This book provides a fi rst-hand account of business analytics and its implementation and an account of the brief theoretical framework underpinning each component of business analytics. The themes of the book include (1) learning the contours, scope, and boundaries of business analytics; (2) understanding the design aspects of an analytical organization; (3) providing knowledge focusing on developing business activities for fi nancial impact through functional analysis; and (4) deriving a whole gamut of business use cases in a variety of situations to apply business analytics techniques. The book gives a complete and insightful understanding of developing and implementing analytical solutions by analyzing concrete use cases.
See Full PDFMain focus of any organization functioning in today's competitive marketplace is to gain and sustain competitive advantage. With the huge volumes of data stored in databases, data marts and data warehouses coupled with advanced data analysis tools, managers are now in a better position to make smart and effective decisions which result in competitive advantage for their organizations. Business Analytics (BA) is a new and upcoming area of advanced data analysis that has emerged as a significant area of study for both researchers as well as practitioners over the last two decades. BA is the process of transforming huge volumes of data into new knowledge through analysis and using that knowledge for effective decision making and problem solving which ultimately results in value-creating competitive actions. Keeping in view the importance of Business Analytics, this paper discusses the concept of Business Analytics, its framework and application.
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2019, European Journal of Operational Research
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The competitive corporate environment is forcing entities to adopt new methods of work control and mechanization that ensure effectiveness. These methods of business mechanization through computer applications are known today as business analytics. Analytics portfolios help organizations to better identify and meet customers' needs while mitigating risks of managing business for better. These techniques lower the cost incurred on producing products/delivering services as well as maximize output. This study elaborates on techniques being used, their advantages and gaps therein in order to make businesses even better. The information collected is processed through Business Analytics (BA) tools to better depict results. This analysis helps in managing and anticipating the results of different problems including business outcomes. BA tools provide a better way to manage the whole business which leads to higher outcomes in terms of business growth. Purpose of the study is firstly to thoroughly understand the very concept of Business Analytics (BA) and then propose a theory based on my research and understanding of the term as to how Business Analytics (BA) lead to greater competitive advantage of the organizations.
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Awareness to the relevance of Business Analytics in identification of product attributes is considered significant by the market, relevance of Business Analytics in assessing factors leading to customer satisfaction, relevance of Business Analytics in Market segmentation, Market performance, relevance of Business Analytics in Finance applications and in Human Resources applications. Also a seventh construct was considered capturing the keenness of managers to incorporate Business Analytics in their company operations. Using the last construct as grouping variable, Analysis of Variance, ANOVA, was applied to assess which of the first six constructs significantly led to managers’ keenness to incorporate Business Analytics in their company operations. The empirical study concluded that out of the six awareness constructs, awareness was already significant for Identification of product attributes, market segmentation issues and customer satisfaction. However, for the other three constructs, awareness was not yet significant. From the ANOVA one may conclude that awareness to: (a) Market segmentation, (b) market performance and (c) HR applications would highly significantly (2% level of significance) lead managers to incorporate Business Analytics in their company operations. The three other awareness constructs, product attributes, finance and H.R. applications do not yet lead to the keenness of managers to incorporate Business Analytics in their company operations at such high degree of confidence.
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Management:Journal of Sustainable Business and Management Solutions in Emerging Economies
Research question: In the last decade, the concept of Business analytics (BA) has gone through revolutionary changes. It has gained a lot in popularity and attracted immense interest both in academic and commercial communities. Accepting this reality, the main research topic of the paper is to investigate and present the global tendencies regarding the development of business analytics concept. Motivation: We have been motivated by huge changes in ICT environment and have studied how they reflect on the domain of Business analytics. Idea: The main idea was to elaborate and make distinction between crucial characteristics of the classical and new emerging concepts of Business analytics. In addition to technical aspects, the broader business context of all presented concepts and phenomena are discussed. Findings: Dealing with essential elements of the BA concept, this paper points out that the history of Business analytics has been strongly influenced by the changes and innovations in.
