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DC Field | Value | Language |
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dc.contributor.author | Bell, D | - |
dc.contributor.author | Marshan, A | - |
dc.contributor.author | Lycett, M | - |
dc.contributor.author | Monaghan, A | - |
dc.date.accessioned | 2020-09-26T16:29:05Z | - |
dc.date.available | 2020-09-26T16:29:05Z | - |
dc.date.issued | 2020-10-13 | - |
dc.identifier | ORCID iDs: David Bell https://orcid.org/0000-0003-3148-6691; Mark Lycett https://orcid.org/0000-0001-6290-8258; Alaa Marshan https://orcid.org/0000-0001-6764-9160. | - |
dc.identifier.citation | Bell, D. et al. (2021) 'Exploring future challenges for big data in the humanitarian domain', Journal of Business Research, 131, pp. 453 - 468. doi: 10.1016/j.jbusres.2020.09.035. | - |
dc.identifier.issn | 0148-2963 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/21599 | - |
dc.description.abstract | This paper examines the challenges of leveraging big data in the humanitarian sector in support of UN Sustainable Development Goal 17 “Partnerships for the Goals”. The full promise of Big Data is underpinned by a tacit assumption that the heterogeneous ‘exhaust trail’ of data is contextually relevant and sufficiently granular to be mined for value. This promise, however, relies on relationality – that patterns can be derived from combining different pieces of data that are of corresponding detail or that there are effective mechanisms to resolve differences in detail. Here, we present empirical work integrating eight heterogeneous datasets from the humanitarian domain to provide evidence of the inherent challenge of complexity resulting from differing levels of data granularity. In clarifying this challenge, we explore the reasons why it is manifest, discuss strategies for addressing it and, as our principal contribution, identify five propositions to guide future research. | - |
dc.description.sponsorship | 10.3030/824115 European Commission (HPC and Big Data Technologies for Global Systems: HIDALGO); EPSRC Studentship. | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Copyright © 2020 Elsevier Inc. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.jbusres.2020.09.035, made available on this repository under a Creative Commons CC BY-NC-ND attribution licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | big data | en_US |
dc.subject | veracity | en_US |
dc.subject | granularity | en_US |
dc.subject | heterogeneous datasets | en_US |
dc.subject | humanitarian | en_US |
dc.subject | value | en_US |
dc.title | Exploring Future Challenges for Big Data in the Humanitarian Domain | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.jbusres.2020.09.035 | - |
dc.relation.isPartOf | Journal of Business Research | - |
pubs.publication-status | Published | - |
dc.identifier.eissn | 1873-7978 | - |
dc.rights.holder | Elsevier Inc. | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | Copyright © 2020 Elsevier Inc. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.jbusres.2020.09.035, made available on this repository under a Creative Commons CC BY-NC-ND attribution licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). | 1.51 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License