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
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01 Jan 2002
TL;DR: The MS in Business Analytics is targeted both at students with undergraduate degrees from business and other disciplines who seek specialized knowledge and training to work in increasingly data-rich business environments, as well as working professionals looking to develop business intelligence and analytics capability.
Abstract: The MS in Business Analytics combines foundational knowledge in (1) data management and business intelligence, (2) applied statistics, machine learning, and data mining, with (3) knowledge of business functional areas, analytics applications in specific contexts, and (4) understanding of analytics and information management practice and strategy in organizations. It is targeted both at students with undergraduate degrees from business and other disciplines who seek specialized knowledge and training to work in increasingly data-rich business environments, as well as working professionals looking to develop business intelligence and analytics capability.
22 citations
14 Nov 2013
TL;DR: The paper provides a general overview of the motivations behind the proposal of this track, including a review of the current state of Learning Analytics, from its origins and a definition of the term to some pending issues for future research.
Abstract: There is increasing interest in Learning Analytics, a concept that has evolved in the last years from the status of buzzword or trend to the generation of a discipline within the educational field. In this paper, we introduce the Track on Learning Analytics within the Technological Ecosystems for Enhancing Multiculturality 2013 Conference.The paper provides a general overview of the motivations behind the proposal of this track, including a review of the current state of Learning Analytics, from its origins and a definition of the term to some pending issues for future research, which are addressed by the papers participating in the track and which hopefully will be expanded by future studies. The paper also includes an insight on the submission management and participants' selection process, which is followed by a detailed summary of the manuscripts accepted for participation in the conference and how they are linked to the track objectives.
22 citations
TL;DR: IT departments are under pressure to serve their enterprises by professionalizing their business intelligence (BI) operation by linking their systematic and structured approach to BI to the business itself.
Abstract: IT departments are under pressure to serve their enterprises by professionalizing their business intelligence (BI) operation. Companies can only be effective when their systematic and structured approach to BI is linked into the business itself.
22 citations
TL;DR: In this article, Babson College educators teach emergent strategy, which uses ideation, prototyping, and iteration, tools more familiar to entrepreneurs and design thinkers, and they do it using design thinking.
Abstract: Analyzing a new business opportunity with traditional business tools, which are based on the logic of predictability, can handicap a new project before it even begins. That's why Babson educators teach emergent strategy, and they do it using design thinking.
When the variables are unknown, predicting outcomes a priori with traditional business analytics is often a losing proposition. At Babson College, educators teach emergent strategy, which uses ideation, prototyping, and iteration—tools more familiar to entrepreneurs and design thinkers.
22 citations
01 Dec 2012
TL;DR: This research analyzes the problem of differences among individuals in terms of their preferences for data attributes and proposes a set of solutions that focus on principles of employee empowerment, decentralization, and mechanisms to measure and reward individuals for their data quality efforts.
Abstract: The collection, representation, and effective use of organizational data are important to a firm because these activities facilitate the increasingly important analysis needed for business operations and business analytics Poor data quality can be a major cause for damages or losses of organizational processes The many tasks that individuals perform within an organization are linked and normally require access to shared data These linkages are often documented as process flow diagrams that connect the data inputs and outputs of individuals However, in such a connected setting, the differences among individuals in terms of their preferences for data attributes such as timeliness, accuracy, and others, can cause data quality problems For example, individuals at the head of a process flow could bear all of the costs of capturing high quality data but not receive all of the benefits, even though the rest of the organization benefits from their diligence Consequently, these individuals, in absence of any managerial intervention, might not invest enough in data quality This research analyzes this problem and proposes a set of solutions to this, and similar, organizational data quality problems The solutions focus on principles of employee empowerment, decentralization, and mechanisms to measure and reward individuals for their data quality efforts
22 citations