1. Analysis of existing metadata case studies
Alice Born (Statistics Canada),
Jenny Linnerud (Statistics Norway)
and Jessica Gardner (UNECE)

2. Existing case studies 10 existing cases studies from :
Australia (AUS), Canada (CAN), Croatia (HRV), Czech Republic (CZE), New Zealand (NZL), Norway (NOR), Portugal (PRT), South Africa (ZAF), Sweden (SWE) and the United Nations
Industrial Development Organization (UNIDO)
Available from the METIS-wiki

3. Organisational and workplace culture issues
Case study template
Section 5
Overview of roles and responsibilities
Metadata management team
Training and knowledge management
Partnerships and cooperation
Other issues

4. Roles (5.1) X X X Subject matter expert IT expert
AUS
CAN
HRV
CZE
NZL
NOR
PRT
ZAF
SWE
UNIDO
Subject matter expert X
IT expert
Statistician/ Methodologist X 
Dissemination experts
Standards  X
Project managers
Business analysts
Terminologist

5. Metadata management team (5.2)
AUS
CAN
HRV
CZE
NZL
NOR
PRT
ZAF
SWE
UNIDO
Development project X X   X
One organisational unit
(plus IT)
AUS – Data Management Section (DMS) in the Methodology and data Management Division
CAN – Corporate Metadata Section (CMS) in the Informatics and Methodology Field
NZL – Standards, Solutions and Capability Group
NOR – Statistical Methods and Standards
PRT – Metadata Unit in the Methodology and Information System Department
SWE – Classifications, metadata and content harmonisation (KMI) group

6. Training (5.3) X X X Subsystems Business context Intranet Manuals
AUS
CAN
HRV
CZE
NZL
NOR
PRT
ZAF
SWE
UNIDO
Subsystems  X X X 
Business context
Intranet
Manuals
Workshops
New employees

7. Partnerships and cooperation (5.4)
AUS
CAN
HRV
CZE
NZL
NOR
PRT
ZAF
SWE
UNIDO R  C  V  Â V C
Australia and Portugual cooperate with researchers/Universities. Portugal participates in projects with and sends consultants to the 5 Portuguese speaking African nations. South Africa has also visited Latvia. Ireland and Slovenia. Norway participates in SOS group (Statistical Open Source).
V – visited, C – sent consultents, R- reviewed documents/plans, Â – Neuchâtel group

8. System and design issues
based on the following Case Study Template sections:
Section 2.2 Current Systems
Section 4 System and Design Issues
4.1 IT Architecture
4.2 Metadata Management Tools
4.3 Standards and formats
4.4 Version Control and Revisions
4.5 Outsourcing v.s. in-house development
4.6 Sharing software components and tools
Section 2.1 Links to the GSBPM

9. System components (2.2 + 4.3) x x Data element registry
AUS
CAN
HRV
CZE
NZL
NOR
PRT
ZAF
SWE
UNIDO
Data element registry x
Classification management system
Classification coding system
Questionnaire development tool
  x
Questions and response choices
Question modules/blocks
Instruments (questionnaires)

10. System components (2.2 + 4.3) x X ? Data quality component
AUS
CAN
HRV
CZE
NZL
NOR
PRT
ZAF
SWE
UNIDO
Data quality component x
Other survey metadata ("passive") X
Business activity monitoring
Process metadata ("active")
Dataset registry
Data archiving ?
Collection management system
Corporate metadata system

11. Tools and standards (4.2 and 4.3)
AUS
CAN
HRV
CZE
NZL
NOR
PRT
ZAF
SWE
UNIDO
ISO/IEC 11179 x
SDMX
DDI (Data Documentation Initiative)
Oracle database
.NET x 
GSBPM

12. Architecture and development (4.1, 4.4 and 4.5)
AUS
CAN
HRV
CZE
NZL
NOR
PRT
ZAF
SWE
UNIDO
Service Oriented (SOA) x
In-house development
mix
Sharing of software
Australia is looking for partnerships
Canada and Norway will share documentation on data model
Czech Republic will share but needs to check with partners
Others?

