The 6th International Conference on Big Data for Official Statistics has been postponed due to the impact of COVID-19.
The new dates are 31 August to 2 September 2020. Please follow our Twitter account for updates.


Regional Workshop on Modernization of Official Statistics in the State of Qatar

This workshop was held on 4-5 November 2019 to shed light on the priority aspects that should be given due attention in the modernization process to promote official statistics. This activity comes within the framework of the transformation of official statistics project to support the Sustainable Development Agenda which is being implemented by Qatar, in cooperation with the United Nations Statistics Division, other United Nations organizations, regional, Arab and Islamic organizations, ministries and government agencies, research centers, universities and others.

The outcome of the workshop was captured in the Doha Declaration.

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Activities of the GWG task team on mobile phone data

Ronald Jansen
16 October 2019

Mobile phones are used by large segments of the population in all parts of the world, and it is thus expected that mobile phone data could fill data gaps worldwide. In its 2018 "Measuring the Information Society Report", ITU showed that the average mobile subscription rate is 107 per 100 inhabitants worldwide, with a lower average in Africa (76 per 100). These numbers show how pervasive mobile phone use is. Almost every person in the world lives within reach of a mobile-cellular signal.

Mobile phone data could help determine where tourists and migrants come from, how long they stay and where they go. The granularity of information which potentially can be obtained from mobile phone data is much higher than what can be obtained through traditional surveys. Moreover, the time lag from data collection to analysis could also be significantly reduced.

The GWG task team on the use of mobile phone data for official statistics conducts use cases, develops methodology and runs training workshops to build capacity in various regions of the world. It delivers on project activity, training workshops and preparation of training materials. Details of these activities are given hereafter.

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Planet and Google are ready to help the United Nations in the data work behind the 2030 Agenda for Sustainable Development

Ronald Jansen
27 September 2019

The 2030 Agenda for Sustainable Development was adopted in 2015 by all Heads of States. It provides a shared blueprint for peace and prosperity for people and the planet, now and into the future. At its heart are the 17 Sustainable Development Goals (SDGs), which are an urgent call for action by all countries - developed and developing - in a global partnership. They recognize that ending poverty and other deprivations must go hand-in-hand with strategies that improve health and education, reduce inequality, and spur economic growth - all while tackling climate change and working to preserve our oceans and forests.

To achieve the goals and targets by 2030, we need to know if we are on the right track and need to know in which areas we have to speed up our efforts. The global community of official statistics was explicitly made responsible for assuring relevant data to monitor progress. Not surprisingly, it proves to be an enormous challenge to provide timely and frequent data for monitoring change on the SDG indicators, preferably also at local levels of all societies. In this regard, Ms. Amina Mohamed, the UN Deputy Secretary-General, emphasized in her video message to our 5th international conference on Big Data in Kigali in May of this year that "Quality, relevant and timely data are essential to drive policies and programs, whether it be in our efforts to create decent work for all, monitoring environmental degradation, containing the spread of the Ebola virus or improving the living conditions in our urban areas. We need not only good data, but also real-time data."

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6th International Conference on Big Data for Official Statistics
  Seoul, Republic of Korea         31 Aug - 2 Sep 2020

The 6th International Conference on Big Data for Official Statistics has been postponed due to the impact of COVID-19.
The new dates are 31 August to 2 September 2020. Please follow our Twitter account for updates.

The focus of this year's conference is on the use of Data Science for official statistics, in particular the use of Artificial Intelligence and Machine Learning. The GWG built the UN Global Platform as a digital collaborative environment to work together on new data solutions with the whole statistical community and to learn together. Big Data in combination with AI and Machine learning could fill gaps, make statistical operations more cost effective, enable the replacement of surveys and provide more granularities in outputs e.g. in support of the monitoring of the SDG goal of 'leaving no one behind'. This Conference will also cover the topic of changing the production line to introduce new data solutions in national statistical systems.

International Symposium on the use of Big Data for official statistics
  Hangzhou, China         16 - 18 October 2019

The symposium aims to increase the knowledge and skills of statistical offices in using new data source, tools and methods, and to foster collaboration among countries in the Asian-Pacific region in the use of Big Data for official statistics. The symposium will give an overview on the work of the GWG and its Task Teams. Special attention will be devoted to the UN Global Platform as a digital platform enabling international and regional collaboration. This platform enables statisticians, data scientists and other researchers from different countries and locations to work together on projects involving, for example, satellite data to estimate crop production.

The UN-China Centre on Big Data is being set up as a regional hub of the UN Global Platform, giving countries in Asia a better opportunity to advance the work on Big Data. This Centre will be useful to initiate and execute innovative data projects. The Centre will also serve as a training institute to develop new skills for staff of national statistical offices.


The GWG has compiled a Inventory of Big Data projects (including exploratory research, feasibility studies, pilot projects and projects currently in production) that have implications for compiling official statistics and/or supporting the measurement of the SDG indicators. The aim is to share broad information about potential Big Data projects in the statistical community and share specific information about partnerships, data sources, and tools. The Inventory includes information such as the objective of the project; the Big Data source used; data access and the use of partnerships; applicability to specific domain(s) of official statistics and/or SDG indicators; methods and technology used; and assessment of quality, among others. The GWG collected this information from the statistical community in two surveys conducted in 2014 and 2015.

