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2010 Analysis of existing metadata case studies. Alice Born (Statistics Canada), Joza Klep (Statistical Office of the Republic of Slovenia) METIS Work Session March, 2010. Existing case studies. 17 existing cases studies from :

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2010 Analysis of existing metadata case studies

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2010 analysis of existing metadata case studies l.jpg

2010 Analysis of existing metadata case studies

Alice Born (Statistics Canada),

Joza Klep (Statistical Office of the Republic of Slovenia)

METIS Work Session March, 2010


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Existing case studies

17 existing cases studies from :

Albania (ALB), Australia (AUS), Austria (AUT), Canada (CAN), Croatia (HRV), Czech Republic (CZE), Finland (FIN), Germany (DEU), Latvia (LVA), Netherlands (NLD), New Zealand (NZL), Norway (NOR), Portugal (PRT), Slovenia (SVN), 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/


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Scope of analysis

  • In response to participants’ requests,2010 analysis focuses on:

    Section 1 and Section 2.4 Strategies for implementation

    Section 4

    System and design issues

    Section 5

    Organizational and workplace culture issues

    Section 6

    Lessons learned


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Metadata strategies

  • Template: Explanation of the overall strategy for managing metadata across the organization.

  • For example, the mandate/programme providing framework for the project, and basic metadata management principles used.

    1. implicit metadata strategies

    2. explicit metadata strategies


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Implicit metadata strategies (part of wider context)

  • New business architecture

  • Architecture for datawarehouse

  • SOA funcitionality

  • Process-oriented production

  • Finding and interpreting statistical data

  • Formal process for standardization


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Explicit metadata strategies - examples

  • Strategy for End-to-End Management of ABS Metadata - Australia

  • Strategically, our metadata management system forms part of a larger system of applications called the End-to-end Statistical Data Management Facility (ESDMF). Metadata driven system is inevitable because metadata is used and generated at every stage of the statistical production process - South Africa

  • The Business Model Transformation Strategy (BmTS) is designing a metadata management strategy that ensures metadata - New Zealand

  • One of the primary objectives of the Integrated Metadatabase (IMDB) is to inform users on concepts, methodologies and data accuracy. The IMDB provides the metadata to support the statistical products released by Statistics Canada's Dissemination Division, and relates to the interpretability in the Agency's quality assurance framework - Canada


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Explicit metadata strategies - examples

  • To standardize definitions across all statistical activities; to move the production of statistics closer to the subject-matter experts in order to speed up the statistical survey life cycle; to present statistics on internet along with its context in order to make statistics understandable and available to users of all types, i.e. to extend the use of statistics beyond the usual statistical publications - Croatia

  • Systematic use of metainformation inside and outside the SIS as a tool for internal and external integration. SMS is focused on the SPP. The model used for definition of a statistical variable ensures its standard description from the beginning up to the end of LCST - Czech Republic


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Explicit metadata strategies - examples

  • there are several projects - independently planned and implemented - that involve a centralised metadata management. Taken together, these projects form the work plan for the next 2-3 years. The task is to combine the projects in a way that at least the outline of a common metadata strategy starts to emerge - Germany

  • The strategy focuses on establishing a conceptual framework, clear roles and responsibilities, and a stepwise development involving integration and linkage of systems - Norway

  • In 2002 after thoughtful analysis of data and metadata flows, Integrated Metadata Driven Statistical Data Management System (further IMD SDMS) was created - Latvia


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Implementation Strategy (2.4)

  • Most countries reported a “step-wise” implementation

  • Also reported:

    • swinging of pendulum

    • integration of previously existing legacy systems into anIntegrated Metadata System

    • »big-bang« with a subsystems implementation strategy


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Implementation Strategies – Stepwise approached

  • from a set of static web pages to definitions of conceptsvariables and classifications

  • Five surveys as pilots

  • new metadata in parallel with general modificitaion of statistical data system

  • systems by systems, first classifications

  • new development projects should act according to the new business architecture


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Implementation Strategies – “swinging of the pendulum”

  • from »big bang« (cathedral projects) – to »opportunistic ("piggybacking" on other projects ) and incremental with a broad »master plan«

  • development of a "2020 Vision" encapsulating longer term ABS aspirations. Having clearly defined the state the ABS aspires to reach longer term, the next step would be to determine the most appropriate strategy for moving forward.)

  • these two considerations informed the development of the "SESAME Framework" (Standards Enabled Shared Active Metadata Environment) in 2008

    Australia


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Implementation Strategies – integration of previously existing legacy systems into the IMS

  • In order to minimize the complexity of the complete system, the individual components (subsystems) should be able to work independently, communicating with each other and the central "Registry" by means of a web service and program interface layer

    Austria


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Implementation Strategies

»big-bang« with a subsystems implementation strategy

Czech Republic


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System and design issues

Section 4 of case study template

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


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Tools and standards (4.2 and 4.3)

ISO-IEC 1117959%

SDMX47% (in use, planning to use)

