sbvr-driven business transformation and data governance in large organisations

Pieter De Leenheer, PhD

Business Semantics-driven Transformation in Large Organisations

a Case for the Flemish Public Administration

Enterprise Data World, San Francisco, March 2010

Thursday 26 August 2010

www.collibra.com


http://www.pieterdeleenheer.be


www.collibra.com


www.collibra.com

www.pieterdeleenheer.be




“Study Area”

asked to >20 people in the Ministry of Education and

Training

What is ...


‣ One of the 16 categories mentioned in article 19 in which academic trainings are bundled (decree concerning universities)

‣ A group of related training content (decree concerning education XIV)

‣ A group of related training content in secondary adult education or in higher vocational education (decree concerning adult education)

‣ One of the categories mentioned in articles 23 and 24 of the decree which bundles trainings (decree concerning the restructuring of higher education in Flanders)

‣ A group of structural components based on content-wise relations in technical and vocational education, as well as , in [...] (decree secondary education)

sample from an inquiry of >20 people in the Ministry

Well, it means ...


‣ One of the 16 categories mentioned in article 19 in which academic trainings are bundled (decree concerning universities)

‣ A group of related training content (decree concerning education XIV)

‣ A group of related training content in secondary adult education or in higher vocational education (decree concerning adult education)

‣ One of the categories mentioned in articles 23 and 24 of the decree which bundles trainings (decree concerning the restructuring of higher education in Flanders)

‣ A group of structural components based on content-wise relations in technical and vocational education, as well as , in [...] (decree secondary education)


Well, it means ...

In public administrations the decrees dictate the semantics of business terms and how they relate to each other.

This business context is a crucial source to give meaning to metadata.


‣ metadata

‣ is ambiguous

‣ is inconsistent

‣ is multi-form


‣ metadata

‣ is ambiguous

‣ is inconsistent

‣ is multi-form

Study Area

TutorStudent

Institute

Resume....

...



What is metadata?


‣ data about data, of information on a more abstract level;

‣ a conceptual framework that is unambiguous (= one meaning) and uniform (= holds for all);

‣ documentation for business analysts;

‣ description of the meaning of tables and fields of a database;

‣ from a technical point of view: auxiliary data about something;

‣ documentation about projects and processes and nothing more;

‣ a sort of classification of data that one manages, and that can be used and exchanged, so that at all times everybody knows what something means, and what its purpose is.


What is metadata?


‣ data about data, of information on a more abstract level;

‣ a conceptual framework that is unambiguous (= one meaning) and uniform (= holds for all);

‣ documentation for business analysts;

‣ description of the meaning of tables and fields of a database;

‣ from a technical point of view: auxiliary data about something;

‣ documentation about projects and processes and nothing more;

‣ a sort of classification of data that one manages, and that can be used and exchanged, so that at all times everybody knows what something means, and what its purpose is.

“The term ’metadata’ is widely used with different meanings. The common understanding of meta-data as ’data about data’ has misled

IT professionals and information workers alike”

Gartner(http://www.gartner.com/DisplayDocument?ref=g search&id=708407)


What is metadata?


http://www.gartner.com/DisplayDocument?ref=g

http://www.gartner.com/DisplayDocument?ref=g

SchemasSocial Tagging

Taxonomies Documents



Enterprise One Enterprise Two

Metadata Mishmash in Enterprise 2.0







Person NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NameYear Wine

Person NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NameMan Woman

Person NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NameCoffee Drink

Person NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NameAnimal Mammal

Person NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NameOS Computer

Person NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NameComputer Desktop

Person NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NameComputer Mouse

Person NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NameCar Name

Person NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NameAddress Person

Person NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NameEngine Car

Person NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NameCar Person

Person NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson NamePerson Name












































































































































B2B

meaningful
























































































































































B2B

Business Semantics Management Semantic Integration

meaningful



Conceptual Level

Relational Level

Technical Level

Implementation Level

concepts, definitions, glosses

relations, facts, taxonomies

star schemes, UML

database schema, code

Organisation

Operational/Technical

Business/Social

7tangible solutions for your business semantics

2. Create Every core term is syntactically described. E.g., Figure 3 illustrates the concept defini-

tion for term “Street Address” (specialisation of the type “Address”) in the BSG. Apart from its

gloss, it is described in terms of facts (eg “business address located in street”) and rules (eg

business address has exactly one zip code). For each term (within its context) there are certain

roles to play as a “steward concept and a number of relevant stakeholders. Each change is

carefully logged in order to be able go back any time. The activity is fed by implicit know-

how of domain experts, by to automatic extraction of facts from existing metadata (see De

Leenheer (2009) for a review of extraction techniques). According to our metadata landscape

analysis, many FMET entities have isolated `` grassroots’’ metadata initiatives. They manifest

themselves in various forms such as taxonomies, keyword systems, glossaries, information

about database fields, metadata in Web pages and content management systems (CMS).

