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Modernising official statistics
- 1. United Nations Economic Commission for Europe Statistical Division United Nations Economic Commission for Europe Statistical Division Introduction to the Initiative Steven Vale UNECE steven.vale@unece.org
- 3. UNECE Statistics: Priorities Population censuses, migration, gender Globalisation, National Accounts, employment, business registers Sustainable development, environmental accounts, climate change Modernisation
- 4. Introducing the HLG-MOS High-level Group for the Modernisation of Official Statistics Created by the CES Bureau in 2010 Strategic vision endorsed by CES in 2011/2012 Annual projects in priority areas Activities are voluntary and demand driven
- 5. Who are the HLG-MOS members? Ireland - Chair Australia Canada Italy Netherlands New Zealand Republic of Korea Slovenia Eurostat OECD UNECE
- 6. Why is the HLG-MOS needed? Before the HLG-MOS Now Many expert groups Clear vision Little coordination Agreed priorities No overall strategy Strategic leadership Limited impact Real progress
- 7. • Launched in 2016 • Open to all statistical organisations who endorse “Statement of Intent” • No fee, but expectation to contribute • Partners benefit from collaboration and sharing • Four main principles: − Openness − Flexibility − Participation − Pragmatism Statistical Modernization Community
- 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?
- 12. The story so far - 2011
- 13. The story so far - 2013
- 14. The story so far - 2015
- 15. CSPA GSBPM GAMSO
- 16. The GSBPM
- 17. What is the GSBPM? Generic Statistical Business Process Model It shows the different steps needed to produce official statistics It provides standard terminology to help statistical organisations • Modernise statistical production processes • Share methods and components
- 18. Why do we need the GSBPM? To define and describe statistical processes in a coherent way To compare and benchmark processes within and between organisations To make better decisions on production systems and organisation of resources
- 19. What will this mean for resources?
- 20. Applicability (1) All activities undertaken by producers of official statistics which result in data outputs All statistical domains National and international statistical organisations
- 21. Applicability (2) Development and maintenance of statistical registers All types of data source: • Surveys / censuses • Administrative sources / register-based statistics • Mixed sources • “Big Data”
- 22. Structure of the Model (1) Process Phases Sub- processes (Descriptions)
- 23. Key features Not a linear model Sub-processes are not followed in a strict order It is a matrix, through which there are many possible paths
- 25. The GSBPM is used by more than 50 statistical organisations worldwide
- 29. KSBPM – Republic of Korea
- 30. Beyond statistics: Data archives Generic Longitudinal Business Process Model
- 31. Uses of the GSBPM Managing statistical programmes Cost / resource allocation Documenting statistical processes Framework for quality assessment Sharing statistical software
- 32. The GAMSO
- 33. What is the GAMSO? Generic Activity Model for Statistical Organisations It extends and complements the GSBPM by adding other activities needed to support statistical production
- 34. Background In the GSBPM, activities not directly part of statistical production are referred to as over- arching processes, and are listed, but not elaborated in detail There have been several calls to expand the GSBPM to better cover these activities. The GAMSO was developed to meet these needs
- 35. Who created the GAMSO? The business activity model developed by the Statistical Network provides the basis for over 70% of the content of GAMSO. The Modernisation Committee on Standards further developed GAMSO, taking account of comments from a public consultation GAMSO v1.0 was approved by HLG in March 2015.
- 36. Level 1 of the GAMSO
- 37. Capabilities A capability is an ability that an organisation, person, or system possesses Methods People Standards and Frameworks Processes Technology
- 38. Uses of GAMSO Resource planning Measuring costs Assessing readiness to implement different aspects of modernisation Supporting risk management systems Implementing enterprise architecture Measuring and communicating the value of statistical modernisation activities
- 39. Maintaining GSBPM & GAMSO Owner = High-Level Group Maintenance is delegated to the Modernisation Committee on Standards Discussion forums to gather feedback Importance of stability over time • Reviews every 5 years • Revisions only if really needed
- 41. What is the GSIM? A reference framework of information objects: • Definitions • Attributes • Relationships It gives us standard terminology GSIM aligns with relevant standards such as DDI and SDMX
- 42. GSIM and GSBPM GSIM describes the information objects and flows within the statistical business process.
