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.

Demystifying analytical information processing capability: The case of cybersecurity incident response

Abstract: Little is known about how organizations leverage business analytics (BA) to develop, process, and exploit analytical information in cybersecurity incident response (CSIR). Drawing on information processing theory (IPT), we conducted a field study using a multiple case study design to answer the following research question: How do organizations exploit analytical information in the process of cybersecurity incident response by using business analytics? We developed a theoretical framework that explains how organizations respond to the dynamic cyber threat environment by exploiting analytical information processing capability in the CSIR process. This, in turn, leads to positive outcomes in enterprise security performance, delivering both strategic and financial benefits. Our findings contribute to the BA and cybersecurity literature by providing useful insights into BA applications and the facilitation of analytics-driven decision making in CSIR. Further, they contribute to IPT by providing new insights about analytical information needs, mechanisms to seek analytical information, and analytical information use in the process of CSIR.

Analytics based decision making

Abstract: Purpose – This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics. Design/methodology/approach – The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day. Findings – Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions. Originality/value – This is a historical perspective from the early days of analytics to present day use of analytics.

Internet of Things and its impact on Business Analytics

Abstract: Objectives: To study the Impact of IoT data on Business Analytics. Methods/Statistical Analysis: An exploratory research to study the Impact of IoT data on Business Analytics was conducted. Through the Literature review process, various preliminary information on IoT and Business Analytics including the Advanced Analytics was gathered. Research papers, Journals, Internet Sites and books were used to collate the relevant content on the subject. Analysis of all the relevant examples was done. The gaps in the area of research were identified to arrive at the problem statement and its impact on Organizations. Findings: World is moving very rapidly towards the Industry 4.0, where the most impactful position in almost all the areas would be of IoT (Internet of Things). Profoundly IoT allows the connection between people and things at any point and any given place with devices that can transmit data over the network. Thus, the Smart environment evolves which consists of Smart devices transmitting the real time data over Smart networks. Business Decision Making is facilitated with a greater accuracy with real time data transmitted coupled with the relevant information. IoT and Business Analytics based on IoT data is gaining a lot of significance and importance in larger organizations. Right decision making at the right time and at the right place is the key to successful businesses in today’s dynamic environment. Application: The real time analytics becomes a reality with IoT data transmitted over the Internet and consumed by the Business Analytics. Use of Past data is to analyze and identify the hidden trends so that future predictability is built. Current data helps validate the relevancy of the Business Analytics Model. It also helps in taking some course corrections as and when required.

Diagnosis of delivery vulnerability in a logistics system for logistics risk management

Abstract: Delivery vulnerability is a critically important theme in logistics risk management. However, while logistics service providers often collect and retain massive amounts of logistics data, they seldom utilize such information to diagnose recurrent day-to-day logistics risks. Hence, the purpose of this paper is to investigate delivery vulnerabilities in a logistics system using its own accumulated data.,This study utilizes pragmatic business analytics to derive insights on logistics risk management from operations data in a logistics system. Additionally, normal accident theory informs the discussion of its management implications.,This study’s analytical results reveal that a tightly coupled logistics system can align with normal accident theory. Specifically, the vulnerabilities of such a system comprise not only multi-components but also interactive ones.,The tailored business analytics comprise a research foundation for logistics risk management. Additionally, the important research implications of this study’s analytical results arrived at via such results’ integration with normal accident theory demonstrate the value of that theory to logistics risk management.,The trade-offs between logistics risk and logistics-system efficiency should be carefully evaluated. Moreover, improvements to such systems’ internal resilience can help to alleviate potential logistics vulnerabilities.,This pioneering analytical study scrutinizes the critical vulnerability issues of a logistics service provider and therefore represents a valuable contribution to the field of logistics risk management. Moreover, it provides a guide to retrieving valuable insights from existing stockpiles of delivery-vulnerability data.