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Transcript of 1 1 Developing a framework for standardisation High-Level Seminar on Streamlining Statistical...
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Developing a framework for Developing a framework for standardisationstandardisation
High-Level Seminar on Streamlining Statistical production
Zlatibor, Serbia 6-7 July 2011
Rune GløersenIT DirectorStatistics Norway
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Contents
• Preconditions for improved standardisation
• The characteristics of processes and data at NSIs
• Applicable standards for various business processes
• Governance
• Some international trends
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Reasons for standardising statistical production
• Leaving stovepipes– Shift of focus from surveys and products to processes
• Introduction of quality frameworks– Coherence and comparability, quality assessments– Quality assurance and audits, risk reduction
• Improving efficiency– Internal interoperability, streamlining work processes, cost effectiveness
• Globalisation– International interoperability, comparability, benchmarking
• Content standardisation– Data and metadata standards– Best practise methods
• Technological standardisation– High-level architecture, standardise and reuse tools
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Model for Total Quality and Code of Practice
Use rs :Perc eived qualityNeeds and effec ts
Ins titutio nal E nviro nm e nt:Struc tural quality
S tatis tical O utp ut:Produc t quality
S tatis tical P ro ce sse s :Proc ess quality
The point of departure fors ys tem atic quality w ork isthe "us er needs "
The us ers dem and "produc t quality":R elevanc eAc c urac y and R eliabilityT im elines s and Punc tualityC oherenc e and C om parabilityAc c es s ibility and C larity
S tudy of proc es s es is a prec onditionfor im provem ents :
Sound MethodologyAppropriate s tatis tic al proc eduresNon-Exc es s ive Burden on R es pondentsC os t effec tivenes s
S truc tural c onditions provide a fram ew ork:P rofes s ional Independenc eMandate for D ata C ollec tionAdequac y of R es ourc esQ uality C om m itm entS tatis tic al C onfidentialityIm partiality and O bjec tivity
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Cornerstones of standardisation and improved interoperability
Organisationalinteroperability
Technologicalinteroperability
Semanticalinteroperability
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Enterprise ArchitectureCoherence and interoperability
Generic StatisticalBusiness Process Model
ICT-Architecture(Principles)
Generic StatisticalInformation Model
Best PracticeStatistical Methods
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Specifyneeds
Design Build Evaluate
Quality Management/Metadata Management
Process stages and data archiving
Data archiving spans the 4 main business processes,and comprises 4 steady states of the data life cycle
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Classifi-cations
Interface
Master Metadata
Statistics production systems
DataDoc
Variables
StatisticalProducts
Input datadefinition
Users
Interface
About theStatistics
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Specifyneeds
Design Build Evaluate
Quality Management/Metadata Management
Adopting standards
DDISDMX
?
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10 IT architecture principles
• IT-solutions must be built upon standard methods, a standard infrastructure and be in accordance with Statistics Norway’s business architecture
• IT-business alignment
• Open standards
• Our IT-solutions must be platform independent and component based, shared components must be used wherever possible
• It must be possible to create new IT-solutions by integrating existing and new functionality
• Our services must have clearly defined, technology-independent interfaces
• Distinguish between user interface, business logic and data management (layered approach)
• End-user systems must have uniform user interfaces
• Store once, reuse may times (avoid double storage)
• Data and metadata must be uniquely identifiable across systems
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A layered and modularised model for a coherent, streamlined production system
Users
Monitor and Manage
Tools and services
Workflow
Data and metadata
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Project Portfolio ManagementPrioritized and followed up by the Top Management
Project-Proposal
Decision Planning DecisionProjectExecution
Project-Directive
Assessment
Priority parametersScoreComments
Project-plan
Approved project plansDetailed requirementsAvailable resources
ReportsAssessment
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Statistics NorwayOrganisation and Management
SLACommon Services
Appendix
SLADepartments
Appendix
Service Level AgreementsSystems Maintenance
• One SLA for each of the subject matter departments
• Approx. 400 IT systems are maintained for the 4 Statistics Production Departments. In addition approx.70 systems for data collection, administration and dissemination
• An SLA covering Common Services is under development
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The diversity of users, needs and data flows
Public(re)use
Domain specificanalysis
Research andData Integration
Questionnaires
Data transfersRegisters
Common high level models, vocabulary etc
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Maturity growth in e-Government
OrganisationalInteroperability
SemanticalInteroperabilitySource: www.semicolon.no
Analytical Framework for e-Government Interoperability
SharingKnowledge
Aligning WorkProcesses
Joining ValueCreation
AligningStrategies
Bilateral data exchange, semi automated,Technical specifications and standards
Share best practises, metadata specifications,Set up standards for technical systems and dataexchange
Common information models, process models and service catalogues, shared development costs
Legislation,Whatever
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Common GenericIndustrial Statistics
GSBPM GSIM
Methods Technology
Statistical Concepts Information Concepts
Statistical HowTo Production HowTo
conc
eptu
alpr
actic
alIndustrializing Statistics
De-coupling content and technical standardisation