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.

Business Process Modeling Language for Performance Evaluation

Abstract: Evaluation of business processes is important for analysis and improvement of an organization. Different methods are used to evaluate the performance like statistics or visualization. However, these methods meet demands mainly on the top organizational level. There is insufficient support to evaluate processes at the process managerial level leading to a limited visibility of deficiencies in business processes at process level. In this paper, we address this challenge and focus on the relation between evaluation of business processes and their representation at the process managerial level. In our research, we follow the design science methodology in order to provide business process models for performance analysis. We also provide constructs and patterns of our proposed modeling language for analysis and improvement of business processes. The analytical business process modeling language is further explained with the help of a case study and demonstrated by extending an existing modeling language.

Modeling a Business Process and Querying the Resulting Database: Analyzing RFID Data to Develop Business Intelligence

Abstract: This case supports the high‐level objective of learning to model business situations to design databases and query them to solve business problems. Students analyze Radio Frequency Identification (RFID) and related fulfillment data to develop business intelligence for a grower of bedding plants selling to big‐box retailers. Students prepare a business process diagram to understand the business process; identify objectives for detecting and correcting data errors and analyzing business performance; construct a Resource, Event, Agent (REA) diagram to specify a database design; implement the database and query it to satisfy business objectives; communicate results; document data error correction procedures; and prepare adjusting journal entries. The case is appropriate for students with moderate database querying proficiency. The case's managerial and financial accounting and internal control implications make it suitable for courses in Accounting Information Systems (AIS) and database systems for ...

The Role of Business Capabilities in Supporting Organization Agility and Performance During the COVID-19 Pandemic: An Empirical Study in Indonesia

Abstract: This study aims to analyze the important role of business analytics capability, information quality, and innovation capability in influencing organization agility and organization performance during the Covid-19 pandemic. Data was collected from 76 companies from various sectors in Indonesia. Structural Equation Model-Partial Least Square (SEM-PLS) analysis was conducted to analyze the relationship between variables and test a series of hypotheses. Importance-Performance Matrix Analysis (IPMA), a useful analysis approach in PLS-SEM, is used, which extends the results of the estimated path coefficient (importance) by adding a dimension that considers the average values of the latent variable scores (performance). The IPMA approach examines not only the performance of an item but also the importance of that item. The results show that business analytics capability has a significant effect on information quality and innovation capability which then affects organization agility. Organizational performance is influenced by organizational agility. IPMA results show that organizational agility has the highest level of impact on organizational performance. This study will assist companies in planning business analytics, improving information quality, increasing innovation capability, and ultimately increasing agility and performance during the Covid-19 pandemic. This study will add to existing knowledge about previous literature, especially in the Covid-19 pandemic situation.

Large-Scale Data Analytics

Abstract: This edited book collects state-of-the-art research related to large-scale data analytics that has been accomplished over the last few years. This is among the first books devoted to this important area based on contributions from diverse scientific areas such as databases, data mining, supercomputing, hardware architecture, data visualization, statistics, and privacy. There is increasing need for new approaches and technologies that can analyze and synthesize very large amounts of data, in the order of petabytes, that are generated by massively distributed data sources. This requires new distributed architectures for data analysis. Additionally, the heterogeneity of such sources imposes significant challenges for the efficient analysis of the data under numerous constraints, including consistent data integration, data homogenization and scaling, privacy and security preservation. The authors also broaden reader understanding of emerging real-world applications in domains such as customer behavior modeling, graph mining, telecommunications, cyber-security, and social network analysis, all of which impose extra requirements for large-scale data analysis. Large-Scale Data Analytics is organized in 8 chapters, each providing a survey of an important direction of large-scale data analytics or individual results of the emerging research in the field. The book presents key recent research that will help shape the future of large-scale data analytics, leading the way to the design of new approaches and technologies that can analyze and synthesize very large amounts of heterogeneous data. Students, researchers, professionals and practitioners will find this book an authoritative and comprehensive resource.

Process Analytics Approach for R&D Project Selection

Abstract: RD however, they are not effective, as they fail to consider the real insight of core tasks. Other decision models that analyze core tasks are effective but inefficient when handling large amounts of submissions, and they suffer from irrelevant assignment. Furthermore, they largely ignore real deep insight of back-end data such as quality of the reviewers (e.g., quality and citation impact of their produced research) and the effect of social relationships in project selection processes that are essential for identifying reviewers for interdisciplinary proposal evaluation. In light of these deficiencies, this research proposes a novel hybrid process analytics approach to decompose the complex reviewer assignment process into manageable subprocesses and applies data-driven decision models cum process analytics systematically from a triangular perspective via the research analytics framework to achieve high operational efficiencies and high-quality assignment. It also analyzes big data from scientific databases and generates visualized decision-ready information to support effective decision making. The proposed approach has been implemented to aid the project selection process of the largest funding agency in China and has been tested. The test results show that the proposed approach has the potential to add great benefits, including cost saving, improved effectiveness, and increased business value.