Big Data :: Potential, Challenges and Statistical Implications. /
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-me...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Washington, D.C. :
International Monetary Fund,
2017.
|
| Schriftenreihe: | IMF staff discussion note ;
SDN/17/06. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource (41 pages). |
| ISBN: | 148431090X 9781484310908 1484318994 9781484318997 |
Internformat
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| 100 | 1 | |a Hammer, Cornelia, |e author. |0 http://id.loc.gov/authorities/names/no2009043343 | |
| 245 | 1 | 0 | |a Big Data : |b Potential, Challenges and Statistical Implications. / |c Prepared by Cornelia L. Hammer, Diane C. Kostroch, Gabriel Quirós, and STA Internal Group. |
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| 490 | 1 | |a IMF staff discussion note ; |v SDN/17/06. | |
| 520 | 3 | |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. | |
| 588 | |a Description based on online resource; title from PDF title page (IMF eLibrary, viewed April 6, 2018). | ||
| 650 | 0 | |a Big data. |0 http://id.loc.gov/authorities/subjects/sh2012003227 | |
| 650 | 6 | |a Données volumineuses. | |
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| 650 | 7 | |a Big data |2 fast | |
| 700 | 1 | |a Kostroch, Diane, |e author. | |
| 700 | 1 | |a Quirós Romero, Gabriel, |e author. |0 http://id.loc.gov/authorities/names/n87883954 | |
| 758 | |i has work: |a Big Data (Text) |1 https://id.oclc.org/worldcat/entity/E39PCG3fJwjY6bbTvTH3BqVRyq |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Hammer, Cornelia. |t Big Data: Potential, Challenges and Statistical Implications. |d Washington, D.C. : International Monetary Fund, 2017 |z 9781484310908 |
| 830 | 0 | |a IMF staff discussion note ; |v SDN/17/06. |0 http://id.loc.gov/authorities/names/no2011013696 | |
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| spelling | Hammer, Cornelia, author. http://id.loc.gov/authorities/names/no2009043343 Big Data : Potential, Challenges and Statistical Implications. / Prepared by Cornelia L. Hammer, Diane C. Kostroch, Gabriel Quirós, and STA Internal Group. Washington, D.C. : International Monetary Fund, 2017. 1 online resource (41 pages). text txt rdacontent computer c rdamedia online resource cr rdacarrier IMF staff discussion note ; SDN/17/06. 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. Description based on online resource; title from PDF title page (IMF eLibrary, viewed April 6, 2018). Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Données volumineuses. COMPUTERS / Databases / Data Warehousing. bisacsh Big data fast Kostroch, Diane, author. Quirós Romero, Gabriel, author. http://id.loc.gov/authorities/names/n87883954 has work: Big Data (Text) https://id.oclc.org/worldcat/entity/E39PCG3fJwjY6bbTvTH3BqVRyq https://id.oclc.org/worldcat/ontology/hasWork Print version: Hammer, Cornelia. Big Data: Potential, Challenges and Statistical Implications. Washington, D.C. : International Monetary Fund, 2017 9781484310908 IMF staff discussion note ; SDN/17/06. http://id.loc.gov/authorities/names/no2011013696 FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1615699 Volltext |
| spellingShingle | Hammer, Cornelia Kostroch, Diane Quirós Romero, Gabriel Big Data : Potential, Challenges and Statistical Implications. / IMF staff discussion note ; Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Données volumineuses. COMPUTERS / Databases / Data Warehousing. bisacsh Big data fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2012003227 |
| title | Big Data : Potential, Challenges and Statistical Implications. / |
| title_auth | Big Data : Potential, Challenges and Statistical Implications. / |
| title_exact_search | Big Data : Potential, Challenges and Statistical Implications. / |
| title_full | Big Data : Potential, Challenges and Statistical Implications. / Prepared by Cornelia L. Hammer, Diane C. Kostroch, Gabriel Quirós, and STA Internal Group. |
| title_fullStr | Big Data : Potential, Challenges and Statistical Implications. / Prepared by Cornelia L. Hammer, Diane C. Kostroch, Gabriel Quirós, and STA Internal Group. |
| title_full_unstemmed | Big Data : Potential, Challenges and Statistical Implications. / Prepared by Cornelia L. Hammer, Diane C. Kostroch, Gabriel Quirós, and STA Internal Group. |
| title_short | Big Data : |
| title_sort | big data potential challenges and statistical implications |
| title_sub | Potential, Challenges and Statistical Implications. / |
| topic | Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Données volumineuses. COMPUTERS / Databases / Data Warehousing. bisacsh Big data fast |
| topic_facet | Big data. Données volumineuses. COMPUTERS / Databases / Data Warehousing. Big data |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1615699 |
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