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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.


Papers
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Proceedings ArticleDOI
01 Dec 2013
TL;DR: The paper aims to demonstrate that Big data analytics can be used as a catalyst for generating and increasing value for organizations by improving various business parameters and establish that big data analytics supports creation, enhancement and improvement of various business services to significantly improve customer experience as well as value creation for organizations.
Abstract: The term `Big Data' is used to represent collection of such a huge amount of data that it becomes impossible to manage and process data using conventional database management tools Big Data is defined by three important parameters `Volume' - Size of Data, `Velocity' - Speed of increase of data and `Variety' - Type of Data Big data analytics is the process of analyzing this ever growing Big Data The goal of every organization is to maximize its value for its stake holders The paper aims to demonstrate that Big data analytics can be used as a catalyst for generating and increasing value for organizations by improving various business parameters Furthermore, by utilizing case studies the paper also aims to establish that big data analytics supports creation, enhancement and improvement of various business services to significantly improve customer experience as well as value creation for organizations

19 citations

Journal ArticleDOI
TL;DR: In this article, a critical essay reflecting the experience and reflections of the author with many ideas drawn from and extending selected items from project management, artificial intelligence (AI) and analytics literatures is presented.
Abstract: PurposeThe purpose of this essay is to illustrate how project management “pull” and AI or analytics technology “push” are likely to result in incremental and disruptive evolution of project management capabilities and practices.Design/methodology/approachThis paper is written as a critical essay reflecting the experience and reflections of the author with many ideas drawn from and extending selected items from project management, artificial intelligence (AI) and analytics literatures.FindingsNeither AI nor sophisticated analytics is likely to elicit hands on attention from project managers, other than those producing AI or analytics-based artifacts or using these tools to create their products and services. However, through the conduit of packaged software support for project management, new tools and approaches can be expected to more effectively support current activities, to streamline or eliminate activities that can be automated, to extend current capabilities with the availability of increased data, computing capacity and mathematically based algorithms and to suggest ways to reconceive how projects are done and whether they are needed.Research limitations/implicationsThis essay includes projections of possible, some likely and some unlikely, events and states that have not yet occurred. Although the hope and purpose are to alert readers to the possibilities of what may occur as logical extensions of current states, it is improbable that all such projections will come to pass at all or in the way described. Nonetheless, consideration of the future ranging from current trends, the interplay among intersecting trends and scenarios of future states can sharpen awareness of the effects of current choices regarding actions, decisions and plans improving the probability that the authors can move toward desired rather than undesired future states.Practical implicationsProject managers not involved personally with creating AI or analytics products can avoid mastering detailed skill sets in AI and analytics, but should scan for new software features and affordances that they can use enable new levels of productivity, net benefit creation and ability to sleep well at night.Originality/valueThis essay brings together AI, analytics and project management to imagine and anticipate possible directions for the evolution of the project management domain.

19 citations

Journal Article
TL;DR: The ongoing market transformation process indicating a shift from tra- ditional value chains toward value networks—a change which, if it is sustainable, would seriously threaten the business models of well-established data pro- viders, such as Dun & Bradstreet, for example.
Abstract: Data management seems to experience a renaissance today. One par- ticular trend in the so-called data economy has been the emergence of business models based on the provision of high-quality data. In this context, the paper examines business models of business partner data providers. The paper ex- plores as to how and why these business models differ. Based on a study of six cases, the paper identifies three different business model patterns. A resource- based view is taken to explore the details of these patterns. Furthermore, the pa- per develops a set of propositions that help understand why the different busi- ness models evolved and how they may develop in the future. Finally, the paper discusses the ongoing market transformation process indicating a shift from tra- ditional value chains toward value networks—a change which, if it is sustaina- ble, would seriously threaten the business models of well-established data pro- viders, such as Dun & Bradstreet, for example.

19 citations

Journal ArticleDOI
TL;DR: This manuscript developed a case study in a mid-sized European port, in which Process Mining—an emerging type of business analytics—was applied to a seven-month dataset from the freight export process, and enabled enhancements in the overall export time length.
Abstract: The current digitalization trend, the increased attention towards sustainability, and the spread of the business analytics call for higher efficiency in port operations and for investigating the quantitative approaches for maritime logistics and freight transport systems. Thus, this manuscript aims at enabling analytics-driven improvements in the port transportation processes efficiency by streamlining the related information flow, i.e., by attaining shorter time frames of the information and document sharing among the export stakeholders. We developed a case study in a mid-sized European port, in which we applied Process Mining (PM)—an emerging type of business analytics—to a seven-month dataset from the freight export process. Four process inefficiencies and an issue that can jeopardize the reliability of the time performance measurements were detected, and we proposed a draft of solutions to cope with them. PM enabled enhancements in the overall export time length, which might improve the vessels’ turnover and reduce the corresponding operational costs, and supported the potential re-design of performance indicators in process control and monitoring. The results answer the above-mentioned calls and they offer a valuable, analytics-based alternative to the extant approaches for improving port performance, because it focuses on the port information flow, which is often related to sustainability issues, rather than the physical one.

19 citations

Proceedings ArticleDOI
Ta Hsin Li1, Rong Liu1, Noi Sukaviriya1, Ying Li1, Jeaha Yang1, Michael Sandin1, Juhnyoung Lee1 
27 Jun 2014
TL;DR: This paper focuses on ticket analytics and some key statistical techniques applied in the analyses, and uses real-data examples to demonstrate these techniques and discusses major challenges of ticket analyses.
Abstract: An important IT service outsourcing business is to resolve incidents related to IT infrastructures our clients contract our company to support. Incidents are recorded as structured and unstructured data in tickets, which contain various characteristics

19 citations


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