Big Data Analytics in Support of the Decision Making Process
Number of Authors: 2
2016 (English)In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 100, 1071-1084 p.Article in journal (Refereed) Published
Information is a key success factor influencing the performance of decision makers, specifically the quality of their decisions. Nowadays, sheer amounts of data are available for organizations to analyze. Data is considered the raw material of the 21st century, and abundance is assumed with today's 15 billion devices [aka Things!] already connected to the Internet. Accordingly, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such rapidly changing data of high volume, velocity, variety, veracity, and value by using big data analytics. This paper aims to research how big data analytics can be integrated into the decision making process. Accordingly, using a design science methodology, the “Big – Data, Analytics, and Decisions” (B-DAD) framework was developed in order to map big data tools, architectures, and analytics to the different decision making phases. The ultimate objective and contribution of the framework is using big data analytics to enhance and support decision making in organizations, by integrating big data analytics into the decision making process. Consequently, an experiment in the retail industry was administered to test the framework. Accordingly, results showed added value when integrating big data analytics into the decision making process.
Place, publisher, year, edition, pages
2016. Vol. 100, 1071-1084 p.
Research subject Information systems
IdentifiersURN: urn:nbn:se:ltu:diva-59520DOI: 10.1016/j.procs.2016.09.251OAI: oai:DiVA.org:ltu-59520DiVA: diva2:1033087
International Conference on ENTERprise Information Systems/International Conference on Project MANagement/International Conference on Health and Social Care Information Systems and Technologies, CENTERIS/ProjMAN / HCist 2016
Konferensartikel i tidskrift2016-10-052016-10-052016-11-22Bibliographically approved