NSI/ISI Statistical software
Issues and a way forward to maximise
re-use and minimise integration efforts
by Andrea Toniolo Staggemeier
Content
• Background
• Case Studies
Data collection
Editing and Imputation
Time Series Analysis
Statistical Disclosure Control
• Proposal for consideration
• Conclusion and next steps
The Big Picture - Today
Business Functions and Operation
Business
Surveys
MLD
Demography
Social
Surveys
Further
Analysis
NeSS
Geography
Corp.
Services
i-Diss
Census
Systems and Datastores
SAS
OpenRoad
Excel
ABF
Ingres
Super
Cross
Uniface
M204
J2EE
Oracle
Notes
Visual
Basic
Foxpro
MS SQL
SPSS
Excel
Foxpro Oracle 7
Clipper
Clipper
Blaise
Citrix
ESRI
Blaise
Infrastructure
Numa
Desktop
ZSeries
PSeries
Wintel
Server
Sun
The Big Picture - 2012
Business Functions and Operation
Business
Surveys
MLD
Administrative
Sources
Demography
Social
Surveys
Multi-Channel
Collection
Further
Analysis
NeSS
Business
Process Mgt
Geography
Corp.
Services
i-Diss
Warehouse
for Analysis
Metadata
Census
i-Dissemination
Systems and Datastores
SAS
OpenRoad
Excel
ABF
Ingres
Super
Cross
M204
J2EE
Oracle
Notes
Visual
Basic
SPSS
MS SQL
Excel
Blaise
Citrix
ESRI
Oracle 7
Infrastructure
Desktop
ZSeries
PSeries
Wintel
Server
Sun
Linux
Aim of this paper
• The paper will discuss the following main concerns:
(1) There is some great work being done within National
Statistical Organisations on specialised statistical software.
This is great software and works very well.
(2) The challenge is that it is hard to predict what the long term
support will be, whether there will be updates for the
software, and how additional functionality can be added to
meet specific requirements.
(3) So the question to be resolved is - how do we turn very high
quality 'unsupported' software into very high quality software
with a real and guaranteed future that we would all be happy
to invest in?
Case study – Blaise for Data collection
• Great for interview based data collection
• Areas where we look for more robust solution
Scalability
Stream line technologies and minimum
dependencies
Serviceability – easy to manage/deploy
Case Study – CANCEIS/Banff for
Editing and Imputation
• Great methodologies
• Areas where we are looking for more robust
solution
Supportability
Serviceability
Integrability (integratability)
Stream line architecture and open APIs
Case Study – X12-ARIMA for Time
Series Analysis
• Rich functionality
• Areas where we are looking for more robust
solution
Compatibility (consistent APIs between versions)
Serviceability (release management transparency)
Case Study – Tau-Argus for Statistical
Disclosure Control
• Rich methodologies
• Areas where we are looking for more robust
solution
License agreement
Support agreement
Open APIs
Better documentation
Proposal for consideration
• Create an IT development community
amongst NSI/ISI(s) interested in making
available statistical services/products.
• Establish a governance agreement which
comprises a sustainable development and
support model for any service made available
to the community.
• Community members should establish a
common development standard.
Principles to be taken into consideration by
community members are:
• 1. Any statistical service should include
enough methods to encompass needs of the
parties of the cooperation
1.1. Be extendable to add new methods
(parties own methodologies)
1.2. Be generalised to fulfil all significant
needs of the parties
Principles to be taken into consideration by
community members are: (Cont.)
• 2. Any statistical service created and made
available by a community member should
also publish full API(s) of the software
enabling better integration.
2.1. when new release developments are
planned the systems should first consider a
SOAP approach
Principles to be taken into consideration by
community members are: (Cont.)
3. Statistical Standards and guides from
international agencies should be use and new
requirements for national standards proposed
should be made public to all participants of
the development community.
Principles to be taken into consideration by
community members are: (Cont.)
4. Common vocabulary, metadata models and
data definitions coherent and consistent at all
statistical value chain building blocks
Principles to be taken into consideration by
community members are: (Cont.)
5. Ensure integrity, confidentiality and security
of systems and data at all times.
Principles to be taken into consideration by
community members are: (Cont.)
6. User access through consistent and easy to
use interfaces and from any appropriate
languages
Principles to be taken into consideration by
community members are: (Cont.)
• 7. Sustainable agreement on maintenance
and cooperation of the developed statistical
services
7.1. Procedure for inclusion of needs of
other parties of the cooperation.
7.2. Assurance of maintenance of the
system (time scope)
7.3. High level support assuring continuity.
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