Users’ perceptions of Data as a Service (DaaS)
DOI:
https://doi.org/10.37380/jisib.v6i2.172Keywords:
business intelligence as a service, DaaS, data governance, data steward, DBaaS, ethics, Intelligence as a Service (IaaS), management of dataAbstract
In this study, 190 market intelligence (MI), competitive intelligence (CI) and
business intelligence (BI) professionals and experts were asked about Data as a Service (DaaS). Findings show there were few limits or restrictions on what kind of data users could imagine buying or renting, if all types of data were available. Data that is more sensitive—personal data and private data—will be difficult to buy, users think. Company secrets and most data for business-to-business (B2B) industries is especially difficult to obtain. The major concerns for
DaaS from a user perspective are confidentiality, quality, reliability, security and accessibility. Besides, it is often pointed out by users that when everyone has much of the same data competition will increase. Users want to see more on company metrics, less expensive, more secure and more flexible data solutions. The analysis reveals that the ethical dimension are a major concern as DaaS develops. An extensive discussion follows, which also addresses new
points.
References
Abourezq, M. & Idrissi, A. (2016). Database-as-s-
Service for Big Data: An Overview.
International Journal of Advanced Computer
Science and Applications, 7(1), 157-177.
Canard, S., & Devigne, J. (2016). Highly privacuprotecting
data sharing in a tree structure.
The International Journal of Escience, 62, 119-
Chang, V. (2014). The business intelligence as a
service in the cloud. Future Generation
Computer Systems, 37, 512-534.
Curino, C., Jones, E. P., Popa, R. A., Malviya, N.,
Wu, E., Madden, S., & Zeldovich, N. (2011).
Relational cloud: A database-as-a-service for
the cloud.
Ellenberg, J. (2015). How not to be wrong. The
power of mathematical thinking. Penguin
books, New York, NY
MacFarland, C., Paget, R., & Samani, F. (2016).
“The hidden data economy”. Mcaffee report.
Intel Security. 19 pp.
Nyblom, M., Behrami, J., Nikkilä, T., & Solberg
Søilen, K. (2012). An evaluation of Business
Intelligence Software systems in SMEs – a
case study, Journal of Intelligence Studies in
Business, 2, 51-57.
Pringle, T., Baer, T., & Brown, G. (2014). Dataas-
a-Service: the next step in as-a-Service
journey. Ovum consulting report. 10 pp.
Rajesh, S., Swapna, S., Shylender, & Reddy, P.
(2012). Data as a Service in Cloud Computing.
Global Journal of Computing Science and
Technology Cloud & Distribute, 12(11).
Di Sano, M. (2014). Business intelligence as a
service: A new approach to manage business
processes in the cloud. In WETICE Conference
(WETICE), 2014 IEEE 23rd International (pp.
-160). IEEE.
Sabanovic, A. & Solberg Søilen, K. (2012).
Customers’ Expectations and Needs in the
Business Intelligence Software Market,
Journal of Intelligence Studies in Business, 2,
-20
Seibold, M., & Kemper, A. (2012). Database as a
Service. Datenbank-Spektrum, 12(1), 59-62.
Sharma, Sugam (2015). Evolution of as-a-service
Era in Cloud. Report.
Vu, Q. H., Pham, T. V., Truong, H. L., Dustdar,
S., & Asal, R. (2012, March). Demods: A
description model for data-as-a-service.
In Advanced Information Networking and
Applications (AINA), 2012 IEEE 26th
International Conference 605-612.
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