This often occurs since the format in which the data is collected will depend on the source and the IT requirements for such data collection and storage. This is largely a rules-based activity.

Derivations are the use of mathematical or logical algorithms to change or to create new data values or flags. Derivations also include imputations for missing data to facilitate statistical analysis and inference.

3.5.2 Data aggregation

3.5.3 Data integration

INTEGRATION is the storage of individual datasets in a common physical or virtual IT system. The individual datasets remain distinct entities, but have are located in the same IT environment/infrastructure.

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Emerging TechnologiesVersion: 0.1

Date: 22 April 2013

4 INPUT (draft material that can be used – to be deleted in final document)

4.1 Metadata management

Term Synonym Definitionattribute 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 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.

Meta Data Repository

MDR 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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Date: 22 April 2013

4.2 Master data management

Term Synonym DefinitionMaster 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 Management

MDM 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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Date: 22 April 2013

4.3 Controlled terminology

Term Synonym DefinitionConcept A concept is a “unit of thought” within a particular domain – a unitary or atomic mental representation of a

real or abstract thingConcepts, 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.

Concept A concept definition is the explanation of the meaning of the concept. The concept definition may be

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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Date: 22 April 2013

Term Synonym Definitiondefinition 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 representation

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, references and other resources that serve to further identify and clarify what the concept is.

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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Term Synonym DefinitionValue set 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).

4.4 Interoperability

Term Synonym DefinitionSemantic Interoperability

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-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

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Term Synonym Definition

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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Emerging TechnologiesVersion: 0.1 Date: 22 April 2013

5 REFERENCES & RELATED DOCUMENTs

Related DocumentsReference 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/Guidances/UCM292334.pdf

[CDISC1] CDISC Glossary - 2009 http://www.cdisc.org/stuff/contentmgr/files/0/08a36984bc61034baed3b019f3a87139/misc/act1211_011_043_gr_glossary.pdf

[ISO1] ISO1179 - ISO/IEC 11179 Metadata Registry (MDR) standard

Accessible on ISO site

[ISO2] ISO2109ISO 21090 Healthcare Data Type Standard

Accessible on ISO site (draft version available on Internet)

Status Name Company Date Signature

Author

Author

Author

Author

6 Appendices6.1 CDISC glossary

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http://www.cdisc.org/stuff/contentmgr/files/0/08a36984bc61034baed3b019f3a87139/misc/act1211_011_043_gr_glossary.pdf




