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Representational quality challenges of big data: insights from comparative case studies

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationChallenges and Opportunities in the Digital Era - 17th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2018, Proceedings
PublisherSpringer Verlag
Number of pages19
ISBN (Print)9783030021306
Publication statusPublished - 2018
Publication typeA4 Article in a conference publication
EventConference on e-Business, e-Services, and e-Society - Kuwait City, Kuwait
Duration: 30 Oct 20181 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11195 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceConference on e-Business, e-Services, and e-Society
CityKuwait City


Big data is said to provide many benefits. However, as data originates from multiple sources with different quality, big data is not easy to use. Representational quality refers to the concise and consistent representation of data to allow ease of understanding of the data and interpretability. In this paper, we investigate the challenges in creating representational quality of big data. Two case studies are investigated to understand the challenges emerging from big data. Our findings suggest that the veracity and velocity of big data makes interpretation more difficult. Our findings also suggest that decisions are made ad-hoc and decision-makers often are not able to understand the ins and outs. Sense-making is one of the main challenges in big data. Taking a naturalistic decision-making view can be used to understand the challenges of big data processing, interpretation and use in decision-making better. We recommend that big data research should focus more on easy interpretation of the data.


  • Big data, Interpretation, Naturalistic decision making, Sense-making

Publication forum classification

Field of science, Statistics Finland