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

Decisions continue to be important to researchers, organizations and societies. However, decision research requires re-orientation to attain the future of data-driven decision making, accommodating such emerging topics and information technologies as big data, analytics, machine learning, and automated decisions. Accordingly, there is a dire need for re-forming decision theories to encompass the new phenomena. This paper proposes a modern data-driven decision theory, DECAS, which extends upon classical decision theory by proposing three main claims: (1) (big) data and analytics (machine) should be considered as separate elements; (2) collaboration between the (human) decision maker and the analytics (machine) can result in a collaborative rationality, extending beyond the classically defined bounded rationality; and (3) meaningful integration of the classical decision making elements with data and analytics can lead to more informed, and possibly better, decisions. This paper elaborates the DECAS theory and clarifies the idea in relation to examples of data-driven decisions.

Purpose: – Trajectory is the path a moving object takes in space. To understand the trajectory movement patters, data mining is used. However, pattern analysis needs semantics to be understood. Therefore, the purpose of this paper is to enrich trajectories with semantic annotations, such as the name of the location where the trajectory has stopped, so that the paper is able to attain quality decisions. Design/methodology/approach: – An experiment was conducted to explain that the use of raw trajectories alone is not enough for the decision-making process and detailed pattern extraction. Findings: – The findings of the paper indicates that some fundamental patterns and knowledge discovery is only obtainable by understanding the semantics underlying the position of each point. Research limitations/implications: – The unavailability of data are a limitation of the paper, which would limit its generalizability. Additionally, the lack of availability of tools for automatically adding semantics to clusters posed as a limitation of the paper. Practical implications: – The paper encourages governments as well as businesses to analyze movement data using data mining techniques, in light of the surrounding semantics. This will allow, for example, solving traffic congestions, since by understanding the movement patterns, the traffic authority could make decisions in order to avoid such congestions. Moreover, it could also help tourism authorities, at national levels, to know tourist movement patterns and support these patterns with the required logistical support. Additionally, for businesses, mobile operators could dynamically enhance their services, voice and data, by knowing the semantically enriched patterns of movement. Originality/value: – The paper contributes to the already rare literature on trajectory mining, enhanced with semantics. Mainstream literature focusses on either trajectory mining or semantics, therefore the paper claims that the approach is novel and is needed as well. By integrating mining outcomes with semantic annotation, the paper contributes to the body of knowledge and introduces, with lab evidence, the new approach.