1. Thérèse Lalor Statistical Management and Modernisation Unit
United Nations Economic Commission for Europe

2. Introducing
An initiative of the High-level Group for the Modernisation of Official Statistics
Heads of 13 statistical organisations
Annual projects in priority areas
Expert groups and task teams
Activities are voluntary and demand driven

3. Who are the HLG-MOS members?
Ireland - Chair
Republic of Korea
Australia
Slovenia
Canada
United Kingdom
Italy
Eurostat
Mexico
OECD
Netherlands
UNECE
New Zealand

4. Statistical Modernization Community
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
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.

5. Governance Structure Since 20171
The role of the new groups in this structure can be summarised as follows:
 Blue-skies thinking network – This is an exciting new initiative to create the “ideas factory” for statistical modernization. The network provides a research and innovation environment where members share ideas and look for partners to explore the potential benefits for statistical organizations.
 Capabilities and communication – This group is responsible for the human resources, training and organizational aspects of modernization, as well as communicating with the official statistics community about modernization activities.
 Supporting standards – This group maintains key modernization standards such as the Generic Statistical Business Process Model (GSBPM), the Generic Statistical Information Model (GSIM) and others, as well as providing supporting materials to help implementers.
 Sharing tools – This group is responsible for implementing the Common Statistical Production Architecture (CSPA), designing and building statistical software according to common principles, so it can be re-used more easily and cheaply.

6. Modernstats Models and Frameworks
The mission of HLG-MOS is to oversee development of frameworks, and sharing of information, tools and methods, which support the modernization of statistical organizations.
These models and frameworks are cross-cutting, supporting the modernisation of all types of statistical production.
The mission of HLG-MOS is to oversee development of frameworks, and sharing of information, tools and methods, which support the modernization of statistical organizations. A number of frameworks and models have been developed and endorsed by the HLG-MOS over the last five years.
These frameworks and models include the Generic Statistical Business Process Model (GSBPM), the Generic Statistical Information Model (GSIM), the Common Statistical Production Architecture (CSPA), and the Generic Activity Model for Statistical Organisations (GAMSO). These models are being implemented and used by national and international statistical organisations.
The work of all bodies created under the Conference and its Bureau is regularly reported to the UNECE Executive Committee. A report on the achievements of the HLG –MOS, as well as a request to extend its mandate for another 5 years will be presented at the end of In view of this, the Conference is invited to endorse the frameworks and models as outputs of HLG-MOS.

7. Generic Statistical Business Process Model (GSBPM)
It defines and describes the processes needed to produce official statistics.
The GSBPM is used by more than 50 statistical organisations worldwide.

8. Generic Activity Model for Statistical Organisations (GAMSO)
GAMSO extends and complements the GSBPM by adding other activities needed to support statistical production

9. Generic Statistical Information Model (GSIM)
GSIM describes the information objects and flows within the statistical business process. It can use be used to consistently design processes and to more easily share software between statistical organisations

10. the Future of Statistical Production
CSPA
the Future of Statistical Production

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

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

13. ... we get this ...

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

15. … but if statistical organisations work together to define a common statistical production architecture sharing is easier!

16. Common Statistical Production Architecture (CSPA)
The CSPA has been developed to support the sharing and re- use of tools across statistical domains and between statistical organisations.
It provides a blue-print for a new way of designing, building and implementing the tools needed to produce official statistics. 

17. Help to use the standards

18. Modernisation Maturity Model (MMM)
The MMM and its Roadmap focus on how to build organisational capabilities through implementation of the models and standards identified as key to statistical modernisation i.e. GSBPM, GAMSO, GSIM and CSPA.
The MMM allows statistical organisations to evaluate their current level of maturity against a standard framework, while the Roadmap provides clear guidelines on the steps to take to reach higher levels of organisational maturity more quickly and efficiently. The roadmap includes supporting instruments to help statistical organisations, at different maturity levels, to implement the different standards.
The MMM and its Roadmap should help any organisation regardless of their level. They acknowledge that within one statistical organisation there can be different maturity levels depending on the statistical domain or part of the organisation. The Roadmap addresses needs expressed by statistical organisations, particularly those in the earlier stages of modernisation, to have clearer information about how to progress in the most efficient way

19. Strategic Investment Planning
Sharing plans between organisations
Finding partners with similar priorities
Surveys:
Pilot 2015
Full 2016 – 23 responses, 282 “investments”
2017 survey released in July

