Data Integration
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There are many possible types of data integration and many possible combinations of data sources and modelling approaches. The experiences from the 2016 and 2017 UNECE Data Integration Projects, will be used to produce a User Guide to Data Integration in Official Statistics. Covering a limited number of experiments during 2016, this version of the guide uses the information gained, plus experience from other data integration work already completed or in progress, to explore issues and learnings with the aim of ultimately providing advice and material to support and advance data integration activities by official statistics organisations.

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The initial version of the guide is already available for members of the project. It does not aim to provide a comprehensive guide to Data Integration in Official Statistics. Rather, it aims to alert the user to some of the issues and factors that may need to be considered to undertake successful data integration projects. It will be further expanded and continuously updated to reflect new lessons learned and to show best practices and recommendations.

This ongoing Guide to Data Integration in Official Statistics will be shared publicly on these pages soon.

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A Guide to Data Integration for Official Statistics






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This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/. If you re-use all or part of this work, please attribute it to the United Nations Economic Commission for Europe (UNECE), on behalf of the international statistical community.
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Table of Contents
Table of Contents
Preface
I. Introduction
II. What is data integration?
A. Definition
B. Types of data integration
III. Planning for Data Integration
A. Access to data
B. Partnerships
C. Skills
IV. Data Considerations
A. Concepts
B. Identifiers
C. Privacy and Confidentiality
V. Quality
VI. Methods and Tools
A. Record Linkage
B. Statistical Matching
C. Other methodological considerations
Annex 1: Types of data integration
A. Integrating survey and administrative sources
B. Integrating new data sources (such as big data) and traditional sources
C. Integrating geospatial and statistical information
D. Validating official statistics

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