The Future of Enterprise Information Systems

[First paragraph] Enterprise information systems (EIS) have been important enablers of crossfunctional processes within businesses since the 1990s. Often referred to as enterprise resource planning (ERP) systems, they were extended in line with electronic businesses to integrate with suppliers as well as customers. Today, EIS architectures comprise not only ERP, supply chain, and customer relationship management systems, but also business intelligence and analytics. Recently, the move towards decentralized technologies has created new perspectives for EIS. Information systems (IS) research has already addressed opportunities and challenges of these developments quite well, but what will be the pressing opportunities and challenges for supporting enterprises with IS in the coming years? The remainder of this discussion focuses on the future of EIS from diverse but complementary perspectives

Future role of the information systems executive

John F. Rockart, Leslie Ball, Christine V. Bullen

The future of information systems-using social systems to create protocols for the virtual environment (systems analysis through social analysis)

Information is the medium for communication, power-play, politics, and the building block for knowledge systems. It is associated with social interaction, and can be mediated by technology use. The paper argues that the key to understanding the impact of future technologies lies in the interaction between the social and technical environment. It suggests that future technologies such as virtual reality make necessary a move away from traditional methods of systems analysis and design. The interactive nature of such technology requires a validation in the social environment. The paper proposes the creation of protocols (a set of universally applicable standards) for the virtual environment. It suggests that information systems are split into three protocols: physical, learning, and cultural protocols. Finally it illustrates that their influence over each other can be understood by applying structuration theor

IS-Organization Coevolution: The Future of Information Systems

How information systems (IS) affect organizations and enable radical organizational change has been explored from a variety of perspectives. Through a case study in the investment banking industry, we will examine the changing nature of ISñorganization relationships focusing on the crucial role that IS play and will continue to play as organizations strive for competitive advantage in a global economy. By expanding on the organizational emergence theoretical framework of Truex et al. (1999) and drawing on the technology evolution literature, we aim to gain a deeper understanding of the coevolution between an IS and an investment banking company

Exploring the Future of Indian Health Information Systems

The Data Policy Roundtable - The Future of Indian Health Information Systems was convened to explore options and develop strategies for future Indian Health Service/Tribal/Urban (I/T/U) program information systems. The concerns and needs of both tribes who chose to contract/compact and those who did not were considered.The focus was on developing strategies to create a new Indian health information system, one that was not an Indian Health Service system but rather a system designed and supported by tribal and urban health care delivery organizations and the Indian Health Service.The participants represented a broad range of concerns and needs. They identified problems, issues, and solutions. Participants generously shared information and reports about the often extensive work their groups have done to assess their needs and study the options available to them to improve their systems to meet their needs.The group benefited from the candid expression of viewpoints coming from representatives of the tribes and urban groups, members of national and regional tribal health boards, medical professionals, and staff from various Indian Health Service divisions, industry experts, and interested observers. As the roundtable discussion of issues, concerns, and options progressed it became clear that in the future an Indian Health Information System would be driven increasingly by the needs of and ultimately directed by the tribes (both those who chose to contract/compact and those who did not) and urban Indian organizations. This report summarizes ten recommendations resulting from the roundtable

Lookahead Strategies for Sequential Monte Carlo

Based on the principles of importance sampling and resampling, sequential Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with complex stochastic dynamic systems. Many of these systems possess strong memory, with which future information can help sharpen the inference about the current state. By providing theoretical justification of several existing algorithms and introducing several new ones, we study systematically how to construct efficient SMC algorithms to take advantage of the "future" information without creating a substantially high computational burden. The main idea is to allow for lookahead in the Monte Carlo process so that future information can be utilized in weighting and generating Monte Carlo samples, or resampling from samples of the current state.Comment: Published in at http://dx.doi.org/10.1214/12-STS401 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Using quantum theory to reduce the complexity of input-output processes

All natural things process and transform information. They receive environmental information as input, and transform it into appropriate output responses. Much of science is dedicated to building models of such systems -- algorithmic abstractions of their input-output behavior that allow us to simulate how such systems can behave in the future, conditioned on what has transpired in the past. Here, we show that classical models cannot avoid inefficiency -- storing past information that is unnecessary for correct future simulation. We construct quantum models that mitigate this waste, whenever it is physically possible to do so. This suggests that the complexity of general input-output processes depends fundamentally on what sort of information theory we use to describe them.Comment: 10 pages, 5 figure