UNITED NATIONSECONOMIC AND SOCIAL COMMISSION FOR ASIA AND THE PACIFICSTATISTICAL INSTITUTE FOR ASIA AND THE PACIFIC (SIAP)
Regional Training Course on Big Data for Sustainable Development
15 – 19 April 2019
Daejeon, Republic of Korea
CONCEPT NOTEI.

Background and rationale

The achievement of the Sustainable Development Goals (SDGs) requires the availability of highquality, timely and reliable data to produce the relevant SDG indicators and other statistics,
disaggregated as relevant. In order to meet this need official statistics must modernize andincorporate new data sources, including Big Data. At its 2014 session, the United Nations
Statistical Commission recognized the importance of Big Data with the establishment of a Global
Working Group on Big Data for Official Statistics. At its 2018 session, the Statistical
Commission further underscored the importance of Big Data with the adoption of the Bogota
Declaration, which calls for the innovation of current statistical production processes and thecreation of new ways of compiling SDG indicators.
Innovations are needed in the daily production of official statistics, which requires partnerships
with the private sector, new skills and infrastructure, and clear links between available Big Data
sources and the SDG indicators. This training aims at sharing experience and building the skills
required for the production and dissemination of official statistics with new data sources.
The Regional Training Course on Big Data for Sustainable Development will be heldfrom 1 5 -19 April 2019 i n Daejeon, Republic of Korea, and will be conducted by
the Statistical Institute for Asia and the Pacific (SIAP), in collaboration with Statistics
Korea (KOSTAT).

II.

Objectives

The course aims to provide a platform for demonstrating good practices and exchangingexperiences in dealing with challenges and issues in the introduction of Big Data into theproduction and dissemination of official statistics. The course will focus on the use ofadministrative data (Big Data collected by other government departments), publicly available
data and private sector data sources.
1III. Learning outcomes
As a result of this course, participants are expected to be able to:IV.

•Understand the concepts and technologies use for analysing Big Data.

•Discuss the tasks required to introduce administrate data into the statistical process.

•Understand methodologies for linking private sector data to existing survey andadministrative data.

•Retrieve and perform simple analysis of publicly available Big Data.

•Understand the architecture and infrastructure required to process Big Data.

Course design and content

The course is comprised of nine sessions, consisting of presentations on the main topic andrelated issues/problems; illustrative good practices from resource persons presenting practical
approaches and solutions to the issues/problems; hands-on exercises; sharing of relevant country
experiences and concerns; and plenary discussions.
To facilitate exchange of experiences, each participant will need to submit a country
presentation of no more than 10 slides on or before 31 March 2019 to escap-staff@un.org.
The presentation should be 10-15 minutes long. It should briefly introduce the current use of Big
Data, present main challenges encountered, and outline specific plans to incorporate new data sources
into statistical production processes. Please include relevant detail regarding, (a) the current useof administrative data sources, private sector and crowd sourced data; (b) the scope, sources,
frequency, and quality checks protocols used for these data; (d) SDG indicators that will becompiled using Big Data sources or a combination of source to include Big Data.
Course Outline 1
ModuleCoverage

Session 1: Overview of Big Data•
•Session 2 : Big Data Analysis using aStatistical Software••1

Understanding Big Data
Uses of big data

Hands on: Introduction to data analysis
Hands on: Introduction to data visualization

The final list of topics and detailed program of the training will defined by the trainers.

2Session 3: Administrative Data••Session 4: Statistics Production usingdigital economy•••

Session 5: Country Presentations•Session 6: Field Trip•

Session 7: Statistics Production
Using Private Data••••

Session 8: Crowd sourced data andsocial media data••••

Session 9: Next Steps•IV.

Data acquisition, preparation and storage
Case study: Statistics production using
administrative data

Collecting and analysing online retail price data
Linking administrative data to private data sources
Case study: Analysis of digital economy data
Country Presentations
Field trip

Accessing personal data
Linking private data sources.
Case study: Statistics production using personal data
Hands on: Analysing simulated telecommunication
dataAnalysis using crowd sourced data
Hands on: Processing, analysis and visualization
Analysis using social networks dataHands on : Social Media data analysis
Discussion: Advancing Big Data within yourorganization

Target participants

The course is designed for mid-level statisticians and technical staff from national statistical
offices whose main responsibilities include the production and dissemination of official
statistics with new data sources to include private sector data, administrate data and datacollected from non-traditional sources such as social media data and online pricing data.
Approximately 20 participants are expected to attend. The participants are expected to be
have a basic proficiency in the use of a statistical software.

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