1. IT Directors Meeting November 2012

2. Background A Business case for change
Common challenges facing National Statistical organisations
Limited funding
Full work programs
Aging infrastructure
Rapidly changing information industry
The Statistical Network formed in June 2010
Background - Paris Workshop
The formation of the Statistical Network (SN) evolved from an informal group of countries who responded with interest to a paper presented to CSTAT by the Australian Statistician in 2009.
A business case for change was presented outlining common challenges facing National Statistical organisations, including limited funding, full work programs, aging infrastructure and the rapidly changing information industry. We could argue today these challenges have increased in complexity. In particular, the pace of the digitised world.
As a result, a workshop was held with the interested countries ( Norway, Sweden, UK, Canada, New Zealand and Australia) suggesting that the collaborative workgroup could be a major catalyst for responding to these challenges.
The international workgroup provided an opportunity for organisations to not only consider individual agency development and lifecycle costing, but a possibility for joint agency development and lifecycle of costing for some future projects.

3. Current Membership
Australia
Canada
Italy (joined June 2012)
New Zealand
Norway
Sweden UK

4. Statistical Network Goal and Purpose
SN Critical Goal: “Harmonising statistical methods, systems and capability across national statistical agencies”
SN Purpose:
Working together
with pace and passion
to better meet
our societies’ information needs
while driving down costs
Refresh in peoples minds the Statistical Network Goal, Purpose.
The Purpose includes to explore a collaboration approach in striving for improvement in Statistical Information Management. The Statistical Network brings together interested NSI’s that have experience in harmonising production environments as well as expertise in relevant fields.

5. Chief Statisticians Expectations
Key standards must be agreed
Dialogue should address common needs
Collaboration should leverage resources
Renewal should be fundamental
Start small, learn actively and grow
The Chief Statisticians set the following expectations between member countries.
List the dot points….
A key priority for the network is knowledge sharing; recognising and harnessing opportunities to work together to achieve mutual benefits and look for ways to help drive down the costs of statistical production.

6. Key lessons learnt Purpose, scope and level of collaboration
Different approaches to project management and governance
Working in an international team
Some projects have accelerated more quickly
Lessons from early collaboration efforts can be grouped into three main categories:
Purpose, scope and level of collaboration: opportunities identified were at first defined at the high level and it was expected that project teams would further define and prioritise scope in order to successfully deliver the outputs. It took a long time for some teams to understand, refine and agree to the scope at the practical implementation level.
Different approaches to project management and governance: NSIs use different approaches to project management and the “light touch” project governance model, initially agreed in the inaugural workshop did not provide enough visibility of project’s issues. The Network has aligned project management templates and governance arrangements to strengthen its position and facilitate issues resolution. Standardised project management practices and regular reporting to the Network Steering Committee is proving more successful.
3. Working in an international team at first proved more difficult than expected. Face to face time for project teams is extremely important. Network members work hard to find and create opportunities for project teams to meet face to face to progress work faster which in combination with virtual meetings increases productivity and shortens the time to deliver outputs.
4. Some projects have accelerated more quickly - Some projects have worked better than others: projects related to conceptual frameworks (e.g., GSIM) and methodology (e.g., editing) lend themselves more easily to international collaboration since there is a common need for standards and frameworks in statistical systems, and they are not hampered by issues of intellectual property and common IT platforms and SW.
The Networks next challenge is to find opportunities to test these the frameworks in a practical way.

7. Forward Work Program Alignment with HLG-BAS priorities GSIM
Innovation in Dissemination
New opportunities
Administrative Data Quality
Business Architecture
Platform for Collections
Enterprise Data Warehousing
It is important to note the Statistical Network does not have a central funding source unlike ESSnet. Nor does it have central legislative frameworks. Resources are funded from each participating Country in the project.
The Network is considering how best to align with HLG-BAS and be practical in its efforts to help modernise the production of statistics. How we bring it all together which I will talk about in a little more detail in the next slide.
Current Projects
GSIM – originally a “Statistical Network” Project, now governed by HLG-BAS. The HLG-BAS saw the GSIM project as an important corner stone of reaching their goal of modernisation statistical production. The Statistical Network has some representation on the HLG-BAS governing board for GSIM.
Innovation in Dissemination – is progressing well.
Key highlights
SNZ and ABS are actively using .Stat and specifying improvements to the product. Statistics Canada have also loaded .Stat into their environment and they are currently doing a formal review of it.
There has also been some good work on search engine optimisation (SEO) and each agency is now implementing SEO in their sites (all have different web infrastructures).
Social media policy has been shared amongst countries as part of the Statistical communications stream of the project.
There are a number of “New opportunities” members are currently working together to produce business cases for:
Administrative Data Quality
Business Architecture
Platform for Collections
Enterprise Data Warehouse
These will be considered by the Network Steering Committee in the new Year to assess the feasibility and value to network members in forming collaborative projects.

