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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
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Journal ArticleDOI
31 Oct 2013
TL;DR: The objective of this paper is to provide both a research and teaching introduction to business analytics in the context of both current and prospective perspective of the business analytics domain.
Abstract: In recent times, business analytics and big data have gained momentumboth in industry practice and academic research. The objective of this paper is to provide both a research and teaching introduction to business analytics in the context of both current and prospective perspective of the business analytics domain. It begins by providing a quick overviewof the three types of analytics. To assist the future analytics professionals, we identify, group and discuss nine different participants of the analytics industry into clusters. We then include a brief description of some current research projects under way in our team. We also note some research opportunities in Big Data analytics. The paper also concludes with a discussion of teaching opportunities in analytics.

26 citations

Proceedings ArticleDOI
03 Dec 2013
TL;DR: A researcher-centric prescriptive analytics system, InSciTe advisory, is introduced to provide researchers with advice of their future research direction and strategy and it is useful tool to understand the designated researcher in the perspective of prescriptives as well as descriptive analytics.
Abstract: We introduce a prescriptive analytics system, InSciTe advisory, to provide researchers with advice of their future research direction and strategy. The system analyzes several thousands of heterogeneous types of data sources such as papers, patents, reports, Web news, Web magazines, and collective intelligence data. It consists of two main parts of descriptive analytics and prescriptive analytics. Once given a researcher, the descriptive analytics part provides results from activity history and research power w.r.t the designated researcher. Then, prescriptive analytics part suggests a group of role model researchers to the researcher, as well as how to be like the role model researchers. The prescription for the researcher is provided according to 5W1H questions and their corresponding answers. All of the analytical results and their explanations about the given researcher are automatically generated and saved to a report. This researcher-centric prescriptive analytics has not been introduced before and it is useful tool to understand the designated researcher in the perspective of prescriptive as well as descriptive analytics.

26 citations

Journal ArticleDOI
TL;DR: The relevance and use of time series analyses for Big Data and business analytics is examined by discussing the emergence of Big Data in business, presenting time series models, and providing an example of how timeseries models can be efficiently and effectively applied in accounting and auditing using Big Data.
Abstract: The application of Big Data and time series models is currently at an early stage. This paper examines the relevance and use of time series analyses for Big Data and business analytics by discussing the emergence of Big Data in business, presenting time series models, and providing an example of how time series models can be efficiently and effectively applied in accounting and auditing using Big Data. Using sophisticated Big Data and time series models, millions of transactions can be searched to spot patterns and detect abnormalities and irregularities. The time series model and Big Data analysis presented in this paper provide policy, practical, educational, and research implications. Businesses and management can use our suggested time series model and Big Data analysis in their predictive models of managerial strategies, decisions, and actions. Business schools and accounting programs can integrate the time series model, Big Data, and data analytics into business and accounting education.

26 citations

Journal ArticleDOI
TL;DR: This study presents an investigation of Trustev, a global provider of digital verification technology, and its development of the profile-based social fingerprinting fraud detection solution, and constructs a framework and reveals a road map for organizations to become analytically capable in online fraud detection.

26 citations

Proceedings ArticleDOI
24 Mar 2014
TL;DR: Researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models are introduced.
Abstract: Learning analytics (LA) as a field remains in its infancy. Many of the techniques now prominent from practitioners have been drawn from various fields, including HCI, statistics, computer science, and learning sciences. In order for LA to grow and advance as a discipline, two significant challenges must be met: 1) development of analytics methods and techniques that are native to the LA discipline, and 2) practitioners in LA to develop algorithms and models that reflect the social and computational dimensions of analytics. This workshop introduces researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models.

26 citations


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