Explore Inventory


Access and Partnerships

Access to Big Data sources and forging partnerships with other public and private organisations in order to work with Big Data is becoming ever more important to national statistical systems (NSS) for fulfilling their mission in society. The national statistical systems (NSS) should collaborate rather than compete with the private sector, in order to advance the potential of official statistics. At the same time, the NSS should remain impartial and independent, and invest in communicating the advantages of exploiting the wealth of available digital data to the benefit of the people. Building public trust will be the key to success. The objectives of the task team are to facilitate access to Big Data sources for official statistics and facilitate forming partnerships with other public and private organisations in order to work with Big Data.

Big Data and the Sustainable Development Goals

The recent report of the Independent Expert Advisory Group (IEAG) on the Data Revolution for Sustainable Development defines the data revolution for sustainable development as the integration of data coming from new technologies with traditional data, in order to produce relevant high-quality information, with more detail and at higher frequencies to foster and monitor sustainable development. This revolution also entails the increase in accessibility to data through much more openness and transparency, and ultimately more empowered people for better policies, better decisions and greater participation and accountability, leading to better outcomes for the people and the planet.

Mobile Phone Data

Mobile Phone Data has surfaced in recent years as one of the Big Data sources with a lot of promise. It is expected that Mobile Phone data could fill data gaps especially for developing countries given their high penetration rates. In its 2014 'Measuring the Information Society Report', ITU shows that the average mobile subscription rate is 96.4 per 100 inhabitants world-wide, with some lower averages in Asia (89.2) and Africa (69.3). Nevertheless, these numbers show how pervasive mobile phone use is. ITU elaborates that rural areas are still lacking behind urban areas, and this should be considered in studies using Mobile Phone data, but it is clear that the coverage of these data is global. Almost every person in the world lives within reach of a mobile-cellular signal.

Satellite Imagery and Geo-Spatial Data

The demand for more diversified, sophisticated and rapid statistical services could be met by leveraging the emerging sources of Big Data, such as those relating to remote sensing imagery, transactional and social media data and mobile device data. Satellite imagery has significant potential to provide more timely statistical outputs, to reduce the frequency of surveys, to reduce respondent burden and other costs and to provide data at a more disaggregated level for informed decision making. The Task Team on Satellite Imagery and Geo-Spatial Data aims to provide strategic vision, direction and development of a global work plan on utilising satellite imagery and geo-spatial data for official statistics and indicators for post-2015 development goals. We are building on precedents to innovatively solve the many challenges facing the use of satellite imagery and geo-spatial data sources.

Scanner Data

Scanner data is a Big Data source being increasing used in national statistical systems for the calculation of price indices as statistical offices explore ways to meet the expectation of society for enhanced products and improved, more efficient ways of working. Many of the price measurement issues and methods for scanner data from supermarket chains and other retailers apply also to other big data sources (for example, online prices obtained from webscraping). This task team plans to deliver: 1. An open source application for analysis, monitoring and index estimation from cleaned and classified Prices’ big data; 2. Accompanying training and instructional material; and 3. Accompanying methodological guidance including recommendations and cataloging good practice.

Social Media Data

Social Media Data has surfaced in recent years as one of the Big Data sources with a lot of promise. Whereas Satellite Imagery or Mobile Phone Data are relatively well-defined as data sources, Social Media is more of a mixed basket, which will need to be further clarified by this task team. Maybe a general denominator of such data is that they are disseminated throught the Internet; further, most data are text messages, images, video or searches voluntarily submitted by persons. Against this background, the task team should clarify which kind of social media data can be collected, how it can be collected, how it can be analyzed and processed into statistics, useful for policy purposes.

Training, Competencies and Capacity Development

Big Data is by definition different from traditional data sources used by national statistical systems (NSSs). This implies that new methodologies need to be developed to work with Big Data. The kind of sources of Big Data poses challenges both in how to approach their processing and analysis, but also the mere technological way of dealing with them. This means that new skill sets are necessary to successfully work with the new Big Data sources. Some of these new skill sets could be hired temporarily, others will need to become in an integral part of the institution. It is up to the senior management to decide what will be done by the institute itself and what will be outsourced. An additional complication is that there is not just one kind of Big Data source, and each kind of Big Data may have different requirements as far as new skill sets are concerned. We need therefore to develop tools to identify and assess the needs for new skills.

Committee on Global Platform for Data, Services and Applications

Building on the best practices of public and private Big Data initiatives, and offering the technology infrastructure and a network for data innovation to the official statistical community, the Global Platform could address the needs for (a) a global hub for official statisticians, data scientists and domain experts from the public and private sector to exchange ideas and methods for processing, analysing and visualising Big Data; (b) a global hub for storing Big Data, and related processing, analysing and visualising methodology, and services and applications for continuous development and re-use; (c) a global hub for demonstrating the value of Big Data in better decision making through official statistics through pilots and case studies; and (d) a global resource hub for training materials and workshops on Big Data for capability building.



International Organizations

  • Australia
  • Bangladesh
  • Brazil
  • Cameroon
  • Canada
  • China
  • Colombia
  • Denmark
  • Egypt
  • Georgia
  • Germany
  • Indonesia
  • Ireland
  • Italy
  • Mexico
  • Morocco
  • Netherlands
  • Oman
  • Pakistan
  • Philippines
  • Poland
  • Republic of Korea
  • Saudi Arabia
  • Switzerland
  • United Arab Emirates
  • United Kingdom
  • United Republic of Tanzania
  • United States
  • African Development Bank
  • Eurostat
  • FAO
  • IMF
  • OECD
  • GCC-Stat
  • ITU
  • UN Global Pulse
  • UN Statistical Institute for Asia and the Pacific
  • UNSD
  • Universal Postal Union
  • World Bank