8. Where are we up to? Bringing it all together
Generalized Statistical Production System
Conceptual
GSBPM
GSIM
Common Generic
Statistics Production
It was recognised early in the development of GSIM that most of the strategic benefits would not be realised by simply agreeing on GSIM, and having staff in statistical agencies then adopt that common terminology. The business case for GSIM was contingent on the intent to then harness GSIM, and GSBPM, to move forward and achieve standardized generic production of statistics in a practical sense.
GSIM and GSBPM would provide a necessary, but not sufficient, basis for moving forward in practice on a consistent basis. Further agreement would be required, for example, on architectural design standards, on implementation standards, on “leading practice” for design and reuse.
Agreement on common terms and concepts at the level of GSBPM and GSIM will expedite achieving agreement at more practical and detailed levels.
The idea is that applying the agreed frameworks, standards and practices it is possible to move forward and achieve standardized generic production of statistics in a practical sense. Over time, a Generalized Statistical Production System will be realized in practice.
This diagram was originally created at GSIM Sprint 1 in February this year. The concept it illustrates is essential and supported by HLG-BAS.
The title proposed originally for the concept was “Grand Unification”.
The title is not so widely agreed. We are seeking integrated architectural guidelines, practice guidelines, implementation standards and so on which allow us to progress toward realizing a Generalized Statistical Production System in practice. This is not a analogous to, for example, the Grand Unified Theory in Physics which was first proposed in 1974 and is yet to be generally accepted as a reality 38 years later.
The HLG-BAS Strategy identifies the need to develop common approach and language for process standardisation based on the GSBPM and the GSIM, together with common methodologies and tools. In other words, it identifies that, as a community, we need to agree and share common “process design” methodologies and tools which will help us standardise the design of statistical business processes. This is additional, and complementary, to the goal that those processes, once designed, will use standard components that apply standard methods to the production of official statistics.
The HLG-BAS Strategy identifies the next step toward this:
Create a universal statistical "plug and play" architecture to facilitate collaborative development, reuse and shared processing between organisations and countries in a way that is independent of technical platforms.
It is possible to further evolve the diagram to portray how “plug and play” architecture might be enabled.
Practical
Methods
Technology

9. Expanding on the diagram
Conceptual
GSIM
GSBPM
GSIM
GSBPM
GSIM
informs
informs
informs
Service Inputs
Service
Service Outputs
Methods
Technology
Service defined by methods and business need
Standards Based
e.g. DDI, SDMX
enables
business process
In this diagram, the goal of achieving a Generalised Statistical Production System is realized through defining services which align with business needs (eg to perform activities described within GSBPM).
By definition, services support well delineated business functions and can be reused in different processes. In many cases services are delivered via software components.
In this case the services accept inputs and produce outputs whose definition is consistent with GSIM and which can be represented in practice using standards such as SDMX and DDI.
As the “service interfaces” are designed on a consistent basis, it is possible to assemble selections of services in a flexible manner to meet business needs. It is easy at a later time to “unplug” use of an existing service/component within a statistical business process and “plug in and play” a new service/component which offers improved quality (which may include improved performance and/or reduced cost).
The centre of the diagram illustrates the value of a service oriented approach when seeking to realize a Generalised Statistical Production System. A service oriented approach requires well defined business architecture. Services are more likely to be shared and reused if they support well defined, commonly recognised business needs.
Service oriented architecture (SOA) is driven by a set of design principles. It is not prescriptive in terms of technologies to be used. For example, web services are often chosen as a means to implement solutions, but their use is not mandated.
The “plug and play” paradigm that members of HLG-BAS are seeking can be defined and supported using SOA concepts and techniques.
We can start with an existing framework for describing how services exchange information with their environment. The framework is CORE.
These are some of the challenges the Statistical Network faces.
Practical
Generalised Statistical Production System 9

10. Expanding on the diagram
Conceptual
SN Business Architecture
GSIM
GSBPM
GSIM
GSBPM
GSIM
informs
informs
informs
Service Inputs
Service
Service Outputs
Methods
Technology
SN Enterprise Data Warehouse
Service defined by methods and business need
Standards Based
e.g. DDI, SDMX
enables
business process
In this diagram, the goal of achieving a Generalised Statistical Production System is realized through defining services which align with business needs (eg to perform activities described within GSBPM).
By definition, services support well delineated business functions and can be reused in different processes. In many cases services are delivered via software components.
In this case the services accept inputs and produce outputs whose definition is consistent with GSIM and which can be represented in practice using standards such as SDMX and DDI.
As the “service interfaces” are designed on a consistent basis, it is possible to assemble selections of services in a flexible manner to meet business needs. It is easy at a later time to “unplug” use of an existing service/component within a statistical business process and “plug in and play” a new service/component which offers improved quality (which may include improved performance and/or reduced cost).
The centre of the diagram illustrates the value of a service oriented approach when seeking to realize a Generalised Statistical Production System. A service oriented approach requires well defined business architecture. Services are more likely to be shared and reused if they support well defined, commonly recognised business needs.
Service oriented architecture (SOA) is driven by a set of design principles. It is not prescriptive in terms of technologies to be used. For example, web services are often chosen as a means to implement solutions, but their use is not mandated.
The “plug and play” paradigm that members of HLG-BAS are seeking can be defined and supported using SOA concepts and techniques.
We can start with an existing framework for describing how services exchange information with their environment. The framework is CORE.
The orange bubbles are SN projects, where you can see they are at three different levels of development as shown in the slide. The SN Business Architecture project is in the top level (expanding on GSBPM into the business activities/capabilities), work on EDW is at the second level, and work on admin data, dissemination and data collection are examples of development at statistical processing system level).
These are some of the challenges the Statistical Network faces.
SN Admin Data Quality
SN Innovation in Dissemination
SN Platforms for Data Collection
Practical
Generalised Statistical Production System