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Business analytics has proven itself as a high business performance tool that can be applied to multiple divisions of an enterprise and creates a fact based culture across the organisation.The use of Quantitative analysis, predictive models and statistical tools helps the business managers in gaining a different view of the data, and deriving maximum value out of it, which in turn contributes towards improved decision making and problem solving in an organisation. However, it has been observed that despite the several benefits that are anticipated with Business Analytics adoption, very few have achieved proficiency in its implementations and organizations are facing adoption barriers, most of them being management and culture related rather than being data and technology related. The present study was undertaken to bridge this gap between the highly proclaimed business benefits of Analytics and the problems faced by organizations in successfully implementing this tool.It presents a detailed analysis of success stories of 7 companies from different sectors viz. FMCG, banking, healthcare, transportation and online selling, who have capitalized on this tool. Case studies were developed and critically analysed on the basis of their objectives of implementing Analytics, tools that were adopted, results achieved and success factors leading to their enhanced performance. The results lead to identification of factors that contribute towards successful implementation of Business Analytics for enhancing the performance of companies from various industries and also companies with varied capital structures. This research also helps in critically analysing various factors that are pulling a company away from implementing the analytical models. Considering the various dimensions of successfully implementing analytics in organizations, as identified
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2014, Decision Support Systems
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2022, International Journal for Research in Applied Science & Engineering Technology (IJRASET)
Business analytics is primarily about getting the most out of data. Data has lately been dubbed "the new oil" rather than the "sludge of the information era." While data can be used to develop new products and services, identify market niches, and spot new opportunities, it is also notoriously amorphous and difficult to extract value from. It involves different steps to get the insights from the data present majorly involving approaches like Aligning strategy, desired behaviors, and business performance management with analytical activities and capabilities is necessary to derive value from data. This article uses both conventional and qualitative research methods to examine the expanding body of work on business analytics (BA).In this paper, an attempt is being made to review several viewpoints on how business analytics is defined and how it relates to business intelligence. Additionally, we highlight business education and demonstrate how business analytics are applied in both company and industrial sectors. I. INTRODUCTION Big data is a fast-growing discipline being used to define and analyze huge quantities of data present in various forms organized, semistructured, and non-structured data from multiple enormous and complex sources. Data is rapidly growing in every sector, making it a quickly growing to enforce used to define and analyze massive volumes of data present in various forms of organized, semistructured, and non-structured data from various immense and complicated sources. The method also necessitates the use of sophisticated data-processing technologies and advanced analytic programs. Big data has changed the way businesses and organizations operate. Companies of all sizes and industries may benefit from big data applications. According to corporate groups, such benefits might boost productivity, revenue, and growth.1 Many organizations are implementing big data tools and complicated statistical applications to improve quality in areas such as operations, customer happiness or satisfaction from the deployed process, and loyalty, as well as to strengthen overall standards of corporate governance and combat malicious activities such as fraud, cyber attacks, embezzlement, and other financial crimes, which have recently increased. Apart from that, big data has a variety of business applications. High-quality essays on both theoretical and practical knowledge of big data in business will be included in this special issue.2 Statistical procedures (analysis of variance (ANOVA, tables and charts, and so on), data software applications (data mining, sorting routines), and market methodological approaches are all used to explore, visualize, identify, and further communicate the patterns or trends existing in data (linear programming). Broadly said, analytics is the conversion of data into useful knowledge. Analytics is an older term that today refers to a wide number of disciplines, not only business. A notable example of how analytics can be employed is the collecting and translation of meteorological data into statistics, which are then used to anticipate weather patterns.3 Business analytics is described as the art of discovering insight via the use of complex mathematical, analytical, machine learning, and network science methodologies, as well as a variety of data and expert knowledge. It aids in speedy decision-making. Business analytics may be considered a tool for resolving problems and making decisions. Indeed, business analytics is a subgroup of analytics that employs the utilization of tools, techniques, and other statistical ideas to solve more complicated business problems. Analytics is often used by businesses to explain, predict, and improve their performance. As data grows, it contains insights that, when used properly, may result in productive outputs and provide value for the firm. Because of its growing popularity as a term, analytics is being used to replace a variety of previously popular ideas such as intelligence, mining, and discovery. For example, business intelligence is now known as business analytics, whereas customer intelligence is now known as customer analytics, Web mining is now known as Web analytics, and knowledge discovery is now known as data analytics. Because of the number, diversity, and speed with which data is generated-i.e., big data-modern analytics may necessitate a significant amount of computation, as well as the tools, methodologies, and algorithms used in analytics projects. The special issue on big data in company or organization accepted five accomplishments in the areas of business innovativeness in the big data era, non-structured big data analytical methods and techniques in firms, advanced analytical approach for business analytics in big data, geospatial deep insight for retail proposition using similarity metric, and big data as well as modifications through interactive data visualisation.4
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