These use many proprietary formats as well as open formats such as XSD and UML used.

Thanks to SBVR (OMG, 2007), the business concepts can be defined in a natural way, while at

anytime a formal enterprise information model can be automatically generated in any format.

3. Refine Fact types created in the previous activity are refined so they are understand-

able to both business and technology. E.g., The somewhat technical term “Empl” becomes

“Employee” or “EmplAddr” is revealed as the fact type “Employee residing in Residential

address.

4. Articulate Since multiple users parallelly render their perspective on a term, it may be that

after the refine activity some facts and rules impose contradicting statements. During this

activity, conflicts and inconsistencies are removed. Specifically designed algorithms may help

here. Eg. in the Netherlands an address is uniquely identified by a combination of postcode

and house number, while in Belgium a combination of postcode, street name and street

number is required.

5. During unification is a new version of the business model generated.

Figure 3 Screenshot of the term “Home Address” in the Business Seamntics Glossary.

Even though the concept definitions look like natural language, one can automati-cally generate an enterprise information model that provides a formal specification.

Levels & Types of Metadata


Conceptual Level

Relational Level

Technical Level




star schemes, UML


Organisation


Business/Social



















address.






number is required.






















address.






number is required.






Conceptual Level

Relational Level

Technical Level




star schemes, UML


Organisation


Business/Social




Business/Social

"Customer"

"Person in Country Belgium that bough a product last year"

"Person that buys our products with a certain frequency"

CRM DATABASE MAINFRAME

Manage the business context

Business communities, IT demand side

Technical communities, IT supply side

Mind the Gap



Business/Social

"Customer"






Technical communities, IT supply side data semantics: what does my data mean?

data governance: who is

responsible for my data?

data utilisation: how is my data utilised ?

data provenance: where

does my data come from?

Mind the Gap

data quality: how is my data evaluated?


"Customer"







Bridging the Gap


"Customer"







BUSINESS SEMANTICS is unified metadata that provides

meaningful answers

Bridging the Gap


"Customer"








meaningful answers

Bridging the Gap


meaningful answers


...more than technologyOrganisation Technology

Culture Methodology

Conceptual level

Relational level

Technical level

Implementation level


Dimensie 3: technologie

‣ Systematische en praktische toepassing van metadatabeheer die geïmplementeerd en ondersteund wordt door computersoftware en -hardware.

13

...


Reality Constraints

© Martin Hepp


Reality Constraints

© Martin Hepp

stop the clock ! big design

upfront


Reality Constraints

© Martin Hepp


upfront X years implementation


Reality Constraints

© Martin Hepp


upfront X years implementation

back to reality: first test after X years when business reality already

changed !


Agile methodology

‣ Two processes

‣ Semantic reconciliation of versatile metadata in uniform and unambiguous Business Semantics

‣ Semantic application of (derived) standardised Enterprise Information Model

‣ independent iterations: scalability by cross-feedback

‣ Governance through roles and responsibilities

Semantic Reconciliation Semantic Application

Articulate Unify Select CommitRefineCreateScope

15


Dimension 1: organisation

16

!‣ Een sociaal arrangement van

rollen en verantwoordelijkheden die als gemeenschappelijk doel informatiebeheer nastreven door aan metadatabeheer te doen.

‣ Ziet erop toe dat de methodologie wordt toegepast en juiste technologie gehanteerd wordt.


Dimensie 2: cultuur

‣ Bevat het gezamenlijk begrijpen, beheren en toepassen van metadata binnen de context van een onderneming.

