united nations economic commission for europe statistical division international collaboration to...
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United Nations Economic Commission for EuropeStatistical DivisionUnited Nations Economic Commission for EuropeStatistical Division
International Collaboration to Modernise Official Statistics
Steven ValeUNECE
UNECE Statistics: Priorities
Population censuses, migration, Millennium Development Goals
Globalisation, National Accounts, employment, business registers
Sustainable development, environmental accounts, climate change
Modernisation
Introducing the HLG
High-level Group for the Modernisation of Statistical Production and ServicesCreated by the Conference of European Statisticians in 2010Vision and strategy endorsed by CES in 2011/2012
Who are the HLG members?
Pádraig Dalton (Ireland) - Chairman Trevor Sutton (Australia) Wayne Smith (Canada) Emanuele Baldacci (Italy) Bert Kroese (Netherlands) Park, Hyungsoo (Republic of Korea) Genovefa Ružić (Slovenia) Walter Radermacher (Eurostat) Martine Durand (OECD) Lidia Bratanova (UNECE)
What does the HLG do?
Oversees activities that support modernisation of statistical organisations
Stimulates development of global standards and international collaboration activities
“Within the official statistics community ... take a leadership and coordination role”
Why is the HLG needed?
Before the HLG Now
Many expert groups Clear vision
Little coordination Agreed priorities
No overall strategy Strategic leadership
Limited impact Real progress
These challenges are too big for statistical organisations to
tackle on their own
We need to work together
Using common standards, statistics can be produced
more efficiently
No domain is special!
Do new methods and toolssupport this vision, or do they
reinforce a stove-pipe mentality?
What has the HLG achieved?
2012• Generic Statistical Information Model
2013• Common Statistical Production Architecture• Frameworks and Standards for Statistical
Modernisation
2014• Implementation of the Common Statistical
Production Architecture• Big Data in Official Statistics
What is GSIM?
A reference framework of information objects It sets out definitions, attributes and
relationships of information objects It aligns with relevant standards such as DDI
and SDMX
GSIM and GSBPM
GSIM describes the information objects and flows within the statistical business process.
Main Changes Phase 8 (Archive) removed
• Archiving can happen at any stage in the statistical production process
New sub-process• "Build or enhance dissemination components"
Clearer distinction between detection and treatment of errors
Sub-processes re-named to improve clarity Descriptions of sub-processes improved Terminology is less survey-centric
… but if statistical organisations work together to define a common
statistical production architecture ...
Project Outcomes
The CSPA approach works
It promises increased:• sharing• interoperability• collaboration opportunities
Some licensing issues!
Services being built
1. Seasonal Adjustment – France,Australia, New Zealand
2. Confidentiality on the fly – Canada, Australia
3. Error correction – Italy
4. SVG Generator – OECD
5. SDMX transform – OECD
6. Selecting sample from business register – Netherlands
7. Editing components – Netherlands
8. Classification Editor – Norway
Architecture Working Group:Australia, Austria, Canada, France, Italy, Mexico, Netherlands, New Zealand, Turkey, Eurostat
Catalogue team:Australia, Canada, Italy, Hungary, New Zealand, Romania, Turkey, Eurostat
Current status
Key challenges identified:• Quality framework for Big Data• Privacy and data security• Partnerships with suppliers, processors and users• Methodology and IT for Big Data• Skills needed to use Big Data
Task teams established to tackle these issues Reports / guidelines by the end of 2014
Sandbox Irish Centre for High-end Computing /
CSO have created a Big Data ‘sandbox’ containing datasets and tools
“Play is the highest form of research” – Einstein
Sandbox: Aims
Test feasibility of remote access and processing: - Could this approach be used in practice?
Test whether existing statistical standards / models / methods can be applied to Big Data
Determine which Big Data software tools are most useful for statistical organisations
Learn about the potential uses, advantages and disadvantages of Big Data – “learning by doing”.
Build an international collaboration community on the use of Big Data
Virtual meetings
We use Webex – others are available Flexibility
Free for participants• Join meetings from office, home, airport etc.
Screen sharing Virtual whiteboard
Wikis
Central repository of information Latest versions and comments in one place Good for joint drafting of papers Access anywhere with a web connection Can be public or restricted
Governance
CES
Bureau
HLG
HLG Secretariat Team
Modernisation Committee
Standards
Modernisation Committee
Production and methods
Modernisation Committee
Products and sources
Modernisation Committee
Organisational framework
and evaluation
Executive Board
Project 1
Project 2
Project n