Business analytics

About: Business analytics is a research topic. Over the lifetime, 3593 publications have been published within this topic receiving 84601 citations. The topic is also known as: Business Analytics & business analytics.

The quality movement from six perspectives

Abstract: Purpose The purpose of this paper is to examine the quality movement in the framework of an organizing taxonomy model from six perspectives: global trend, national mandate, industry trend, organizational strategy, operational strategy, and personal philosophy. Design/methodology/approach The authors use the organizing taxonomy model to analyze the quality movement from each of the six perspectives in terms utilizing a diverse range of key questions, characteristics, and issues which must be addressed. Findings The analysis shows that viewing the quality movement from these various perspectives can help practitioners in developing an understanding of the quality movement not only from a historical standpoint, but also in terms of current requirements and future demands. This can also benefit quality management researchers in terms of organizing the focus of their research on the various perspectives. The organizing taxonomy model can also be used to assess other phenomena such as lean, supply chain management, knowledge management, and business analytics which are similarly impacting organizations across all industries and throughout the world. Originality/value The paper presents a fresh look at the quality movement from a range of perspectives and provides insight into an organized method of assessing major movements that continue to impact businesses globally.

Integration: Knowledge Management and Business Intelligence

Abstract: As business competition intensifies and the market environment becomes increasingly complex, more and more enterprises learn to make use of Knowledge Management (KM) and Business Intelligence (BI) in order to improve corporate decision-making capacity and efficiency. However, there is still not a unified view for the concept of KM and BI and the relationship between the two in academia and the business world, which may bring about confusion and errors in theory study and application. this paper is trying to provide a thorough analysis of the differences between BI and KM and to establish a framework for relating one field to the other. And it comes to a conclusion that in business management and decision-making process, both BI and KM must be effectively integrated to give full play to their complementary functions.

Approaches To Business History

Abstract: Everybody in this group will be aware of the fact that business history is neither of American nor of recent vintage. The first full-fledged company history was published in 1825 to celebrate the one hundredth anniversary of the Lauchhammer Iron Works in Saxony; and Professor Herman Freudenberger has recently been able to trace the beginnings of company history to the eighteenth century. His research will be published this spring. As early as the 1900's, Professor Richard Ehrenberg of the University of Rostock was the first to see that what we call business history could be developed into an academic discipline, and his book on the enterprises of the Siemens Brothers marks the beginning of company history satisfying the most rigid modern scientific standards. In fact, Ehrenberg's work had an influence on the thinking of Professor N. S. B. Gras.

preCEP: facilitating predictive event-driven process analytics

Abstract: The earlier critical decision can be made, the more business value can be retained or even earned. The goal of this research is to reduce a decision maker's action distance to the observation of critical events. We report on the development of the software tool preCEP that facilitates predictive event-driven process analytics (edPA). The tool enriches business activity monitoring with prediction capabilities. It is implemented by using complex event processing technology (CEP). The prediction component is trained with event log data of completed process instances. The knowledge obtained from this training, combined with event data of running process instances, allows for making predictions at intermediate execution stages on a currently running process instance's future behavior and on process metrics. preCEP comprises a learning component, a run-time environment as well as a modeling environment, and a visualization component of the predictions.

Data-Need Fit – Towards Data-Driven Business Model Innovation

Abstract: Today’s world is one of growing data, yet few companies have succeeded in leveraging data for novel business models. This paper aims to provide an evaluated approach to understanding what kind of data is available and to matching data with potential user needs for compelling value propositions. For this purpose, the paper introduces, on the one hand, the Data Canvas as a new method for considering data resources systematically in the development of business models and, on the other hand, the Data-Need Fit as a conceptual basis for the established business model innovation process according to Osterwalder, Pigneur, Bernarda & Smith (2014). Applied in a case study, the Data Canvas proved simple to use. Integrated into a service design process, it may help companies to leverage data as a resource in business model innovation.