17


Reference ArchitectureBusiness

Information /Communication

Technology

Strategy

(Infra)Structure

Operation

CasesSection 1.7 & Appendix

A

OrganisationSection 1.5 &

Chapter 4

CultureSection 1.6 & Chapter 5

TechnologySection 1.4 & Chapter 3 & Appendix B

MethodologySection 1.3 &

Chapter 2Maes’ Negenvlak


Business Drivers fuel the Perfect Team


Business driver: documentation

‣ Tackle high-turnover

‣ Minimize loss of know-how

‣ Reconstruct reasoning steps of strategic advices

‣ Minimize overhead repetitive calls

20

`` Sometimes people do not know what data is out there. Knowing the whereabouts of data starts with many calls within and between business units over and over

again.'' (Anton Derks)


Business driver: communication

‣ Internal and external:e.g., data exchange with schools and software developers

‣ Technical specification (e.g., in the form of UML)

‣ Common “language” between knowledge workers (internally as well as within the outsourcing partnership)

21

`` No communication without metadata!''(Jan Dejonghe)


Business driver: re-usability

‣ accelerates the retrieval of assets and promotes their reuse

‣ facilitate the retrieval and reuse of software services in SOA

22


Business driver: Impact Analysis

‣ capture complex dependencies between different systems, people and applications

‣ ICT-outsourcing partnership as-is often cannot see the forest for the trees

23

`` You hear now and again: "they have changed something to the data table, and now it does not work anymore." (Frans Decuyper)


Business driver: disambiguation

‣ get rid of ambiguity and inconsistency in metadata

‣ intuitive

‣ repeated discussions

24

`` There are 180 000 teachers, more than one million students and thousands of educational institutions. FMET forms a large part of our society, hence it is certainly

important that labels are attributed the same meaning.''(Martin Maesen)


Business driver: uniformity

‣ Enterprise-wide

‣ More confidence

25

"In my early years I have worked on a glossary: the most difficult part was to obtain consensus." (Marleen Deputter)


Business driver: compliance

Metadata plays an important role in regulatory compliance, a field that is not much explored so far :

• Authentic sources: where data are spread over different systems, it is difficult to determine which of the systems is the original data source.

• Privacy: some data are covered by privacy legislation. When it is impossible to learn whether a piece of data is covered by privacy rules, it is difficult for an organization to comply with such regulation.

• Security: FMET has currently a Security Officer to verify regulatory compliance. Using metadata provenance of data can be logged.

26


Metadata Landscape

27


Metadata Landscape

27

O&V is ready for the next towards information maturity. • exchange platform for technology and best practices• teachable and repeatable metadata methodology• data governance


Metadata Repository Paradox

‣ Several reasons for this:

‣ the historically grown inconsistent and difficult to unlock collection of metadata sources;

‣ the structural independence of the business units ( called "entities");

‣ the intended independence of FMET towards its ICT-outsourcing partner HP;

‣ the general economic trend towards dynamic value networks;

‣ the increasing presence of "Web 3.0" where data and services are decentralized and accessible to each other via URIs. A vision shared with Semantic Web and Web Science communities (cf. IEEE CS Jan. 2010, etc.).

‣ However, a fully decentralized approach is not feasible within FMET where business semantics are determined by regulations.

28


Metadata Repository Paradox

‣ Several reasons for this:

‣ the historically grown inconsistent and difficult to unlock collection of metadata sources;

‣ the structural independence of the business units ( called "entities");

‣ the intended independence of FMET towards its ICT-outsourcing partner HP;

‣ the general economic trend towards dynamic value networks;

‣ the increasing presence of "Web 3.0" where data and services are decentralized and accessible to each other via URIs. A vision shared with Semantic Web and Web Science communities (cf. IEEE CS Jan. 2010, etc.).

‣ However, a fully decentralized approach is not feasible within FMET where business semantics are determined by regulations.

28

Alternatively, BSM stands and falls with two co-evolving initiatives: • a business semantics glossary (BSG)• an enterprise information model (EIM)

Supported by social roles and responsibilities and embedded in the existing enterprise architecture.


Scratching the Metadata SurfaceB

usin

ess /

Socia

lTech

nic

al /

Op

era

tion

al

Conceptual

Relational

Technical

Operational

Metadata Levels

decrees,

synonyms,

taxonomies, ...

facts,

rules,

patterns, ...

UML,

XSD,

ER, ...

SQL,

web-services

xpath, EDI, ...

XSD UML ER (Technical) Metadata Management

Business Intelligence

Data Integration Middleware

Business Model, Glossary, Process Documentation


Full-cycle Business Semantics ManagementB

usin

ess /

Socia

lTech

nic

al /

Op

era

tion

al

Conceptual

Relational

Technical

Operational

Metadata Levels

decrees,

synonyms,

taxonomies, ...

facts,

rules,

patterns, ...