20. Projects for 2017 Data Integration Data Architecture Project
Complete the guidelines
Practical experiments with different data sources
Data Architecture Project
How to manage increasingly diverse data types within a statistical organisation
Following the success of the Data Integration Project in 2016, the project was supported to be continued in The project will focus on completing the Data Integration Guidelines. To ensure that there is no duplication or effort and to include as much practical experience as possible, the project will be sending out a questionnaire in July to gather country experiences in data integration.
The Data Architecture Project will create a reference data architecture which focuses on the functionality that NSO’s will need for the design, integration, production and dissemination of official statistics based on both traditional and new types of data sources. The project has already released the first draft of the architecture!

21. Data Architecture (CSDA): where we are today June 2017
Carlo Vaccari, project leader
Dick Woensdregt, project lead architect

22. Purpose and Scope
Data is an important asset for statistical organisations
Various aspects:
Determining the meaning and therefore (potential) value of data
Usability: is it in a useable form?
Governance & security
The project assignment (Nov 2016 Workshop) talks about “data architecture”, “external data sources and off-site data” and “semantic interoperability and metadata management”
Scope: GSBPM phases 1 thru 7

23. Deliverables Reference Architecture Guidelines Use-cases
All due by the end of 2017.
A first draft of the Reference Architecture was created during the May sprint and is available for review on the project public wiki.

24. Users and usage

25. Capabilities Defining:
Core capabilities (Ingestion, Transformation, Integration, Access & Consumption)
Cross-cutting capabilities (Metadata Management, Data Governance, Provenance & Lineage, Security & Authorization)
Building Blocks (Conceptual and Logical) are used to realize/implement Capabilities
Architecture valid for different Categories of data
Semantic Layer to share the “meaning” of different data

26. Capabilities & Building Blocks

27. How to get involved
We welcome your input. If you want to get involved in the Data Architecture project or give us your comments or input, please contact
Carlo Vaccari at
Dick Woensdregt at
.. Or contact us at this conference.
… we love to talk to you!

28. Other HLG work

29. Capabilities and Communication
How best to bring about the organizational changes necessary to support modernisation in statistical organizations…
Communication
Risk Management
Capabilities Training

30. New work on Geospatial Standards
This work looks at how statistical and geospatial organizations can work together.
An important first step is to help each community understand each other.
Both communities have many frameworks and standards, which need to be brought together.
Workshop on Geospatial standards, Stockholm, 6-8 November 2017

31. Blue Skies Thinking Network
An initiative to create the “ideas factory” for statistical modernization.
The network provides a research and innovation environment where members share ideas and look for partners to explore the potential benefits for statistical organizations.

32. “Telling stories with SDG data" Hackathon
A 3 day virtual hackathon will be held September 2017.
Problem statement: “Create a user-oriented product that puts youth data in context ”
Teams will be given SDG data related to the theme and challenged to create a product using it.

33. Chief Statistician Sprint: Innovation
Modernisation is not a destination, it is an ongoing process within a statistical organisation.
1 day event before the HLG-MOS Workshop (21 November), targeted at Chief Statisticians and senior managers
A few thought provoking presentations combined with small group discussions on how statistical offices continue to evolve.
A chance to discuss and share ideas with colleagues
Following the success of the Seminar in Korea, the HLG will organize a 1 day session on 21 November. Possible topics covered include:
·     How address culture behaviours,
·     How to create agile organisations,
·     How to empower teams‎
·     How do we make our work count? How do we move forward? How do we move to the next level?
·     How to attract and retain skilled staff members?
·     How to promote and elevate research and innovation to the level of Chief Statisticians?

34. Get involved! More Information Anyone is welcome to contribute!
HLG-MOS Wiki:
www1.unece.org/stat/platform/display/hlgbas
LinkedIn group:
“Modernising official statistics”

36. What did HLG MOS ever do for us?
Apart from
Giving us GSPBM, GSIM, GAMSO & CSPA which provide the road-map for modernising organisations – a starting point
Big data sandbox, Generic skills profile for data scientists, Data integration guidelines, Generic data editing models, Guidelines for Managers
A new flexible approach to international collaboration that provides tangible outputs
A forum that gives innovation and blue skies thinking a framework within which to flourish
Organisational structures built on GSBPM and GAMSO
Internal services (e.g. technology, quality frameworks, metadata architectures) increasingly built around GSBPM