DDI29%

Neuchâtel35%

Oracle database29%

.NET53%

PC-Axis18%


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Metadata system components

GSPBM29%

Corporate metadata system41%

Collection management system12%

Data archiving29%

Survey metadata (passive)59%

Process metadata (active)35%

Dataset registry29%


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Metadata system components

  • Data element registry59%

  • Classification system76%

  • Classification coding system12%

  • Questionnaire development tool24%

  • Questions and response choices41%

  • Questionnaires65%


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Architecture and development (4.1, 4.4 and 4.5)

  • Service-oriented architecture (SOA) and enterprise service bus – 35% +

  • In-house development – 59% and mixed 29%

  • Sharing of software – being discussed by NSOs (UN Statistical Commission, MSIS)


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Organisational and workplace culture issues

Section 5 of case study template

5.1Overview of roles and responsibilities

5.2Training and knowledge management

5.3Partnerships and cooperation


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Roles (5.1)

Subject-matter expert82%

IT experts76%

Methodologist65%

Dissemination expert47%

Standards29%

Project manager24%

Business analyst12%

Terminologist 9%

One organizational metadata unit 47%


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Training (5.3)

  • Methods

    • Intranet35%

    • Manuals24%

    • Workshops47%

    • New employees18%


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Partnerships and cooperation (5.4)

  • ALB – worked with Statistics Sweden

  • AUS – global partnership in software development, implementation of SDMX, DDI

  • AUT – University of Vienna, participation in METIS

  • CAN – participation in METIS

  • HRV – worked with Statistics Sweden

  • CZE – internal

  • FIN - participation in METIS, INSPIRE (spatial metadata)

  • DEU - participation in METIS, Statistics Norway and Swiss Federal Statistical Office


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Partnerships and cooperation (5.4)

  • LVA – metadata workshops delivered by Sweden, Norway and Finland

  • NLD - participation in METIS

  • NZL - participation in METIS

  • NOR - participation in METIS, Neuchâtel, Scandinavian collaboration

  • PRT – visits to CAN, advice to SVN and African countries, SDMX Eurostat Workforce

  • SVN - participation in METIS, PC- Axis Reference Group

  • ZAF – visited AUS, NZL, LAV, Ireland and advice from CAN

  • SWE – member of Neuchâtel, PC-Axis Nordic Cooperation


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Lessons learned: Main themes

  • Top management involvement

  • Significant change/framework

  • Quality of metadata

  • Complexities of metadata

  • Find a common language

  • Project management

  • International cooperation


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Top management involvement

  • It is a challenge to formulate a convincing business case for metadata

  • Business issue rather than IT

  • All high-level units given a role

  • Metadata strategy – official mandate

  • All steps of statistical data processing for different surveys allow standardization, while each survey may require complementary functionality


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Significant change/framework

  • Recognize that this is a major change

  • Communication strategy

  • Allow business areas to influence implementation

  • Integrate with business processes

  • Statisticians love frameworks so having one makes life a lot easier

  • Reference to formal documents like the metadata- and IT-strategy is important (approved by the board of directors)


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Quality of metadata

  • Some metadata is better than no metadata - as long as it is of good quality

  • Depends on cooperation, motivation and competencies of metadata authors

  • More efficient to start documenting the metadata right at the outset of any new survey design

  • Authors need to know and understand the how and why of metadata

  • Releasing metadata on the internet improves metadata quality


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Complexities of metadata

  • Not one ideal structure/format

  • Communication of complex metadata principles is a challenge

  • Papers for the METIS sessions and the common metadata framework (especially the case studies) have proven very useful, as they provide arguments for discussions with statisticians and top management

  • Fundamental principles of metadata management, which have been defined by experts during recent years (i.e., In part A of the common metadata framework) will become more and more commonly accepted standards and state of the art for the production and dissemination of statistical information

  • Other metadata standards provide opportunities


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Find a common language

  • Applying externally recognised and supported standards has a lot of benefits - including as a means of building upon a wealth of intellectual efforts and experiences from others

  • Use a metadata framework as common language

  • Training of statisticians, not only in the use of applications but also, and above all, about the concepts underlying the system and workflow of procedures

  • Harmonization between subject areas


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Project management

  • Make simple prototypes as early as possible to get input from users, written requirements are to abstract

  • The advantages and disadvantages of a metadata model can often only be properly evaluated once an it-system is in place

  • »Translation« from »statistical language« to »IT language« can cause misunderstandings

  • Keep in control of outsourced development activities, supplier may have difficult time understanding the statistical business

  • Usability testing

  • Opportunity costs caused by the non-existence of centralized end-to-end metadata systems are rarely found in accounting systems


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International cooperation

  • Might help to understand the subject of metadata management

  • Similarity of the statistical process is important for IT personnel

  • Building on existing international knowledge minimises risks and maximises return on investment

  • SMS is increasingly expected to interoperate with metadata management as practised in other communities (eg geospatial, academic/research) and sectors


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Future work

  • New case studies in METISwiki – looking for countries to add their case studies

  • Convergence on the use of key metadata standards and metadata models

    • Can we move to more harmonization in implementation across countries?

    • Further analysis on comparing architectures

  • Suggestions for improving template

  • Add tables to WP on METIS website


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