Transcript A Collaborative Approach to Agile statistical Processing Architecture ABS Experience and Aspirations Meeting on the Management of Statistical Information Systems (MSIS 2010) 26-29 April 2010 Daejeon,

A Collaborative Approach to Agile statistical
Processing Architecture
ABS Experience and Aspirations
Meeting on the Management of Statistical
Information Systems (MSIS 2010)
26-29 April 2010
Daejeon, Republic of Korea
Brian Studman
IT Director, information Management Transformation Program
Topics
• Learning from Experience - the
threads of process, metadata,
architecture and collaboration
• ABS revised Architecture and
Collaboration
• Aspiration - The Information
Management Transformation
Program
• < 1980s – mainframe, subset of
metadata used consistently,
'generalised' software lessons
• 1980-89 – PCs & client-server
innovation chaos
• 1990-99 - standard process & process
control, output warehouse
(specialising output & dissemination
processing), Web site constant
innovation, KM environment
• 2000-2009 – data capture
specialisation (BSIP), ISHS2, external
view through NSS & collaboration
Architecture Lessons
• Lack of low-level content
• 'Posters' become dated too easily
• Business ownership of architecture
flagged
• Architecture governance did not
influence early enough in project
• Translation from architecture into
high-level design didn't consistently
occur
Need more in
this area
Collaboration Lessons
•
Mutual value
proposition
•
Multi-language
issue – plan/design
•
1+ solution is fine
•
•
Common standards
and architecture
Senior executive
'champions'
•
Incremental
delivery – agile
process
•
Genuine commitment
•
IP, licencing, ongoing support,
•
technology & project
issues – plan to
•
manage and manage
•
•
'Lead' organisation
for projects
Mutual respect &
attention to detail
Out-posting staff
Building
New
Business
Needs
e.g. Concepts,
papers, ideas,
standards,
designs,
architecture
Offers
to
Share
Existing
e.g. Blaise,
PC-Axis,
.stat
Sharing
Highest
Value
Region
e.g. Blaise
& PC-Axis
extensions
Collaboration
IMTP
•
About business
•
agility for complex
information solutions
•
Usual business
drivers: efficiency
•
Enabled by whole-ofGSBPM metadata
•
management
(DDI+SDMX)
•
•
Plus BPMS+SOA+... •
•
Practically further
international
collaboration
•
Heavy emphasis on
metadata standards,
'active' metadata
Paradata separated as
a business value
element
Aligning with other
international work e.g.
SAB & CORA
Focussing on NSI
operations
Merging: Process,
metadata, architecture
& collaboration
SDMX
DDI
A g g r e g ate d d a ta
Micr odata
Indicator s, Time Ser ies
Acr oss time
Acr oss geogr aphy
Open Access
Easy to use
Low level obser vations
Single time per iod
Single geogr aphy
Controlled access
Exper t Audience
From Process to Workflow
Pragmatic (ABS internal) IMTP
DE R
C LA M S 2
Q DT
1
Specify Needs
2
Design
1.1
D etermine
needs for
information
2.1
D esign
outputs
1.2
Consult &
confirm
needs
2.2
D esign variable
descriptions
1.3
Establish
output
2.3
D esign data
collection
objectives
methodology
1.4
Identify
concepts
1.5
Check data
availability
1.6
Prepare
business
case
Quality Management
ID W /C P C F-M at
4
3
Build
5
/ M etadata M anagement
4
Collect
5
Process
4.1
Select
sample
5.1
Integrate
data
3.2
Build or
enhance
process
components
4.2
Set up
collection
5.2
Classify & code
3.3
Configure
workflows
4.3
Run
collection
5.3
Review, validate
& edit
B laise E D R
4 Build3.13.1data 5
Build data
collection
collection
instr ument
instrument
A 1 7.17.1
Update
B Update
output
output
S systems
systems
9
8 I
W 1
7.2
7.2
6.2
6.2
S Produce
Produce
Validate
Validate
dissemination
W
dissemination
outputs
outputs
products
7 A 2 products 3
T D D7.3I S tds
7.3
7.3
6.3
1 MMManage
anage
anage
Scrutinize &
release of
release
releaseofof
explain
2 dissemination
1
O
E
C
D
.
s
t
a
t
dissemination
dissemination
products
products
products
RE E M 2
3.4
Test production
system
2.5
D esign statistical
processing
3.5
Test statistical
business
5.5
D erive new
variables &
methodology
process
statistical units
systems &
workflow
7
Disseminate
6.1
6.1
Prepare
Prepare
draft
draft
outputs
outputs
6
2.4
D esign frame
& sample
methodology
2.6
D esign
production
6
Analyse
4.4
Finalize
collection
5.4
5.4
Impute
Impute
2
7
6.4
6.4
Apply
A pply
disclosure
disclosure
control
control
6.5
Finalize
outputs
5.6
5.6
Calculate
weights
Calculate weights
5.7
5.7
Calculate
Calculate
aggregates
aggregates
5.8
Finalize data files
support
9.2
Conduct
evaluation
repository
8.3
Preserve
data and
9.3
Agree
Action
associated
metadata
plan
1
T re a s u ry
1
DSIP strands
Strand 1 – Leverage off SDMX capabilities
provided by OECD.stat
Strand 2 - REEM
Strand 3 – Publishing standards
using DDI
Strand 4 – QDT->DDI->Blaise;
HSF1(Blaise)->DDI->HSF2(QDT)
5
Strand 5 – IDW/CPCF Mat->DDI<->EDR
Strand 6 - Administrative data acquisition
6
< fo o ter>
8.2
M anage
archive
4
7
8
< d ate/tim e>
9.1
Gather
evaluation
inputs
CS DI
7.5
M anage
user
3
7
8.1
D efine
archive
rules
associated
metadata
2
7
system
9
Evaluate
8.4
D ispose of
data &
7.4
Promote
dissemination
products
1
3.6
Finalize
production
8
Archive
9
Strand 7 - Estimation/Imputation/Macro editing
Strand 8 – Time Series adjustment
–
Replace SEASABS
Strand 9 – National Accounts
< n u m b er>
A Collaborative Approach to Agile statistical
Processing Architecture
ABS Experience and Aspirations
THANK YOU
Brian Studman
IT Director, information Management Transformation Program