1. Streamlining statistical production
PGSC meeting October 2012 Tirana

2. New long term orientations of the ESS
New production method of statistics – the VISION 2017
European Statistics Code of Practice 2005 and 2011
New European Statistical Law 2009 and 2012
Quality Assurance Framework
Communication 211
“Towards robust quality management for European Statistics” in 2011 (preventive quality management, revision of the Statistical Law)
Commitments on Confidence in Statistics

3. The VISION and the CoP Institutional environment (who?)
Statistical output (what?)
Statistical processes (how?)
How?
What?

4. Current situation – “Stovepipe approach”

5. Weaknesses Current situation Weakness
User needs are defined in an isolated manner
As a result, data collections are isolated as well
Regulations are made separately by statistical domain
Separate data transmissions from NSIs to Eurostat
Variety of tools for data validation and analysis
Weakness
no cross-checks for synergies;
Inconsistencies;
Single-purpose use of data
Differences in concepts, breakdowns, reference periods…
Different channels and formats, difficult follow-up;
inconsistencies in metadata;
Inefficiency, lack of interoperability,
difficult quality control

6. Future business model (1)

7. Future business model (2)
Situation in the future
IT side

8. Principles of ESS joint strategy
User needs are at the heart – increase availability of statistics (globalisation and multi-dimensional)
Use separate strategic approaches for “WHAT” (products, services, priorities) and “HOW” (the vision)
Reduce costs while maintaining data quality
Develop close partnership between all the 28 NSIs (MS, Estat) through appropriate dialogue and networks
Reuse of statistics from other sources (web, administrative sources)
Integration and standardisation of methods and tools
Legislation needs to focus on large domains and be output oriented
Develop a strategic human resource policy (staff skills, common training)

9. Major aspects of implementation
Horizontal integration: production of data according to the responding unit (e.g. household, enterprise), not by domain – no domain is specific!
Vertical integration: joint structures, networks (ESSnets)
Standardisation: common tools for each step of the data production process (e.g. common classifications, common definition of variables, common validation rules)
Use and combination of different data sources (survey data, administrative sources)
Move from sending data (push mode) to retrieving data from data warehouses (pull mode)

10. Enterprise architecture
ESS Vision
Information
Network
Information store
Process
Modular Production
Optimal cooperation

11. Information Process Network Information store Modular production
Optimal cooperation
2. EGR
5. Reference
Production
Architecture
6. ESS validation
1. Web infrastructure
7. Admin sources
3. SIMstat
4. ESS data warehouses

12. ESS VIPs related to strategic directions

13. Examples of projects supporting the VISION
ESS vision infrastructure projects – by project
Use of administrative data (ADMIN)
National accounts production system - services(NAPS-S)
ESS data warehouses (price and transport statistics) (PRIX/TRANS)
European System of business registers (ESBR)
Single Market Statistics (SIMSTAT)
Information Society – web infrastructure (ICT)
Common data validation policy (VIPV)
Census hub
Framework legislation for business statistics
Remote access to individual data

14. Examples of projects supporting the VISION
Cross-cutting issues - technical
Information models (defining information standards)
Networks (improve network infrastructure)
Data warehouses (develop a solution for storing, processing and redistributing shared data)
Shared services (put in place a service oriented architecture for integrating and industrialising processes)
Validation (design and maintain validation architecture)
Cross-cutting issues - general
Communciation
Governance
Human resources
Financial resources
Legal framework
Implementation strategy
Change management

15. ESS VIPs versus cross-cutting projects

16. Common data validation rules and tools
Develop standard documentation for validation
Develop standard formats for data
Develop standard rules for data validation
Describe the standard rules
Validation at the most appropriate levels (closest to the data, the sooner the better)
Develop generic IT tools

17. Census hub Use of SDMX as a standard Pull mode of data transmission
Development of standard tools

18. Questions
What measures can the NSI take / have the NSIs taken to streamline their production?
What are the most promising projects (electronic reporting, standard tools, harmonised concepts, generic IT tools)?
What are the obstacles to implement them?
How can the obstacles be overcome?
What kind of success stories do the NSIs can report about?
What can Eurostat do to help?