Evolution Of Decision Support Systems Research Articles

The origins of business analytics and implications for the information systems field

ABSTRACTLike many other disciplines, the information systems (IS) community has embraced big data analytics and data science. However, in the rush to exploit the popularity of this latest trend, the areas of big data analytics and data science that are most relevant to the IS field are not made clear. While many consider data analytics as an evolution of decision support systems (DSS), that is, as a technology that needs to be managed or enhanced, this essay traces the complex origins and philosophy of analytics instead back to Luhn’s text analytics in the late 1950s, Naur’s Computing as a Human Activity and his datalogy, Tukey’s Future of Data Analysis of the 1960s, and Codd’s relational database schema in the 1970s, well before big data analytics and data science became industry buzzwords. Many of what is now considered mainstream thinking in big data analytics and data science can be traced back to these visionaries. This essay examines the implications of the complex origins of data analytics and data science for the IS field, specifically on how those different discourses impact future research and practice.

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine.

STUDY OF PROBLEMS OF ORGANIZATION OF KNOWLEDGE IN THE FIELD OF INFORMATION TECHNOLOGY

The paper analyzes the evolution of decision support systems. An approach to the development of intelligent system handling of knowledge, which will reduce the time of finding relevant information, will increase the accuracy of the information, provide the ability to analyze the information at different levels of decision-making, ensure efficiency and accessibility of information. DOI: http://dx.doi.org/10.12731/2227-930X-2013-4-3

Data‐driven decision making for the enterprise: an overview of business intelligence applications

PurposeThe purpose of this paper is to inform the knowledge worker about Business Intelligence (BI) – its origins, value in decision‐making processes, and currently available BI applications.Design/methodology/approachThe paper discusses the evolution of decision support systems (DSS) and their close relationship with the business intelligence arena. Open source and commercial BI products are also identified.FindingsThe data that are accessed by BI applications must be accurate if managers of knowledge enterprises are to make informed decisions. Effective partnerships for business and information entities should be observed when BI tools are introduced to an enterprise. Buy‐in from top management is absolutely necessary for the successful implementation of BI tools.Originality/valueThe use of BI applications aids a knowledge enterprise by promoting efficiency within an organization, particularly by using analytical methods to provide valuable decision‐making knowledge to minimize operating costs and to accurately forecast market trends.

Bringing Decision Support to the Customer Level

Customer-targeted systems represent a logical next step in the evolution of decision support systems (DSSs), but they have not proliferated because of organizational concerns regarding usage and satisfaction. A six-month study of an interactive telephone system in the financial services sector suggests that these concerns are unwarranted and that the initial cost of designing such systems is offset by increased customer satisfaction and more efficient use of organizational resources.