1. United Nations Development Account 10th Tranche Statistics and Data
ECA
Sub-Regional Workshop on Integration of Administrative Data, Big Data and Geospatial Information for the Compilation of SDG Indicators
Analysis of Country Responses to the Self-Assessment Questionnaire:
Big Data Sources
Haile Mulualem, ECA
Addis Ababa, Ethiopia
23-25 April 2018

2. Big Data
Big data are non-sampled data, characterized by the creation of databases from electronic sources whose primary purpose is something other than statistical inference (W. Horrigan, 2013).
Analysis of big data often relies on techniques such as machine learning and data mining.
Big data is characterized not only by its large Volume, but also by its Variety and the Speed at which it is generated.

3. In general, big data sources can be classified as follows:-
Sources of Big Data
In general, big data sources can be classified as follows:-
Sources arising from the administration of a programme
E.g., electronic medical records, hospital visits, insurance records, bank records and food banks.
Commercial or transactional sources arising from the transaction between two entities
E.g., credit card transactions and online transactions (including transactions from mobile devices)

4. … Sources of Big Data
Sensor network sources
E.g., satellite imaging, road sensors and climate sensors.
Tracking device sources
E.g., tracking data from mobile telephones and the Global Positioning System (GPS).
Behavioral data sources
E.g., online searches (about a product, a service or any other type of information) and online page views.
Opinion data sources
E.g., comments on social media.

5. Procedures to investigate the potential of big data sources for statistical purposes, including for the development of SDG indicators
Only 6 (of 22) countries responded that some procedures are in place to investigate the potential of big data sources for statistical purposes, including for the development of SDG indicators.

6. Main sources of Big Data

7. Involvement of NSOs in big data project relevant for compiling and/or supporting the measurement of SDG indicators
7 NSOs have involved in Big Data projects relevant for compiling and/or supporting the measurement of SDG indicators

8. Main/intended outcomes of ongoing big data project
More in depth analysis for official statistics, in particular development of SDG indicators
To analyze call record details of mobile phone users for the purpose of monitoring SDGs
To add new data and complement the existing ICT statistics
To drive development of data-based innovations
To establish data governance institutional framework
Improvement of data quality for locational and geo-referencing of statistics.

9. Established/Planned NSO Partnerships for big data projects

10. Estimation methods or a methodological framework for use of Big Data Sources
Only 4 NSOs developed new estimation methods or a methodological framework specifically related to the use of big data sources.
Compiling price data for CPI creation (Pilot phase)
No further detail information provided from responding NSOs
3 of those NSOs use Traditional statistical methods while 1 NSO use Data visualization methods

11. Technologies and tools used in Big Data processing projects

12. National challenges in the use of big data sources in the production of official statistics, including for the development of SDG indicators

13. Plans to improve data collection from big data sources for statistical purposes
Further exploitation on how to collect big data
Planning on the use of big data in the NSS during NSDS preparation. Consultants supporting the work, with finance both from Governments and development partners
Capacity building / trainings on big data
Considering big data sources as additional/complementary source of data to be used for monitoring and evaluating development programmes.

14. ECA
Thank You