1. SISAI STATISTICAL INFORMATION SYSTEMS ARCHITECTURE AND INTEGRATION
WORKING GROUP 3rdMEETING MAY 2013 ITEM Modernising Statistical Production and Services

2. Modernising Statistical Production and Services Thérèse Lalor UNECE

3. Introducing UNECE Statistics

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

5. Why is modernisation important?

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

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

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

10. Statistics fights back!
High Level Group for the Modernisation of Statistical Production and Services
Created by the Conference of European Statisticians in 2010
10 heads of national and international statistical organisations

11. A strategy for modernisation
Transform vision to reality
New sources and products
Streamlined processes
Managing organisational change to support modernisation and collaboration

12. A strategy for modernisation
Transform vision to reality
New sources and products
Streamlined processes
Managing organisational change to support modernisation and collaboration
Conference of European Statisticians, June 2012
Endorsed

14. 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?

16. Introducing the GSBPM

17. 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 17

18. The GSBPM is used by more than 50 statistical organizations
worldwide

19. Beyond statistics: Data archives
Generic Longitudinal Business Process Model
26 May 2019

22. Information objects Things that flow between GSBPM sub-processes
Things that drive and integrate sub-processes

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

24. GSIM and GSBPM-processes
GSIM object structures
(formats)
GSBPM -process
GSIM object instances

25. So what is GSIM? GSIM gives us standard terminology
A reference framework of information objects:
Definitions
Attributes
Relationships
GSIM aligns with relevant standards such as DDI and SDMX
GSIM gives us standard
terminology

26. Production Business Concepts Structures Statistical Program
Production Activity
Process Input
Process Method
Statistical Program Design
Process Step
Production
Dissemination Activity
Acquisition Activity
Rule
Process Output
Business
Concepts
Data Set
Data Point
Variable
Population
Data Structure
Structures
Concept
Data Resource
Product
Unit
Classification

27. It is a new way of thinking for statistical organizations
2727
The conceptual model is explicitly chosen to be independent of design or implementation concerns,
The aim of a conceptual model is to express the meaning of terms and concepts used by domain experts to discuss the problem, and to find the correct relationships between different concepts.
The conceptual model attempts to clarify the meaning of various, usually ambiguous terms, and ensure that problems with different interpretations of the terms and concepts cannot occur.
Such differing interpretations could easily cause confusion amongst stakeholders, especially those responsible for designing and implementing a solution, where the conceptual model provides a key artifact of business understanding and clarity.
Once the domain concepts have been modeled, the model becomes a stable basis for subsequent development of applications in the domain. The concepts of the conceptual model can be mapped into physical design or implementation constructs
It is a new way of thinking for statistical organizations

28. GSIM documentation
There are many layers to the GSIM documentation!

30. GSIM: The “sprint’ approach
Locked people in rooms
3030
GSIM: The “sprint’ approach
The HLG decided to accelerate the development of the GSIM using an Agile approach.
Sprints
Collaboration of multi-disciplinary experts
A “time-boxed” period of work, and
A closely defined and agreed output

31. GSIM: The “sprint’ approach
3131
Locked people in rooms
GSIM: The “sprint’ approach
Sprint 1 – Slovenia, February 2012
Sprint 2 – Republic of Korea, April 2012
Integration Workshop, Netherlands, September 2012

32. Developing GSIM
113 person weeks
3 “Sprint” sessions

34. What next?
2 big projects for 2013

36. Implementing GSIM

37. GSIM Map GSIM to SDMX and DDI Conceptual model DDI SDMX Implementation
Other relevant standards
DDI
SDMX
GSIM
Conceptual model
Implementation
standards

38. Reviewing / revising GSBPM and GSIM

39. Geospatial Standards
Task team to investigate role geospatial standards play in the modernisation of official statistics

40. Get involved! Anyone is welcome to contribute to this work.
Contact: Thérèse Lalor
There are lots of resources on the wiki for you to use:

42. The problem
2 big barriers that hinder modernisation of statistical organisations are:
Rigid processes and methods; and
Inflexible and ageing technology environment.

