Big Data and Ethnography: Together for the Greater Good

Vincent Charles, Tatiana Gherman

Research output: Contribution to Book/Report typesChapterResearchpeer-review

Abstract

Ethnography is generally positioned as an approach that provides deep insights into human behaviour, producing ‘thick data’ from small datasets, whereas big data analytics is considered to be an approach that offers ‘broad accounts’ based on large datasets. Although perceived as antagonistic, ethnography and big data analytics have in many ways, a shared purpose; in this sense, this chapter explores the intersection of the two approaches to analysing data, with the aim of highlighting both their similarities and complementary nature. Ultimately, this chapter advances that ethnography and big data analytics can work together to provide a more comprehensive picture of big data, and can thus, generate more societal value together than each approach on its own.
Original languageEnglish
Title of host publicationBig Data for the Greater Good
EditorsAli Emrouznejad, Vincent Charles
PublisherSpringer
Chapter2
Pages19-33
Number of pages15
Volume42
ISBN (Electronic)978-3-319-93061-9
ISBN (Print)978-3-319-93060-2
DOIs
Publication statusE-pub ahead of print - 14 Jul 2018
Externally publishedYes

Publication series

Name Studies in Big Data
PublisherSpringer
Volume42
ISSN (Print)2197-6503
ISSN (Electronic)2197-6511

Fingerprint

ethnography
Values

Keywords

  • Analytics
  • Big data
  • Ethnography
  • Thick data

Cite this

Charles, V., & Gherman, T. (2018). Big Data and Ethnography: Together for the Greater Good. In A. Emrouznejad, & V. Charles (Eds.), Big Data for the Greater Good (Vol. 42, pp. 19-33). ( Studies in Big Data ; Vol. 42). Springer. https://doi.org/10.1007/978-3-319-93061-9_2
Charles, Vincent ; Gherman, Tatiana. / Big Data and Ethnography: Together for the Greater Good. Big Data for the Greater Good. editor / Ali Emrouznejad ; Vincent Charles. Vol. 42 Springer, 2018. pp. 19-33 ( Studies in Big Data ).
@inbook{7c42553d6b45425ea63557af83a87962,
title = "Big Data and Ethnography: Together for the Greater Good",
abstract = "Ethnography is generally positioned as an approach that provides deep insights into human behaviour, producing ‘thick data’ from small datasets, whereas big data analytics is considered to be an approach that offers ‘broad accounts’ based on large datasets. Although perceived as antagonistic, ethnography and big data analytics have in many ways, a shared purpose; in this sense, this chapter explores the intersection of the two approaches to analysing data, with the aim of highlighting both their similarities and complementary nature. Ultimately, this chapter advances that ethnography and big data analytics can work together to provide a more comprehensive picture of big data, and can thus, generate more societal value together than each approach on its own.",
keywords = "Analytics, Big data, Ethnography, Thick data",
author = "Vincent Charles and Tatiana Gherman",
year = "2018",
month = "7",
day = "14",
doi = "10.1007/978-3-319-93061-9_2",
language = "English",
isbn = "978-3-319-93060-2",
volume = "42",
series = "Studies in Big Data",
publisher = "Springer",
pages = "19--33",
editor = "Ali Emrouznejad and Vincent Charles",
booktitle = "Big Data for the Greater Good",

}

Charles, V & Gherman, T 2018, Big Data and Ethnography: Together for the Greater Good. in A Emrouznejad & V Charles (eds), Big Data for the Greater Good. vol. 42, Studies in Big Data , vol. 42, Springer, pp. 19-33. https://doi.org/10.1007/978-3-319-93061-9_2

Big Data and Ethnography: Together for the Greater Good. / Charles, Vincent; Gherman, Tatiana.

Big Data for the Greater Good. ed. / Ali Emrouznejad; Vincent Charles. Vol. 42 Springer, 2018. p. 19-33 ( Studies in Big Data ; Vol. 42).

Research output: Contribution to Book/Report typesChapterResearchpeer-review

TY - CHAP

T1 - Big Data and Ethnography: Together for the Greater Good

AU - Charles, Vincent

AU - Gherman, Tatiana

PY - 2018/7/14

Y1 - 2018/7/14

N2 - Ethnography is generally positioned as an approach that provides deep insights into human behaviour, producing ‘thick data’ from small datasets, whereas big data analytics is considered to be an approach that offers ‘broad accounts’ based on large datasets. Although perceived as antagonistic, ethnography and big data analytics have in many ways, a shared purpose; in this sense, this chapter explores the intersection of the two approaches to analysing data, with the aim of highlighting both their similarities and complementary nature. Ultimately, this chapter advances that ethnography and big data analytics can work together to provide a more comprehensive picture of big data, and can thus, generate more societal value together than each approach on its own.

AB - Ethnography is generally positioned as an approach that provides deep insights into human behaviour, producing ‘thick data’ from small datasets, whereas big data analytics is considered to be an approach that offers ‘broad accounts’ based on large datasets. Although perceived as antagonistic, ethnography and big data analytics have in many ways, a shared purpose; in this sense, this chapter explores the intersection of the two approaches to analysing data, with the aim of highlighting both their similarities and complementary nature. Ultimately, this chapter advances that ethnography and big data analytics can work together to provide a more comprehensive picture of big data, and can thus, generate more societal value together than each approach on its own.

KW - Analytics

KW - Big data

KW - Ethnography

KW - Thick data

UR - https://link.springer.com/chapter/10.1007%2F978-3-319-93061-9_2

U2 - 10.1007/978-3-319-93061-9_2

DO - 10.1007/978-3-319-93061-9_2

M3 - Chapter

SN - 978-3-319-93060-2

VL - 42

T3 - Studies in Big Data

SP - 19

EP - 33

BT - Big Data for the Greater Good

A2 - Emrouznejad, Ali

A2 - Charles, Vincent

PB - Springer

ER -

Charles V, Gherman T. Big Data and Ethnography: Together for the Greater Good. In Emrouznejad A, Charles V, editors, Big Data for the Greater Good. Vol. 42. Springer. 2018. p. 19-33. ( Studies in Big Data ). https://doi.org/10.1007/978-3-319-93061-9_2