1. International Collaboration to Modernise Official Statistics
Steven Vale
UNECE

2. Introducing UNECE Statistics

3. Introducing UNECE Statistics

4. Introducing UNECE Statistics

5. UNECE Statistics: Priorities
Population censuses, migration, Millennium Development Goals
Globalisation, National Accounts, employment, business registers
Sustainable development, environmental accounts, climate change
Modernisation

6. Introducing the HLG
High-level Group for the Modernisation of Statistical Production and Services
Created by the Conference of European Statisticians in 2010
Vision and strategy endorsed by CES in 2011/2012

7. 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) 

8. 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”
Quotes from the Terms of Reference of the HLG

9. 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

10. The Challenges
ABS, like other National Statistical Institutions, faces shared constraints and challenges.
External Challenges
rapidly changing external environment
More sophisticated users
Demand for timeliness and responsiveness
increasing demand for more accessible and ‘joined up’ data to solve complex policy questions
Constraints
Reduced funding and volatility in funding
Our costs are increasing significantly – unable to contact many households, response rates dropping, difficult to recruit and retain interviewers
skills shortages – competing for statistical and ICT skills across government
complex work programs
siloed processes
and aging infrastructure

11. These challenges are too big for statistical organisations to tackle on their own We need to work together

12. Using common standards, statistics can be produced more efficiently No domain is special! Do new methods and tools support this vision, or do they reinforce a stove-pipe mentality?

13. What has the HLG achieved? 2012 2013
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
These projects are chosen based on feedback from the CES and an annual workshop with representatives of around 25 international expert groups.
I will give a short summary of what as achieved in 2013, and what is currently in progress.

14. HLG
Projects
30+ Expert Groups

15. 2012

17. 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
not directly tied to them, nor other concrete implementation details
Provides common semantics that can be used unambiguously across and between different implementations

18. A step back: The GSBPM
Modernization of official stattistics is about the re-design of statistical business processes along which data are collected, processed and disseminated.
In order to do so, a common framework is needed to identify and describe each individual process.
GSBPM offers such framework, describing each step in the production process from the beginning (needs specification) to the end (evaluation and archiving).
Applicable to all domains of statistics, and providing a common language to describe statistical production processes. GSBPM is a flexible tool to compare and harmonize production processes within and across NSIs. As such, it facilitates collaboration and sharing of tools among NSIs.
Many participants will be familiar with the GSBPM, developed by the CES and published in 2009.
The GSBPM provides a framework of standard terminology to describe and define the set of business processes needed to produce official statistics. It is intended to apply to all activities undertaken by producers of statistics at both national and int. levels, which results in outputs. It is designed to be independent of the data sources, so it can be used for the description and qality assesment of processes based on surveys, censuses, administartive records, and other non-statistical or mixed sources.
Developed by UNECE. GSBPM model structured into nine phases (level 1) and their sub-processes (level 2) with a description of activities taking place under each sub-process.
A review is taking place during 2013 which may result in a new version in late 2013 or early 2014.

19. GSIM and GSBPM
GSIM describes the information objects and flows within the statistical business process.

23. Clickable GSIM 23

24. 2013

26. Standards-based Modernisaton%
43%
34,600

27. Outcomes Simplified - 150 110 information objects
Incorporates revised Neuchâtel Model of classification terminology
Better aligned with other standards, particularly DDI

28. Outcomes
GSBPM v5.0

29. Main Changes Phase 8 (Archive) removed New sub-process
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

30. Fundamental Principles of Official Statistics
Mappings
Fundamental Principles of Official Statistics

32. Frameworks and Standards Project

34. Problem statement:
Specialised business processes, methods and IT systems for each survey / output

35. Applying Enterprise Architecture
Disseminate

36. ... but if each statistical organisation works by themselves ...

37. ... we get this ...

38. .. which makes it hard to share and reuse!

39. … but if statistical organisations work together to define a common statistical production architecture ...

40. ... sharing is easier!

41. CSPA development project
Architecture Proof of Concept

42. The Proof of Concept 5 countries built CSPA services
3 countries implemented them

43. Project Outcomes The CSPA approach works It promises increased:
sharing
interoperability
collaboration opportunities
Some licensing issues!

44. 2 Sprints 3 Assemble teams 5 Build teams 1 Working Group
United Kingdom
FAO
42 individuals

45. 2014

46. Implementation of the CSPA

47. Services being built
Seasonal Adjustment – France, Australia, New Zealand
Confidentiality on the fly – Canada, Australia
Error correction – Italy
SVG Generator – OECD
SDMX transform – OECD
Selecting sample from business register – Netherlands
Editing components – Netherlands
Classification Editor – Norway
By the end of the year we will have several CSPA-compliant services (or components) in use. There are 8 currently being developed. Some in collaboration between organisations, others within individual organisations, but the important point is that they will share common specifications.

48. 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
An “Architecture Working Group” has been set up to oversee the development of these services, and ensure compliance with the agreed architecture principles.
A “Catalogue Team” will develop a repository for the finished services, to make them available to other statistical organisations. This can be seen as the box where the Lego pieces are kept, ready for re-use.

49. Big Data

50. In the last 2 years more information was created than in the whole of the rest of human history!

52. Virtual sprint
Consultation
Guidelines
Task teams
Physical sprint

53. 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

54. “Play is the highest form of research” – Einstein
Sandbox
Irish Centre for High-end Computing / CSO have created a Big Data ‘sandbox’ containing datasets and tools
So that this is not a purely theoretical project, another strand is working on a “sandbox”, to be hosted by the Irish Centre for High-End Computing, in partnership with the CSO. This will provide an environment to test the feasibility of working with Big Data held remotely, even in another country, as well as a chance to test the standards, methods and tools for working with Big Data
“Play is the highest form of research” – Einstein

55. 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

56. How to work together for minimum cost and maximum benefit?
The standards we need for modernisation are developed in a series of global projects overseen by the HLG
How to work together for minimum cost and maximum benefit?

57. The “Sprint” method

58. Virtual meetings We use Webex – others are available Flexibility
Free for participants
Join meetings from office, home, airport etc.
Screen sharing
Virtual whiteboard

59. 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

60. Governance

61. HLG Activities – Engagement Map

62. Get involved! More Information Anyone is welcome to contribute!
HLG Wiki:
www1.unece.org/stat/platform/display/hlgbas
LinkedIn group:
“Business architecture in statistics”