43. NSI 1
Collect
Process
Analyse
Disseminate
Survey A
Survey B
Historically statistical organisations have produced specialised business processes and IT systems

44. Now we have an agreed conceptual basis to move forward on, we can focus on “How To”

45. Desired Project Outcomes
Increased:
interoperability in Official Statistics through the sharing of processes and components
ability to find real/genuine collaboration opportunities
ability to make international decisions and investments
sharing of architectural/design
knowledge and practices

46. How does Architecture help?
Many statistical organisations are modernising and transforming using Enterprise Architecture
Enterprise architecture shows what the business needs are, where the organisation wants to be and aligns the IT strategy to this.
It can help to remove silos and improve collaboration across an organisation.

47. EA helps you get to this NSI 1 Survey A Survey B Survey C Collect
Process
Analyse
Survey A
Survey B
Survey C
Disseminate

48. …but if each statistical organisation works by themselves…..

49. …we get this….
Canada
Collect
Process
Analyse
Disseminate
Sweden

50. This makes it hard to share and reuse!
Canada
Collect
Process
Analyse
Disseminate
Sweden ?

51. …but if statistical organisations works together?

52. Common Statistical Production Architecture
An industry architecture will make it easier for each organisation to standardise and combine the components of statistical production, regardless of where the statistical services are built

53. An industry architecture is:
“set of agreed common principles and standards designed to promote interoperability in an industry”
“an architecture template for statistical production”
“common vocabulary to discuss implementations”
“enables the vision and strategy of an industry, showing a clear picture of how to get there”

54. It has 4 layers Business Architecture Information Architecture
Application Architecture
Technology Architecture

55. Based on Service Oriented Architecture
A Statistical Service:
is a representation of a business activity with a specified outcome
is self contained
performs a task in a business process
can be reused in a number of business processes

56. Based on Service Oriented Architecture
Project is sometimes called “plug and play”
Statistical service is the pluggable bit
The idea is that installing a new statistical service should be easy

57. This makes it easier to share and reuse!
Collect
Process
Analyse
Disseminate ?
Sweden
Canada

58. Wrapped legacy/existing
Lego pieces could be:
Wrapped legacy/existing
Brand new

59. Architecture Sprint!

60. Open for public review for 3 weeks
CSPA v0.1 released 22 April
Open for public review for 3 weeks

61. Design Artefacts

63. Quality Attributes
GSIM –
Compliance
GSBPM –
Compliance
Statistical Service
Architectural Pattern

64. GSIM and GSBPM-processes
GSIM object structures
(formats)
GSBPM -process
GSIM object instances

65. Quality Attributes
Statistical Service

66. Statistical Service
Architectural Pattern

67. User Stories Architecture gives an industry strategy Collaborating
Gives common To Be state for statistical organisations
Helps statistical organisations to transition
Aligns roadmaps to achieve future industry vision
International investment
Catalogues show what statistical services exists
Genuine collaboration opportunities
Collaborating
Adapting an existing statistical service or designing new ones
Hosting/Deploying services available to all organisations
Influencing vendors to align to industry requirements

68. Proof of Concept Demonstrate the process of working together
Advantages in cooperation
Demonstrate business viability to senior management
There are benefits to pursuing this
Show it is feasible
Give energy to idea
Prove the value of the Architecture
Here is something that we could not do before

69. NSI-Specific communication platform
E-form
Service ?
Coding
Service
E-form
Service
Editing
Service?
E-form
Service
Coding
Service
Editing
Service? ?
Repository
NSI-Specific communication platform

70. Project Timeline to end June
Strand 1: Architecture
Pre Sprint Architecture discussions
Revisions to Architecture based on feedback
Collect feedback of Sprint Output
Advise on PoC work
18 – 24
March
25 – 31
March
1 – 7
April
8 – 14
April
SPRINT
15 – 21
April
22 –28
April
29 April – 4 May
5 – 11
May
12– 18
May
19 – 25
May
26 May – 1 June
2 – 8
June
9 – 15
June
SPRINT
16 – 22
June
23 – 30
June
Teams for Business and IT requirements for PoC Sprint
Virtual PoC work
Negotiating for licences for PoC
Strand 2: Proof of Concept

71. Get involved! Anyone is welcome to contribute to this work.
Contact: Thérèse Lalor
CSPA v0.1 is out for public review until 17 May: