Qualitative Big Data’s Challenges and Solutions: An Organizing Review
dc.contributor | Aalto-yliopisto | fi |
dc.contributor | Aalto University | en |
dc.contributor.author | Suvivuo, Sampsa | en_US |
dc.contributor.department | Department of Information and Service Management | en |
dc.contributor.department | School Services, BIZ | en |
dc.date.accessioned | 2021-01-25T10:07:34Z | |
dc.date.available | 2021-01-25T10:07:34Z | |
dc.date.issued | 2021-01-05 | en_US |
dc.description.abstract | Digitalization of everyday lives has tremendously increased the amount of digital (trace) data of people’s behaviour available for researchers. However, traditional qualitative research methods struggle with the width and breadth of the data. This paper reviewed 61 recent studies that had utilized qualitative big data for the practical challenges they had encountered and how they were addressed. While quantitative and qualitative big data share many common issues, the review points at that lack of qualitative methods and dataset reduction required by algorithms in big data research decreases the richness of the qualitative data. Locating relevant data and reducing noise are further challenges. Currently, these challenges can be only partially addressed with a combination of human and computer pattern recognition and crowdsourcing. The review describes many “tricks of the trade” but abduction research and pragmatist philosophy seem promising starting places for a more pervasive framework. | en |
dc.description.version | Peer reviewed | en |
dc.format.extent | 980-989 | |
dc.format.mimetype | application/pdf | en_US |
dc.identifier.citation | Suvivuo, S 2021, Qualitative Big Data’s Challenges and Solutions : An Organizing Review . in Proceedings of the 54th Hawaii International Conference on System Sciences . Hawaii International Conference on System Sciences, pp. 980-989, Annual Hawaii International Conference on System Sciences, Maui, Hawaii, United States, 05/01/2021 . < http://hdl.handle.net/10125/70731 > | en |
dc.identifier.isbn | 978-0-9981331-4-0 | |
dc.identifier.other | PURE UUID: 04a993c2-6fc8-4ea0-a7f2-ae0962a7d33e | en_US |
dc.identifier.other | PURE ITEMURL: https://research.aalto.fi/en/publications/04a993c2-6fc8-4ea0-a7f2-ae0962a7d33e | en_US |
dc.identifier.other | PURE LINK: http://hdl.handle.net/10125/70731 | en_US |
dc.identifier.other | PURE FILEURL: https://research.aalto.fi/files/55310190/Suvivuo_HICSS_paper.pdf | en_US |
dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/102072 | |
dc.identifier.urn | URN:NBN:fi:aalto-202101251381 | |
dc.language.iso | en | en |
dc.relation.ispartof | Annual Hawaii International Conference on System Sciences | en |
dc.relation.ispartofseries | Proceedings of the 54th Hawaii International Conference on System Sciences | en |
dc.rights | openAccess | en |
dc.subject.keyword | Big Data and Analytics: Pathways to Maturity | en_US |
dc.subject.keyword | challenges | en_US |
dc.subject.keyword | digital trace data | en_US |
dc.subject.keyword | qualitative big data | en_US |
dc.subject.keyword | review | en_US |
dc.title | Qualitative Big Data’s Challenges and Solutions: An Organizing Review | en |
dc.type | A4 Artikkeli konferenssijulkaisussa | fi |
dc.type.version | publishedVersion |