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New Data Sources and the Data Revolution in Official Statistics

Explore the trends and work being done at the global level in integrating administrative data, big data, and geospatial information for the compilation of SDG indicators. Discover new data sources and their potential for official statistics, and learn about ongoing big data projects. Consider the challenges and opportunities for data revolution and its impact on Africa.

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New Data Sources and the Data Revolution in Official Statistics

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  1. New data sources, including big data, in official statistics – Trend and work being done at global level Sub-regional workshop on integration of administrative data, big data and geospatial information for the compilation of SDG indicators  23 - 25 April 2018 Molla Hunegnaw African Centre for Statistics

  2. Contents • Data Revolution • Big Data vs big data • Official data sources • New data sources • Potential data sources • Overview of Big Data projects • Issues to consider as a way forward

  3. [SG’s Data Revolution Group in “A World that Counts”] An explosion in the volume of data, the speed with which data are produced, the number of producers of data, the dissemination of data, and the range of things on which there is data, coming from new technologies such as mobile phones and the “internet of things”, and from other sources, such as qualitative data, citizen-generated data and perceptions data... A Definition of Data Revolution

  4. Concern for Africa With this definition, we are going to be left behind by the data revolution These things are not necessarily true in Africa

  5. What constitutes a Data Revolution • Data deluge • Open data and data access • Data privacy / data protection • Democratization of data • Data analytics • Data literacy • Improving data production process • Use of other data sources such as Big data • Innovation • …..

  6. Existing sources for official Statistics “Statistical” sources Sample survey: Systematic use of statistical methodology • Direct control over data collection • High cost, quality issues (non-response, survey errors) Census • Direct control over data collection • Allows results for small geographic areas, population sub-groups “Non-statistical” Sources Administrative data: Data for specific purposes, containing information on a complete group of units, updated continuously • Tax data; credit card data; social insurance data; births, deaths, etc.

  7. Big data sources • Social Networks (human-sourced information) • Loosely structured • Previously recorded in books and other forms • Almost entirely digitized • Takes different formats • Traditional Business systems (process-mediated data) • Usually structured • Form of Administrative registers • Becoming digital • Internet of Things (machine-generated data) • Growth in the number of sensors, machines to measure and record events • Continually generated and real-time Source: UNECE

  8. What Big Data “Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” https://www.gartner.com/it-glossary/big-data “Big data is data sets that are so voluminous and complex that traditional data-processing application software are inadequate to deal with them.” Wikipedia

  9. Big Data characteristics Volume: The quantity of generated and stored data. The size of the data determines the value and potential insight, and whether it can be considered big data or not. Variety: The type and nature of the data. This helps people who analyze it to effectively use the resulting insight. Big data draws from text, images, audio, video Velocity: In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Big data is often available in real-time.

  10. Potential data sources for official Statistics • Credit card data • Health records • Mobile phone data • Open street maps • Public transport • Road sensors • Satellite images • Ship identification data • Smart meter electricity • Social media data • Web scrapping

  11. Overview of Big Data projects for official statistics • United Nations Global Pulse • World Bank Group • Universities • National Statistics Offices • Government departments and ministries Source: UN Big Data for Official Statistics Global Working Group

  12. Overview of Big Data projects Statistical domains • Agriculture • Business • Crime • Culture • Demographic • Economic and financial • Energy • Environment • Labour • Price • Tourism • Transport • ……

  13. Overview of Big Data projects

  14. Overview of Big Data projects

  15. Overview of Big Data projects

  16. The way forward • Assist in the statistical process • Deriving statistics • New insights • Exploration / Feasibility stage • Data quality • Big Data capturing • One size does not fit all • Use your intuition

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