2.4 Business Architecture For ESS Validation

Slides:



Advertisements
Similar presentations
Eurostat The ESS.VIP Validation and its implementation in waste statistics Q2014 – Session 13 4 June 2014 Hartmut Schrör, Eurostat.
Advertisements

Eurostat Coverage of Security Issues Pascal Jacques ESTAT B0 Local Informatics Security Officer.
1 Vision Infrastructure Project (VIP) Enhanced Dissemination Chain 2 nd SISAI meeting, June 2012 B4 – IT for statistical production Unit B5 – Management.
European Conference on Quality in Official statistics, Rome 8-11 July 2008 Quality framework in European Trade Statistics Anne Berthomieu International.
Re-occurrence Training and Explanation 1.
Scientific Method. My 4 out of 5 Rule If you make an observation.
ESTAT 2012 ESSnet Workshop Session 2 Shared services Christine WIRTZ Eurostat Head of Unit "IT for statistical production" Rome, 3 December 2012.
Handbook on Precision Requirements and Variance Estimation for ESS Household Surveys Denisa Florescu, Eurostat European Conference on Quality in Official.
Addressing the challenge of producing European comparable data using administrative data Mihaela AGAFIŢEI Sorina VÂJU UNECE Seminar on Statistical Data.
Statistical data editing - UNECE work session – OSLO September 2012 Proposal of a revised approach for data validation within the European Statistical.
Eurostat Procedure for the revision of Mandate of Transmission Coordinators (TCOs) Item 15 DIME – ITDG SG meeting 18/11/2015 Jan Planovsky & Hubertus Cloodt.
Validation Architecture in the ESS CSPA Workshop, Geneva June 2016 Geneva June 2016 Eurostat, Vincent TRONET, Unit B1.
A new approach to continuing competence. Why we regulate The purpose of our regulation is to: protect consumers of legal services support the operation.
UNECE-CES Work session on Statistical Data Editing
External Quality Assurance 2017 – New Approach and New Opportunities
Progress on ESS Validation Project
ESS Validation State of Play and next steps
Update on peer reviews Antonio Baigorri, Eurostat ESS RDG 2014 Item 9
Validation Tools and Services: Current developments at Eurostat
EUROSTAT Unit B3 IT for statistical production Ewa Stacewicz
پروتكل آموزش سلامت به مددجو
Workshop on the Validation of Waste Statistics
Cost estimates for production of official statistics in the European Statistical System: different approaches CCSA Meeting, March 7, 2016, New York Pieter.
ESS Vision 2020 Validation: Implementation of deliverables
SISAI STATISTICAL INFORMATION SYSTEMS ARCHITECTURE AND INTEGRATION
Validation Break-out sessions
'Next steps - Taking CSPA into Statistical Organisations'
ESS Vision 2020 Resource Directors Group – June 2015
District 7475 : Grants Seminar September 8, 2018 DISTRICT GRANTS
Towards a European validation architecture
ESS Vision 2020: ESS.VIP Validation
4th RDG meeting Luxembourg
Strategy for statistical cooperation with the enlargement countries 2014 – 2020 MGSC March 2014 Point 3.1 of the Agenda Ferenc Gálik.
Data Validation in the ESS Context
Item 8 Cost assessment survey of production of statistics in the ESS
ESS.VIP VALIDATION An ESS.VIP project for mutual benefits
ESS Validation Project State of Play and next steps
Business and IT Architecture for ESS validation
Giuliano Amerini Unit E6 (Transport)
Draft Methodology for impact analysis of ESS.VIP Projects
3rd WGM Meeting 3 May 2018 Item 2.3 Possible standards for ESS Validation.
Cordination of Geographic Information in the Commission and the ESS
Validation services developed in the ESS
Crime & Criminal Justice Data & metadata collection
Dissemination Working Group Luxembourg, May 2009
Quality Assurance in the European Statistical System
ESTP course on International Trade in Goods Statistics
ESS Validation Project State of Play and next steps
Applying the ESS EARF in a VIP project: The ESS.VIP Validation example
Test Process “V” Diagram
Resource Directors Group Introduction
Item of the Agenda SODI: Progress made and next steps
ESS.VIP Validation Item 5.1
Streamlining statistical production
ESTP Training Course “Enterprise Architecture and the different EA layers, application to the ESS context ” Rome, 16 – 19 October 2017.
Joint meeting of the ESS.VIP.BUS ICT Project
ESS.VIP.SERV Shared Services
Item 4.2 EUROMOD latest developments
Modernisation of Validation in the ESS Status report
RDG Task Force Cooperation models – Follow up May ESSC
Modernisation of Validation in the ESS Collaboration with countries
ESS Vision and VALIDATION
Task Force Peer reviews and quality Eurostat
The ESS quality reporting implementation process
RDG TF Cooperation models – Action Plan Progress report
EDAMIS 4 Status and outlook
Item 9 Validation in UOE data collection
Joint meeting of the working groups Environmental accounts and Monetary environmental statistics and accounts 15 May 2019 Quality reporting-National Metadata.
Knowing more about SDMX
Presentation transcript:

2.4 Business Architecture For ESS Validation Q2018 Conference Training course on Data Integration and Validation Krakow, 26 June 2018 Vincent.TRONET@ec.europa.eu

Business Architecture What it does? Where does it take place? As-is situation To-be state and validation principles Severity levels and data acceptance process

What does the business architecture for validation do? Sets basic principles for validation in the ESS Defines the to-be state Clarifies the roles of Eurostat and Member States in the validation of data sent to Eurostat Clarifies how common validation services could be used by Member States and Eurostat

Focus of ESS.VIP Validation NSI ESTAT Validation takes place in several points of the ESS statistical production process

As-is situation

To-be state and Validation Principles KISS Where would you put principle ? The sooner, the better Well documented validation errors KISS (Keep It Short and Simple) Comply or explain Good enough is the new perfect Well-documented validation rules Trust, but verify Well documented validation errors The sooner, the better Well-documented validation rules Comply or explain A C Good enough is the new perfect D B Trust, but verify

Severity levels and acceptance Errors: Wrong values. Data containing errors are not considered acceptable. Warnings: suspicious values. Data with warnings may be accepted after justification. Information: Potentially suspicious values. Do not usually require further justification for acceptance. Minimum standard for compliance: data with no errors must be received before deadline

To VALIDATE we need…