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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.


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Journal ArticleDOI
TL;DR: This research work is to find meaningful indicators or metrics in a learning context and to study the inter-relationships between these metrics using the concepts of Learning Analytics and Educational Data Mining, thereby, analyzing the effects of different features on student’s performance using Disposition analysis.
Abstract: Learning Analytics (LA) is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study like business intelligence, web analytics, academic analytics, educational data mining, and action analytics. The main objective of this research work is to find meaningful indicators or metrics in a learning context and to study the inter-relationships between these metrics using the concepts of Learning Analytics and Educational Data Mining, thereby, analyzing the effects of different features on student's performance using Disposition analysis. In this project, K-means clustering data mining technique is used to obtain clusters which are further mapped to find the important features of a learning context. Relationships between these features are identified to assess the student's performance.

62 citations

Journal ArticleDOI
TL;DR: In this article, the authors explore decisions related to formal empirical tests of business models and interpretations and uses of those tests and reveal that the managers' response to the test results is consistent with that expected of Bayesian-rational agents.
Abstract: This study explores decisions related to formal empirical tests of business models and interpretations and uses of those tests. Business models describe managers' rationales as to how their organizations will achieve success. This study documents a test of one company's business model under seemingly favorable conditions for such a test—a successful single-product firm following a consistent strategy over a long period of time with stable management and publicly traded stock. Although the findings provide only weak support for the hypothesized business model, the confidence of the company's top managers in their business model remained high. Further analyses reveal that the managers' response to the test results is consistent with that expected of Bayesian-rational agents. Our analyses provide the basis for development of a framework for understanding the expected value of testing business models in various circumstances. This framework might explain apparent contradictions between previous studi...

62 citations

Book
01 Jan 1973

61 citations

Journal ArticleDOI
31 Dec 2011
TL;DR: In this article, the authors identify the factors that are present in successful business intelligence projects and organize them into a framework of critical success factors, such as the presence of a specific business need and a clear vision to guide the project.
Abstract: Business Intelligence can bring critical capabilities to an organization, but the implementation of such capabilities is often plagued with problems. Why is it that certain projects fail, while others succeed? The aim of this article is to identify the factors that are present in successful Business Intelligence projects and to organize them into a framework of critical success factors. A survey was conducted during the spring of 2011 to collect primary data on Business Intelligence projects. Findings confirm that Business Intelligence projects are wrestling with both technological and non-technological problems, but the non-technological problems are found to be harder to solve as well as more time consuming than their counterparts. The study also shows that critical success factors for Business Intelligence projects are different from success factors for Information Systems projects in general. Business Intelligences projects have critical success factors that are unique to the subject matter. Major differences can be found primarily among non-technological factors, such as the presence of a specific business need and a clear vision to guide the project. Success depends on types of project funding, the business value provided by each iteration in the project and the alignment of the project to a strategic vision for Business Intelligence at large. Furthermore, the study provides a framework for critical success factors that, explains sixty-one percent of variability of success for projects. Areas which should be given special attention include making sure that the Business Intelligence solution is built with the end users in mind, that the Business Intelligence solution is closely tied to the company’s strategic vision and that the project is properly scoped and prioritized to concentrate on the best opportunities first.

61 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023131
2022262
2021176
2020169
2019185
2018203