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220928 ||| eng |
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|a 9781484310908
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| 100 |
1 |
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|a Hammer, Cornelia
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| 245 |
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|a Big Data
|b Potential, Challenges and Statistical Implications
|c Cornelia Hammer, Diane Kostroch, Gabriel Quiros-Romero
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2017
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| 300 |
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|a 41 pages
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| 651 |
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4 |
|a Estonia, Republic of
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| 653 |
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|a Web: Social Media
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| 653 |
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|a Economic Sociology
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| 653 |
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|a International Organizations
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| 653 |
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|a Big data
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| 653 |
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|a Research and Development
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| 653 |
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|a Language
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| 653 |
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|a Finance
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| 653 |
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|a Intellectual Property Rights: General
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| 653 |
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|a Technology
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| 653 |
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|a Large Data Sets: Modeling and Analysis
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| 653 |
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|a Computer Programs: Other
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| 653 |
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|a Social media
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| 653 |
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|a General issues
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| 653 |
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|a Economic Anthropology
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| 653 |
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|a Data Collection and Data Estimation Methodology
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| 653 |
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|a Financial statistics
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| 653 |
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|a International organization
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| 653 |
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|a Social and Economic Stratification
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| 653 |
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|a Data capture & analysis
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| 653 |
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|a Social networks
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| 653 |
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|a International institutions
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| 653 |
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|a Innovation
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| 653 |
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|a International Economics
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| 653 |
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|a Information Management
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| 653 |
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|a Technological Change
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| 653 |
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|a Economic and financial statistics
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| 653 |
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|a Statistics
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| 653 |
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|a Econometrics & economic statistics
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| 653 |
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|a Social networking
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| 653 |
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|a International Agreements and Observance
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| 700 |
1 |
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|a Kostroch, Diane
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| 700 |
1 |
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|a Quiros-Romero, Gabriel
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
0 |
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|a Staff Discussion Notes
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| 028 |
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|a 10.5089/9781484310908.006
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| 856 |
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|u https://elibrary.imf.org/view/journals/006/2017/006/006.2017.issue-006-en.xml?cid=45106-com-dsp-marc
|x Verlag
|3 Volltext
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| 082 |
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|a 330
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| 520 |
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|a 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
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