High-Level Group for the Modernisation of Statistical Production and Services

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Evangeline Horton
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Modernisation of Statistical Production and Services

1 United Nations Economic Commission for Europe Statistical Division High-Level Group for the Modernisation of Statistical Production and Services Steven Vale UNECE Matjaž Jug UNECE

2 Introducing UNECE Statistics

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

4 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

5 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)

6 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

7 Why is the HLG needed? Before the HLG Many expert groups Little coordination No overall strategy Limited impact Now Clear vision Agreed priorities Strategic leadership Real progress

8 New competitors & changing expectations Increasing cost & difficulty of acquiring data Riding the big data wave Rapid changes in the environment Reducing budget Competition for skilled resources

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

10 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?

11 Governance

12 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

13 HLG Projects 30+ Expert Groups

14 2012

16 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

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

20 2013

22 Standards-based Modernisaton

23 Outcomes

24 Outcomes

25 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

26 Mappings Fundamental Principles of Official Statistics

27 Frameworks and Standards Project

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

30 Applying Enterprise Architecture Disseminate

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

32 ... we get this...

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

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

35 ... sharing is easier!

36 CSPA development project Architecture Proof of Concept

37 The sprint method

38 The Proof of Concept 5 countries played the role of Builders Editrules CANCEIS Blaise G Code SCS 3 countries played the role of Assemblers

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

40 2 Sprints 5 Build teams 3 Assemble teams United Kingdom 1 Working Group FAO 42 individuals

41 2014

42 Implementation of the CSPA

Services being built 1. Seasonal Adjustment France, Australia, New Zealand 2. Confidentialized Analysis Canada, Australia 3. Error correction Italy 4. Publication graph generating engine OECD 5.

43 Services being built 1. Seasonal Adjustment France, Australia, New Zealand 2. Confidentialized Analysis Canada, Australia 3. Error correction Italy 4. Publication graph generating engine OECD 5. Data Set transformation OECD 6. Selecting sample from business register Netherlands 7. Linear Error Localisation Netherlands 8. Linear Rule Checking Netherlands 9. List Statistical Classifications Norway 10. Retrieve Statistical Classifications Norway

Australia, Canada, Italy, Hungary, New Zealand, Romania, Turkey,

44 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 More than 50 architects, developers and other experts from 14 countries

45 Big Data

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

48 Virtual sprint Consultation Guidelines Task teams Physical sprint

49 Virtual Sprint March 2 hour web conference every day for 4 days + 2 hours per day of offline work. 13 participants Australia, Canada, Ireland, Italy, Mexico, Netherlands, New Zealand, UK, US, Eurostat, OECD Result: Paper on key issues

50 Physical Sprint 2 3 April, in Rome, hosted by Istat 15 participants Australia, Austria, Canada, Italy, Mexico, Netherlands, Poland, Sweden, United Kingdom, United States, Eurostat, UNECE

51 Next Steps Key challenges: 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 with more than 65 experts from 20 countries established to tackle these issues Reports / guidelines by the end of 2014

52 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

53 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 using various datasets (social networks, web scraping, smart meters, traffic cameras, mobile phone data ) Build an international collaboration community on the use of Big Data

54 How to work together for minimum cost and maximum benefit?

55 HLG Activities Engagement Map

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

outside the Asia Pacific region The Challenges

Modernisation work outside the Asia Pacific region The Challenges 1 These challenges are too big for statistical organisations to tackle on their own We need to work together High Level Group for the Modernisation