02100cas a2200265 a 4500 AALejournalIMF017822 230101c9999 xx r poo 0 0eng d 5.00 USD 9781484310908 2617-6750 BD-DhAAL BD-DhAAL Hammer, Cornelia. Big Data : Potential, Challenges and Statistical Implications / Cornelia Hammer, Diane Kostroch, Gabriel Quiros-Romero. Washington, D.C. : International Monetary Fund, 2017. 1 online resource (41 pages) Staff Discussion Notes <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required Electronic access restricted to authorized BRAC University faculty, staff and students 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. Mode of access: Internet Kostroch, Diane. Quiros-Romero, Gabriel. Staff Discussion Notes; Staff Discussion Notes ; No. 2017/006 Full text available on IMF http://elibrary.imf.org/view/journals/006/2017/006/006.2017.issue-006-en.xml IMF e-Library