UML,

XSD,

ER, ...

SQL,

web-services

xpath, EDI, ...

reco

ncili

atio

nap

plic

atio

nManual commitment

Automatically generated commitment

Business Vocabulary, Facts, Rules (SBVR)

Enterprise Information Model v1

Enterprise Information Model v2 ...

XSD UML ER

clos

ed-lo

op fe

edba

ck

Full-cyle Business Semantics Management


name lname city

pieter de leenheer

cy

brussels finland

person name

first name

last namecity

country

identified by / identifying

residing in / resides

born in / is birth place of

consisting of / part of

consisting of / part of

Fact-orientation

31


© 2010 Collibra nv/sa Ransbeekstraat 230, B-1120 Brussels T +32 2 263 21 18 F +32 2 263 21 59 www.collibra.com

It is not about the metadata format.

The question is: how to deal with metadata in large organisations that need versatility in cross-referring business vocabularies ?


Semantics of Business Vocabulary and Rules (SBVR)

Semantic Community

URI_School URI_Address URI_Student

Shared Body of Meaning

Decree X

StudentVocabulary

Location Vocabulary

II

Facts, fact types, terms, rules

Dublin Core

Location Vocabulary

Speech community

Vocabulary

URI_Teacher ...

Meaning articulation

Concept Identifier


Community Governance

34


Business Semantics Glossary


Impact AnalysisB

usin

ess /

Socia

lTech

nic

al /

Op

era

tion

al

Conceptual

Relational

Technical

Operational

Metadata Levels

decrees,

synonyms,

taxonomies, ...

facts,

rules,

patterns, ...

UML,

XSD,

ER, ...

SQL,

web-services

xpath, EDI, ...

reco

ncili

atio

nap

plic

atio

nManual commitment

Automatically generated commitment

Business Vocabulary, Facts, Rules (SBVR)

Enterprise Information Model v1

Enterprise Information Model v2 ...

XSD UML ER

clos

ed-lo

op fe

edba

ck

Full-cyle Business Semantics Management


Business Semantics Enabler

ESB

xml 1

Middleware


Stock Mngmt ERP

Business Semantics Studio

Enabler



ESB

xml 1

Middleware


Stock Mngmt ERP


Car Movement “5” did not specify a Car Group.

Enabler



ESB Middleware


Stock Mngmt ERP


Enabler



ESB Middleware


Stock Mngmt ERP


xml 1

Car Movement “120” assigns more than one Rental Car.

Enabler



ESB Middleware


Stock Mngmt ERP


Enabler



ESB Middleware


Stock Mngmt ERP


xml 3

Rental Car “XSV829” cannot be assigned to Car Movement specifying Car Group

“Monovolume”.

Enabler



ESB Middleware


Stock Mngmt ERP


Enabler


Metadata Architecture & Governance

‣ A metadata charter is a Memorandum of Understanding that provides motivation, goals, and key stakeholders. It provides a framework of roles and responsibilities and it identifies certain authorities.

‣ Metadata principles establish start points for metadata management that must be respected. E.g., metadata must always be made publicly available and in an open standard format.

‣ Metadata policies contain clear guidelines for relevant actors within the organization to implement BSM in all its facets with sufficient quality and according to the principles.


Information Maturity


Prioritering

40


Zoom in op

‣ P1: uitrollen en ondersteunen van een Eénduidig Begrippenkader:

‣ geen one-shot, business verandert (vb. nieuw decreet)

‣ iteratieve aanpak

‣ versies als basis voor BIM

‣ P3: opstellen van een Bedrijfsinformatiemodel (BIM):

‣ overzicht data en relaties in organisatie

‣ vertrekkende vanuit projectnoden

41


Architectural evolution

Monster

Application



Application 1 Application 2

Data Model

Data

DBMS



Presentation 1 Presentation 2

Data Model

Data

DBMS

Business Logic 1 Business Logic 2

Presentation Tier

Business Logic Tier

Data Tier



Present. 1

Data Model

Data

DBMS

BL

Presentation Layer

Web Service Layer

Data Layer

Present. 2 Present. 3

BL BL BL BL BL BL

Data Model

Data

DBMS

BPM layer BP BP BP


Last Frontier of Separating Concerns ?

