%0 Conference Proceedings
%T Representational Quality Challenges of Big Data: Insights from Comparative Case Studies
%+ Delft University of Technology (TU Delft)
%+ Tampere University of Technology [Tampere] (TUT)
%A Wahyudi, Agung
%A Pekkola, Samuli
%A Janssen, Marijn
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 17th Conference on e-Business, e-Services and e-Society (I3E)
%C Kuwait City, Kuwait
%Y Salah A. Al-Sharhan
%Y Antonis C. Simintiras
%Y Yogesh K. Dwivedi
%Y Marijn Janssen
%Y Matti Mäntymäki
%Y Luay Tahat
%Y Issam Moughrabi
%Y Taher M. Ali
%Y Nripendra P. Rana
%I Springer International Publishing
%3 Challenges and Opportunities in the Digital Era
%V LNCS-11195
%P 520-538
%8 2018-10-30
%D 2018
%R 10.1007/978-3-030-02131-3_46
%K Big data
%K Interpretation
%K Sense-making
%K Naturalistic decision making
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X 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.
%G English
%Z TC 6
%Z WG 6.11
%2 https://inria.hal.science/hal-02274148/document
%2 https://inria.hal.science/hal-02274148/file/474698_1_En_46_Chapter.pdf
%L hal-02274148
%U https://inria.hal.science/hal-02274148
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-11
%~ IFIP-I3E
%~ IFIP-LNCS-11195
%~ INFOLOGY-INFOLOGIE