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.

State of the art in business analytics: themes and collaborations

Abstract: In this study, we illustrate the most recent stage in the scholarly development of the field of Business Analytics. Using around 23,000 research publications collected from the Web of Science, we network-analyzed four types of dataset that include keywords, titles, institutions, and countries. It is noteworthy that we found a thematic cluster focusing on analytical techniques and tools, particular with the rapid dissemination of “big data”-related studies. It is not surprising that universities and institutions in advanced countries in the West still occupy the central position of the collaboration network. However, we can discern a global shift toward certain developing and transitional economies in the world science base.

Multi Criteria Decision Making in Financial Risk Management with a Multi-objective Genetic Algorithm

Abstract: A huge amount of data is being collected and stored by financial institutions like banks during their operations. These data contain the most important facts about the institutions and its customers. A good and efficient data analytics system can find patterns in this huge data source that can be used in actionable knowledge creation. Actionable knowledge is the knowledge that can be put to decision making and take some positive action towards better performance of organizations. This actionable knowledge is termed Business Intelligence by data scientists. Business Intelligence and Analytics is the process of applying data mining techniques to organizational or corporate data to discover patterns. Business Intelligence and Business Analytics are emerging as important and essential fields both for data scientists and organizations. Risk analysis, fraud detection, customer retention, customer satisfaction analysis and actuarial analysis are some of the areas of application of business intelligence and analytics. Credit risk analysis is an important part of a successful financial institution particularly in the banking sector. The current study takes this risk analysis in financial institutions and reviews the state of the art in using data analytics or data mining techniques for financial risk analysis. The analysis of risk from financial data depends on several factors that are both objective and subjective. Hence it is a multi-criteria decision problem. The study also proposes a multi-objective genetic algorithm (MOGA) for analyzing financial data for risk analysis and prediction. The proposed MOGA is different from other evolutionary systems in that a memory component to hold the rules is added to the system while other systems in the literature are memory less. The algorithm is applied to bench mark data sets for predicting the decision on credit card and credit applications. The preliminary results are encouraging and show light towards better decision making in reducing risks.

Electronic Companion to: Business Analytics for Flexible Resource Allocation Under Random Emergencies

Abstract: This Electronic Companion contains supporting materials for the paper: Angalakudati, Balwani, Calzada, Chatterjee, Perakis, Raad, Uichanco (2014), “Business Analytics for Flexible Resource Allocation Under Random Emergencies”

Improving Cyber Security Awareness and Training Programs with Data Analytics

Abstract: This article it is a modest attempt to explore the issues facing cyber security awareness and training programs and potential benefits of using learning analytics, an emerging field in data analytics, for combining existing data sources to provide additional value to these programs This article was written under the assumption that awareness and training are valid preventive controls and therefore the pros and cons of implementing such programs are not being discussed

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

Abstract: High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.