Present. 1

Data Model

Data

DBMS

BL

Presentation Layer

Web Service Layer

Physical Data Storage

Present. 2 Present. 3

BL BL BL BL BL BL

BPM layer BP BP BP

Data

Legacy

Data

Unstructured

Real world Semantics

DS DS DSSemantic Data Services DSwell we’re

here!


Conclusions‣ Any organisation already has much valuable metadata as building blocks for

business semantics

‣ To enable business-IT transformation, business semantics should be represented in such a way that it refer both to real-world objects as to a formal specification

‣ Reconciliation of metadata into sharable and reusable semantic patterns requires more than careful selection of technologies: it requires also organisation, methodology, and culture

‣ identify business drivers that convince your senior management to prioritise and free the necessary budget and resources accordingly

‣ outline a roadmap to gradually implement business semantics management as part of the overall information architecture and governance plan

‣ Application of business semantics for EAI is much effective than traditional point-to-point or hub-and spoke approaches and costs less on the longer term

‣ The adoption of standards makes semantic systems sustainable over longer periods of time

info

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ie /

juni

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24

tmet

adat

a

Om adequaat informatiebeheer uit te bouwen is het onvoldoende om gegevens persistent vast te leggen. Daar zorgen databanken voor. Gegevens mogen nog zo goed gestructureerd zijn, als ze niet ontsloten kunnen worden naar derden, is hun waarde praktisch nihil. Het ICT-outsourcing-partnership tussen het Vlaamse ministerie van Onderwijs en Vorming (O&V) en EDS-Telindus, nu HP, erkent de noodzaak hiervan. Initiatieven zoals het uitbouwen van een datawarehouse en een information-governanceorganisatie zijn daar bewijzen van.In dit artikel beschrijven we hoe O&V is begeleid bij haar volgende grote stap naar informatiema-turiteit. Aan de hand van een metadataroadmap combineren we de metadatatechnologie, -metho-dologie, -cultuur en -organisatie die O&V in staat zullen stellen haar zuurverdiende gegevens semantisch te ontsluiten en zo te herwaarderen.

Gesloten-wereldsyndroomOm gegevens te ontsluiten moet men in staat zijn de informatie die erin verscholen zit te kunnen begrijpen. We willen een zinvol antwoord kunnen geven op onder meer de volgende missiekritieke vragen over gegevens:

1. Semantiek: wat is de betekenis van mijn gege-vens?

2. Gebruik: hoe worden mijn gegevens gebruikt? 3. Afkomst: waar komen mijn gegevens vandaan?4. Governance: wie is verantwoordelijk voor

welke gegevens?5. Kwaliteit: wat is de kwaliteit van mijn gege-

vens?

Veel informatiesystemen lijden aan het gesloten-wereldsyndroom. Ze zijn ontworpen vanuit de naïeve veronderstelling dat alle feiten er al in opgeslagen zijn en dat er nooit behoefte zal zijn aan grootschalige gegevensuitwisseling met andere systemen. Bovendien wordt een databank meestal ontworpen vanuit een IT/IS-standpunt. Daardoor is enkel de ontwerper vertrouwd met de interne structuur en regels ervan. De tech-nologische aard van het syndroom ligt in vendor lock-in van gegevens omdat ze worden opgeslagen in merkgebonden (lees: gesloten) formaten. Dit houdt natuurlijk geen steek in het huidige inter-nettijdperk waarin procesoverschrijdende gege-vensuitwisseling centraal staat.Het ICT-outsourcingpartnership is ervan over-tuigd dat ze, net zoals eerder met gegevens, aandacht moeten besteden aan het gestructureerd

Een berekende

sprong naar

informatiematuriteit

Metadataroadmap voor de Vlaamse overheidAdequaat informatiebeheer is meer dan alleen gegevens opslaan,

ze moeten ook ontsloten kunnen worden naar derden toe: er

is ook metadatabeheer nodig. Voor het Vlaamse ministerie van

Onderwijs en Vorming is een metadataroadmap opgesteld om haar

gegevens semantisch te ontsluiten en zo te herwaarderen.

Pieter De Leenheer, Aldo de Moor en Stijn Christiaens


sbvr-driven business transformation and data governance in large organisations

Technology

trainings decree

secondary adult education

ministry of education

auxiliary data

bundled decree

term metadata

meaning of tables

contentwise relations