Topic
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.
Papers published on a yearly basis
Papers
More filters
01 Mar 2014
TL;DR: In order for manufacturers to take advantage of the use of data and analytics for better operational performance, complementary resources such as fact-based SCM initiatives must be combined with BA initiatives focusing on data quality and advanced analytics.
Abstract: This study is interested in the impact of two specific business analytic (BA) resources-accurate manufacturing data and advanced analytics-on a firms' operational performance. The use of advanced analytics, such as mathematical optimization techniques, and the importance of manufacturing data accuracy have long been recognized as potential organizational resources or assets for improving the quality of manufacturing planning and control and of a firms' overall operational performance. This research adopted a contingent resource based theory (RBT), suggesting the moderating and mediating role of fact-based SCM initiatives as complementary resources. This research proposition was tested using Global Manufacturing Research Group (GMRG) survey data and was analyzed using partial least squares/structured equation modeling. The research findings shed light on the critical role of fact-based SCM initiatives as complementary resources, which moderate the impact of data accuracy on manufacturing planning quality and mediate the impact of advanced analytics on operational performance. The implication is that the impact of business analytics for manufacturing is contingent on contexts, specifically, the use of fact-based SCM initiatives such as TQM, JIT, and statistical process control. Moreover, in order for manufacturers to take advantage of the use of data and analytics for better operational performance, complementary resources such as fact-based SCM initiatives must be combined with BA initiatives focusing on data quality and advanced analytics.
151 citations
TL;DR: In this paper, the authors propose an enterprise modeling approach to bridge the business-level understanding of the enterprise with its representations in databases and data warehouses, focusing especially on reasoning about situations, influences, and indicators.
Abstract: Business intelligence (BI) offers tremendous potential for business organizations to gain insights into their day-to-day operations, as well as longer term opportunities and threats. However, most of today's BI tools are based on models that are too much data-oriented from the point of view of business decision makers. We propose an enterprise modeling approach to bridge the business-level understanding of the enterprise with its representations in databases and data warehouses. The business intelligence model (BIM) offers concepts familiar to business decision making--such as goals, strategies, processes, situations, influences, and indicators. Unlike many enterprise models which are meant to be used to derive, manage, or align with IT system implementations, BIM aims to help business users organize and make sense of the vast amounts of data about the enterprise and its external environment. In this paper, we present core BIM concepts, focusing especially on reasoning about situations, influences, and indicators. Such reasoning supports strategic analysis of business objectives in light of current enterprise data, allowing analysts to explore scenarios and find alternative strategies. We describe how goal reasoning techniques from conceptual modeling and requirements engineering have been applied to BIM. Techniques are also provided to support reasoning with indicators linked to business metrics, including cases where specifications of indicators are incomplete. Evaluation of the proposed modeling and reasoning framework includes an on-going prototype implementation, as well as case studies.
147 citations
TL;DR: Which organisations are building a competitive advantage over less advanced competitors through a better understanding of their audience, and what lessons others can take from their approaches are looked at.
Abstract: News organisations are increasingly embracing the use of analytics and metrics as part of editorial decision making, but what constitutes a sophisticated analytics strategy? And why are so many media organisations still using such a rudimentary approach to analytics? This new report by the Reuters Institute for the Study of Journalism looks at which organisations are building a competitive advantage over less advanced competitors through a better understanding of their audience, and what lessons others can take from their approaches. Based on over 30 interviews with senior figures involved in developing analytics in news organisations, the report, authored by Cherubini and Nielsen, examines analytics at leading organisations, provides a review of current trends – and looks at where others are falling behind. The report examines several best-practice case studies, including sophisticated tailored approaches at The Financial Times, The Huffington Post, The Wall Street Journal, Die Welt, and The BBC. It breaks down approaches used by media organisations as falling into three categories – editorial, generic and rudimentary, and examines the tools, organisational support and news room culture that must be in place to develop the high-level tools required for media organisations to truly understand how their audiences are engaging with their content.
146 citations
Book•
01 Jan 2013
TL;DR: This guide walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect, and helps you understand the many data-mining techniques in use today.
Abstract: Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. You'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization - and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you're to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates
145 citations
TL;DR: This introductory article provides a review of the state-of-the-art research in business intelligence in risk management, and of the work that has been accepted for publication in this issue.
145 citations