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 Plus Intelligence Plus Technology Equals Business Intelligence

Abstract: In this article the authors will show how the parallel developments of information technology at the operational business level and decision support concepts progressed through the decades of the twentieth century with only minimal success at strategic application. They will posit that the twin technological developments of the world-wide-web and very inexpensive mass storage provided the environment to facilitate the convergence of business operations and decision support into the strategic application of business intelligence.

Semantic-Based and Entity-Resolution Fusion to Enhance Quality of Big RDF Data

Abstract: Within an organisation, the quality in big data is a cornerstone to operational, transactional processes and to the reliability of business analytics for decision making In fact, as organizations are harnessing multi-sources data to rise the benefits of their business, the quality of data becomes important and crucial This paper presents a new approach to query big data sources using Resource Description Framework (RDF) representation to ensure data quality by harvesting more relevant and complete query results Our approach handles two important types of heterogeneity over multiple data sources: semantic heterogeneity and URI-based entity identification It proposes (1) a semantic entity resolution method based on inference mechanism using rules to manage the misunderstanding of data, in real world entities (2) Data Quality enhancement using MapReduce-based query rewriting approach includes the entity resolution results to infer and adds implicit data into query results (3) a parallel combination of MapReduce jobs of saturation and query rewriting inferences to handle transitive and cyclic rules for a richer rules’ expression language (4) experiments to assess the efficiency of the proposed approach over real big RDF data originating from insurance and synthetic data sets

Social Media Analytics as a Business Intelligence Practice: Current Landscape & Future Prospects

Abstract: Social media analytics is a nascent and emerging discipline that can help organizations formulate and implement measurement techniques for deriving insights from social media interactions and for evaluating the success of their own social media initiatives. Ultimately, a successful social media analytics program can enable businesses to improve their performance management initiatives across various business functions. However, businesses are still struggling with adopting, implementing and institutionalizing methodologies and techniques for an effective social media analytics program. This paper offers a business intelligence perspective of social media analytics with the aim to provide guidelines to help businesses align their social media programs, processes and technologies with the overall strategic objectives of the organization. Toward this, the paper outlines conceptual underpinnings of social media analytics and business intelligence, and draws upon findings from two online expert panels that were conducted to determine current practices, technologies and processes, and recommendations for businesses adopting social media analytics. By doing so, the paper hopes to offer a basis for establishing a baseline philosophy for businesses partaking various social media initiatives.

Memetic Algorithms for Business Analytics and Data Science: A Brief Survey.

Abstract: This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018.

Progressive Methods in Data Warehousing and Business Intelligence: Concepts and Competitive Analytics

Abstract: Recent technological advancements in data warehousing have been contributing to the emergence of business intelligence useful for managerial decision making. Progressive Methods in Data Warehousing and Business Intelligence: Concepts and Competitive Analytics presents the latest trends, studies, and developments in business intelligence and data warehousing contributed by experts from around the globe. Consisting of four main sections, this book covers crucial topics within the field such as OLAP and patterns, spatio-temporal data warehousing, and benchmarking of the subject.