Integrated Statistical Systems: Data collection, Processing and - PowerPoint PPT Presentation

Integrated Statistical Systems: Data collection, Processing and Dissemination of Integrated Statistics An Integrated Statistics Approach Arab Conference Transformative Agenda for Official Statistics 5-7 April, Ankara Turkey

Challenges • Fast technological developments • Sharp increase in rate of data availability • Greater demand for more (& quicker) information • Decreasing budgets and improving cost efficiency • Demands to decrease response burden

Responds to challenges • Through modernization programmes for integrated statistics. • Characterized by: – technical and managerial specializations of staff – modernization of the IT-environment – harmonization/centralization of statistical production processes - GSBPM – repositioning the legal and regulatory environment of the statistical organizations. • Business as usual will not be enough.

Traditional approach Statistical Domains economic, environment and social statistics Household etc. Industry Education Jobs Environment Agriculture Income and Expenditures Meta data and standards Registers and frames Surveys and admin sources Processing Analysis Dissemination IT processes etc. ,

New Approach grated statistical production process and Integrated statistics programme Integrated business and Integrated household and international trade statistics social statistics programme programme (IBIS) (IHSP) Economic Environment Social Economic Environment Social dimensions dimensions dimensions dimensions dimensions dimensions

Integrated Statistics Programme • Meta data driven statistical production process • Meta data catalogue of variables • Survey repository • Guidelines – GSBPM based register based survey design – Multi source and multi mode collections – Micro data linking – Dissemination and visualisation • Software (micro data cataloguing, disclosure control

Integrated statistics approach Dissemination Macroeconomic Outputs / accounts Household and Economic & demographic environmental Statistical statistics statistics operations Data integration Inputs Data processing Data collection Registers and frames Surveys/Admin data Statistical Standards and methods infrastructure Information, Communication Technology (ICT) Institutional Management and internal policy setting Institutional arrangements

Benefits of integrated systems • Statistical business and information architecture governs common statistical production process and centralized statistical services over time and across countries. • Corporate, centralized services allow for statistical professionalization, project management and coordination. • Meet policy demands : covering business and household statistics, labor statistics, short term statistics, national accounts and international statistics. • Cost effectiveness. • Improved quality: coordinated output; reduction of human factor; improved reproducibility. • Reduction of response burden on business and household respondents. • Offer collaboration in the development and application of common methods and IT tools. • Robust and flexible and a stable platform for facing new developments.

Cost/Investments • Expertise (subject-matter specialists, projectmanagers, methodologists, IT specialists) • Training (new) personnel in change/project management and integration methods and process management • IT-environment using standards-based modules and dissemination platforms • Reorganisations

General Organizational Principles 1. Use corporate business and information architecture — blue print for process development 2. Adopt legal mandates based on fundamental principles for official statistics 3. Mainstream standards and metadata 4. Optimize use of administrative data 5. Maximize multi-use of data

General Organizational Principles (2) 6. Top down editing and imputation 7. Develop modular IT applications across statistical domains 8. Initiate methodological innovation and modernization 9. Establish quality culture 10.Manage development and change I. Project portfolio and portfolio management II. Planning and prioritisation III. Centralization and chain management

Total Cycle of Official Statistics Production (GSBPM) Phase 1 Common needs assessment Phase 8 Phase 2 Common Common evaluation design Phase 3 Phase 7 Common Common build dissemination Disseminated data Phase 6 Phase 4 Common Common analysis collection Phase 5 Output data Common Macro and processing sector Input data statistics Micro data

Corporate services 1. Population and business 6. Project registers and management frames 2. Data 5. IT-services collection and processing 4. Methodology 3. Dissemination and process development

Integrated Statistics Architecture Accounts and Indicators Environm Economic Social ent Statistics Statistics Economic Social Environment statistics Statistics Statistics Statistics IT processes IT processes IT processes etc. etc. etc. Methodology Dissemina-tion Dissemina-tion Dissemina-tion Specialized Data Processing (IT) corporate Analysis Analysis Analysis services Processing Processing Processing Data Collection Surveys and Surveys and Surveys and Admin sources Admin sources Admin sources Statistical Infrastructure Registers and Registers and Registers and Standards and methods, frames frames frames SBR, Legislative mandates, Meta-Data and Meta-Data and Meta-Data and Standards Standards Standards etc. 14

The problem we are trying to solve Disse Collec Proce Analys minat t ss e e Survey A NSI 1 Survey B Historically statistical organisations have produced specialised business processes and IT systems

How does Architecture help? • Many statistical organisations are modernising and transforming using Enterprise Architecture • Enterprise Architecture shows what the business needs are and where the organisation wants to be, then aligns efforts accordingly • It can help to remove silos and improve collaboration across an organisation

Enterprise Architecture helps you get to this Survey A Survey B NSI 1 Survey C Pro Coll Ana ces Dissemin ect lyse ate s

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

…we get this…. Disse Coll Proc Anal mina ect ess yse te Canada Sweden

This makes it hard to share and reuse! Disse Coll Proc Anal mina ect ess yse te Canada ? Sweden ?

…but if statistical organisations work together?

This makes it easier to share and reuse! Disse Colle Proc Analy minat ct ess se e Canada ? Sweden ?