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Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero

IMPLICATIONS VI. CONCLUSIONS AND WORK AHEAD VII. REFERENCES BOXES 1. Adapted UNECE big data classification 2. M-Pesa Using Data Stored in Mobile Transfer Systems for Economic Policy Formulation 3. Week @ the Beach Index 4. Using SWIFT to Monitor Global Financial Flows 5. Mobile Positioning Data as a Data Source for International Travel Service Statistics 6. Administrative Data and Big Data 7. Rethinking of Information Technology & IT Governance FIGURES 1. The “5Vs” of Big Data—Volatility, Variety, Velocity, Veracity, and Volume 2. The Potential of

Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero

“found” in business and administrative systems, social networks, and the internet of things . Social networks are online platforms that help people build social relations with others having similar interests (Facebook, Twitter, LinkedIn). Users create blogs and profiles, share pictures, and exchange messages and thereby provide human-sourced information that is digitalized and stored. Data in social networks are often ungoverned and unstructured. In its big data classification, the United Nations Economic Commission for Europe (UNECE) (see Appendix X) also includes in

Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero
Big data are part of a paradigm shift that is significantly transforming statistical agencies, processes, and data analysis. While administrative and satellite data are already well established, the statistical community is now experimenting with structured and unstructured human-sourced, process-mediated, and machine-generated big data. The proposed SDN sets out a typology of big data for statistics and highlights that opportunities to exploit big data for official statistics will vary across countries and statistical domains. To illustrate the former, examples from a diverse set of countries are presented. To provide a balanced assessment on big data, the proposed SDN also discusses the key challenges that come with proprietary data from the private sector with regard to accessibility, representativeness, and sustainability. It concludes by discussing the implications for the statistical community going forward.