STATISTICAL SYSTEM FOR FUTURE GENERATION

Transcription

1 STATISTICAL SYSTEM FOR FUTURE GENERATION Jan FISCHER President of the Czech Statistical Office Eurostat 2007 Conference Modern Statistics for Modern Society 6 7 December 2007, Luxembourg

2 CZSO strategic documents (2004) Mission, Vision, Strategic Goals MISSION Based on acquired data, CZSO yields a reliable and consistent image about the state of the arts and development of the society according to developing needs of users of statistical service in conditions of changing environment. CZSO co-ordinates state statistical service in the Czech Republic. Selected CZSO goals Maximising benefits and comfort of users of CZSO products and services Optimising respondents burden, keeping up quality of outputs Innovating the statistical system, enhancing efficiency of processes and labour productivity

3 What is expected from Statistical Information System (SIS)? Based on high quality standards Sufficiently detailed Efficient Meta-data driven User friendly (externally, internally)

4 Reasons for SIS improvement Increase of requirements on statistical production based on high quality standards Reduction of respondents burden - - Cut-down of public expenditure - MODERNIZATION

5 Quo vadimus? FROM Single statistical surveys and projects Individual assessment of statistical surveys Statistical information TO System of statistical projects Complex evaluation of all statistical surveys Statistical information with meta-information

6 Quo vadimus (2)? Dissemination from statistical surveys Routine FROM Specific approach Separate data repository TO Integration all approach Data Warehouse (DWH) Dissemination from DWH and Datamart Smart statistics

7 How did we start Team for the redesign of SIS SWOT analysis Best practices assessment Development of major features Design of the Model 2008 new typology of variables Identification of all processes of production and dissemination of statistics

8 New typology of variables Core variables Standard variables Complementary variables Number of Units (%) Number of Units vs. Variables Detail 0 Low Level of Detail High Core Standard Complementary Now

9 Process map USERS REQUIREMENTS Statistical metainformation system (SMS) MODEL 2008 Parameters DECISION ON REQUIREMENTS Implementation parameters Preparation and verification Initialization Processing Data authorisation OUTPUTS (accomplished requirements) QUALITY Evaluation Revision of parameters SIS

10 1. SIS content Framework of SIS 2. Meta-information system 3. Statistical information and technology system

11 Content component of SIS - Statistical projects based on users requirements - Increased use of registers, administrative data, modelling - Specification of major statistical projects (coherence) - Linked system of statistical projects

12 Statistical meta-information system Statistical Projects (Tasks) Statistical Registers Statistical Quality Users Time Series SMS Dissemination Respondents Data Fund Statistical Classifications Statistical Variables

13 Process oriented SIS - decomposition Key documents, Conception SUPPORT EXECUTION CONTROL P R O C E S S E S Variable analysis, Methodological audits II. PREPARATION Sub-system: project preparation III. PROGRAMME Sub-system: processing preparation IV. INPUT Sub-system: data collection and primary processing SP INPUTS International and national legislation DATA VIII. S M S I. REQUIREMENTS Sub-system: assessment and comparison of requirements VII. REGISTERS System of CZSO registers Business Registers Natural persons database Construction Register Farm Register STATISTICAL TASK IX. DATA WAREHOUSE administration X. ICT - Support WAREHOUSE Standards VI. DISSEMINATION Sub-system: dissemination of statistical information and data V. CENTRAL Sub-system: central processing (Modelling, imputations, calculations, quality assessment) OUTPUTS

14 Which management approach is the best? Supervision by top management Committed management Project approach: Project Steering Committee SIS multi-professional team (architecture, design, model) Coordination and development team Implementation groups Tripod approach

15 Side effects (positive or negative?) Improvement of horizontal communication Strengthened team work Educational effect Adopting CZSO goals New style of cooperation with owners of administrative sources

16 Expected benefit Integration of methods and tools (reduction of costs and staff) Reduction of number of statistical surveys and their simplification (reducing respondents burden, staff) Simplified dissemination of statistical data and meta-data (cut-down of costs and staff)

17 First results New design of annual survey 2008 Common framework for all statistical surveys Audit of questionnaire content Reduction of number of surveyed variables

18 Potential Threats Delay in implementation of the project Break of contract (outsourcing) Radical changes of software system Lack of specialists Lack of financial resources

19 Lessons learned Importance of top management commitment Straightforward aims and medium-term planning based on strategic documents Multidisciplinary working team advantages Motivation for the young generation of statisticians

20 Thank you for your attention Looking forward to questions and discussion