- 45. Clickable GSIM
- 48. What is the CSPA? • A template architecture for official statistics • A set of standard specifications for new statistical components (services) that can be used in a modular way • A new way of developing statistical tools, with sharability as a design feature, not an afterthought
- 49. Problem statement: Specialised business processes, methods and IT systems for each survey / output
- 51. ... but if each statistical organisation works by themselves ...
- 52. ... we get this ...
- 53. .. which makes it hard to share and reuse!
- 54. … but if statistical organisations work together to define a common statistical production architecture ...
- 55. ... sharing is easier!
- 56. We all have to modernise our statistical production systems The marginal cost of doing this in a way that supports collaboration and complies with CSPA is relatively low, but the potential savings from such a standard approach are high Key Message
- 57. Get involved! Anyone is welcome to contribute! More Information HLG-MOS Wiki: www1.unece.org/stat/platform/display/hlgbas LinkedIn group: “Modernising official statistics”
Editor's Notes
- Vision An active CSPA community allows statistical organisations to contribute towards achieving a shared goal. Individual organisations retain control over the nature of their own contributions with the support of a global community to create a robust and scalable business driven statistical production platform. The role of HLG is to provide stewardship and to assist in steering the community to deliver on the shared goal in an efficient manner which recognises the right of individual organisations to determine their own contributions based on their own priorities. HLG are the holders of the vision and they are influential supporters of the work done to help realise this goal.
- 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
- The Generic Statistical Business Process Model (GSBPM) was released in 2009 and revised in 2013. The Generic Statistical Information Model (GSIM) was released in 2012 and revised in 2013 Between them, they provide the basis for the Common Statistical Production Architecture, also released in 2013.
- 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.
- For example; Ireland put price on each box to see the cost , compare with other organisations It is about efficiency, compare the different processes Could use for example to compare the production of population data or Rand D statistics.
- What does this mean for our Resources? A change in the effort required for some of the GSBPM activities Less effort on collect and process activities More time and effort focussed on higher value analytical work
- Developed by the Data archives community. Adjusted our model We link to this community through the Data Documentation Initiative (DDI)
- In 2014, the members of the Statistical Network were the national statistical organisations in Australia, Canada, Italy, New Zealand, Norway, Sweden and the United Kingdom. Feedback was received from Australia, Canada, Czech Republic, Italy, Mexico, Norway, Poland, Russia, Sweden, Turkey, Eurostat and OECD. Members of the GAMSO revision group were: Jenny Linnerud (Norway), Eva Holm (Sweden), Daniel Gillman (USA), Juan Munoz (Mexico), Klas Blomqvist (Sweden), Luca.Gramaglia (Eurostat) and Jean-Marc Museux (Eurostat) HLG – High Level Group for the Modernisation of Statistical Production and Services
- GSIM is an umbrella It is a conceptual model, not an implementation model. It doesn’t compete with DDI and SDMX but provides a generic additional layer. GSIM is not directly tied to any particular implementation standard or technology solution.
- Statistical organizations already participate in many international engagement activities that facilitate sharing. Spontaneous, relationship driven engagements between statistical organisations, unable to articulate how efficient we have been The main opportunities that we can focus on: greater sharing of each statistical organisation's ICT portfolio investment and management plans finding and exploiting opportunities to align engagements with some vendors earlier collaboration on innovative and transformative approaches leverage modern technology development practices and tools to support physically dispersed teams What does CSPA offer to sharing? It broadens the types of sharing to smaller parts of IT solutions rather than the large grained, complex pieces that have historically been shared It builds on and enhances sharing of non-ICT dimensions of a capability Sharing of any dimension of a capability can occur with different degrees of maturity. With more maturity in sharing comes: more confidence in the sustainability of things being shared more clarity in roles of providers and consumers of the thing being shared greater visibility of the status of things being shared.