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

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 http://www1.unece.org/stat/platform/display/metis/

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

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

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

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

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

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

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)

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

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

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?

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

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

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

Lessons learned

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

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

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

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.

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

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.

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

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

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