13. Links to GSBPM (2.1) Australia Canada Croatia
ABS has adopted GSBPM as part of their Enterprise Architecture but implementation in their organization still under discussion
Canada
No formal plans to adopt GSBPM at this time however current BPM in their EA is similar to GSBPM
Croatia
Own survey processing model but similar to GSBPM

14. Links to GSBPM (2.1) Czech Republic New Zealand Norway
Uses its own model
New Zealand
Basis for GSBPM however Archiving and Evaluate are embedded in subprocesses
Norway
Modified version of GSBPM however Archiving and Evaluate are embedded in subprocesses and in quality management

15. Links to GSBPM (2.1) South Africa Sweden and UNIDO
Adopted Statistics New Zealand business process model
Sweden and UNIDO
Prepared before GSBPM was adopted however similar except no Archiving phase

16. Lessons learned

17. Main themes Top management involvement Significant change Quality
Complexities of metadata
Common language
People
Project management

18. Top management involvement
Business issue rather than IT
All high-level units given a role
Metadata strategy – official mandate
Good governance
Allocate sufficient resources
Continued management support
Regular reports
High level units involved in implementation and have a role to play (even if just signing off)
Involvement of subject-matter statisticians is a challenge as they have to be released from other priorities

19. Significant change Recognize that this is a major change
Communication strategy
Allow business areas to influence implementation
Integrate with business processes
Regular delivery of functionality
Sweden: “inform a lot”
NZ: SOA requires mind shift
Canada: More efficient to document metadata at the outset of the process than after data is released
NZ: Move from silo systems to business model is a change that should not be underestimated
Portugal: The introduction of the position of survey manager has fostered cooperation and dialogue between production, metadata and dissemination

20. Quality Use standards Accept non-standard classifications exist
Depends on cooperation, motivation and competencies of metadata authors
Continuous training
NZ: Some metadata is better than no metadata - as long as it is of good quality. Our experience around classifications is that there are non-standard classifications used and providing a centralised environment to support these is much better than having an 'black market' running counter to the organisational approach. Once you have the centralised environment with standard & non-standard metadata you are in a much better position to clean-up the non-standard material.

21. Complexities of metadata
Not one ideal structure/format
List of requirements can be endless
Be prepared for survey-specific requirements
Communication of complex metadata principles is a challenge
Other metadata standards provide opportunities

22. Find a common language Harmonization between subject areas
Use a metadata framework as common language
Norway: Harmonising variables between subject matter divisions is also a considerable challenge and an important tool to improve the quality of metadata. Several subject matter divisions may use the same variable names, but define them differently. In some cases this is necessary because of laws and regulations, but this is not always the case. We have meetings where contact persons from divisions using variables with similar names come together and discuss the definitions, e.g. if a division could change the wording of their definition to such an extent that other divisions might use it as well, which would allow us to reduce the number of definitions to one in stead of e.g. three. This is a time consuming work which we have started, but which will require a lot more of resources, both to monitor where harmonisation is needed and to do the job.
Portugal: The distribution of terminology associated with each metadata subsystem is having a beneficial effect at the SP as it encourages the use of a language common to all profiles using the system.

23. People Teamwork Good IT staff Multidisciplinary teams
Outsiders had trouble understanding
Provide incentives
NZ and Croatia: IT staff difficult to recruit, develop and retain (eg attracted to higher paid sectors)
Croatia: Involvement of subject-matter statisticians difficult to achieve as they have other priorities
Portugal: Designing a metadata system not only requires considerable knowledge of statistical production, but also means leaving behind some habits acquired in this area. A great capacity for abstraction and tidy, integrated thinking is also necessary. An institution has specialists with all these capabilities but not always with all of them at the same time. The teams chosen to implement these systems must consist of specialists with different profiles among those mentioned, because they complement each other. The IT technicians who develop applications must participate from the start.
Incentives – (Norway) releasing data online motivates subject matter statisticians to document metadata and improve quality

24. Project management Develop prototypes Usability testing
Break project into manageable pieces
Stepwise approach
Portugal

25. Don’t expect to get it 100% right the first time