The Roles of Big Data in the Decision-Support Process: An Empirical Investigation

The decision-making process is marked by two kinds of elements: organizational and technical. The organizational elements are those related to companies’ day-to-day functioning, where decisions must be made and aligned with the companies’ strategy. The technical elements include the toolset used to aid the decision making process such as information systems, data repositories, formal modeling, and analysis of decisions. This work highlights a subset of the elements combined to define an integrated model of decision making using big data, business intelligence, decision support systems, and organizational learning all working together to provide the decision maker with a reliable visualization of the decision-related opportunities. The main objective of this work is to perform a theoretical analysis and discussion about these elements, thus providing an understanding of why and how they work together.

[1]  Harry Bouwman,et al.  Developing a decision support system for business model design , 2012, Electronic Markets.

[2]  Paul Zikopoulos,et al.  Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data , 2011 .

[3]  Markus Grünwald,et al.  Business Intelligence , 2009, Informatik-Spektrum.

[4]  A. A. Mohamadina,et al.  Business Intelligence: Concepts, Issues and Current Systems , 2012, 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT).

[5]  Meliha Handzic,et al.  Integrated Socio-Technical Knowledge Management Model: An Empirical Evaluation , 2011, J. Knowl. Manag..

[6]  Herbert A. Simon.  The new science of management decision. , 1960 .

[7]  Shaofeng Liu,et al.  Integration of decision support systems to improve decision support performance , 2010, Knowledge and Information Systems.

[8]  Yunhao Liu,et al.  Big Data: A Survey , 2014, Mob. Networks Appl..

[9]  Robert J. Kauffman,et al.  Understanding the paradigm shift to computational social science in the presence of big data , 2014, Decis. Support Syst..

[10]  Rahul Sharan Renu,et al.  Use of Big Data and Knowledge Discovery to Create Data Backbones for Decision Support Systems , 2013, Complex Adaptive Systems.

[11]  F. Azma,et al.  Business intelligence as a key strategy for development organizations , 2012 .

[12]  Ciprian Dobre,et al.  Intelligent services for Big Data science , 2014, Future Gener. Comput. Syst..

[13]  Meliha Handzic,et al.  Improving Customer Relationship Management Through Business Intelligence , 2014, J. Inf. Knowl. Manag..

[14]  Athanasios V. Vasilakos,et al.  Big data: From beginning to future , 2016, Int. J. Inf. Manag..

[15]  Phuoc Tran-Gia,et al.  Big Data , 2013, Informatik-Spektrum.

[16]  Jules J. Berman,et al.  Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information , 2013 .

[17]  Wen-Lung Shiau,et al.  An empirical study of managers’ usage intention in BI , 2013, Cognition, Technology & Work.

[18]  Anna Sidorova,et al.  Business intelligence success: The roles of BI capabilities and decision environments , 2013, Inf. Manag..

[19]  Efraim Turban,et al.  Business Intelligence: Second European Summer School, eBISS 2012, Brussels, Belgium, July 15-21, 2012, Tutorial Lectures , 2013 .

[20]  Andreas Grillenberger.  Big data and data management: a topic for secondary computing education , 2014, ICER '14.

[21]  Ananth Srinivasan,et al.  Agent-enabled service-oriented decision support systems , 2013, Decis. Support Syst..

[22]  Aleš Popovič,et al.  Towards business intelligence systems success: Effects of maturity and culture on analytical decision making , 2012, Decis. Support Syst..