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
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29 Apr 2012TL;DR: This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.
Abstract: Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. Two research communities -- Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.
801 citations
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TL;DR: A critical research agenda is outlined to explore and conceptualize evident changes in business models and society arising from these technological advances and the potential effects of digitization and big data analytics on employment - especially in the context of cognitive tasks.
Abstract: In the era of accelerating digitization and advanced big data analytics, harnessing quality data for designing and delivering state-of-the-art services will enable innovative business models and management approaches (Boyd and Crawford, 2012; Brynjolfsson and McAfee, 2014) and yield an array of consequences. Among other consequences, digitization and big data analytics reshape business models and impact employment amongst knowledge workers - just as automation did for manufacturing workers. This Viewpoint paper considers the mechanisms underlying how digitization and big data analytics drive the transformation of business and society and outlines the potential effects of digitization and big data analytics on employment - especially in the context of cognitive tasks. Its aim is to outline a critical research agenda to explore and conceptualize evident changes in business models and society arising from these technological advances.
743 citations
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TL;DR: Greller, W., & Drachsler, H. (2012).
Abstract: Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics.
Educational Technology & Society, 15(3), 42–57.
664 citations
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TL;DR: The state-of-the-art of data mining and analytics are reviewed through eight unsupervisedLearning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms.
Abstract: Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.
657 citations
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TL;DR: Business intelligence (BI) has become a strategic initiative and is now recognized by CIOs and business leaders as instrumental in driving business effectiveness and innovation.
Abstract: Business intelligence (BI) is now widely used, especially in the world of practice, to describe analytic applications. BI is currently the top-most priority of many chief information officers. BI has become a strategic initiative and is now recognized by CIOs and business leaders as instrumental in driving business effectiveness and innovation. BI is a process that includes two primary activities: getting data in and getting data out. Getting data in, traditionally referred to as data warehousing, involves moving data from a set of source systems into an integrated data warehouse. Getting data in delivers limited value to an enterprise; only when users and applications access the data and use it to make decisions does the organization realize the full value from its data warehouse. Thus, getting data out receives most attention from organizations. This second activity, which is commonly referred to as BI, consists of business users and applications accessing data from the data warehouse to perform enterprise reporting, OLAP, querying, and predictive analytics.
612 citations