Showing papers on "Business analytics published in 2015"

Reflections on societal and business model transformation arising from digitization and big data analytics

Abstract: In the era of accelerating digitization and advanced big data analytics, harnessing quality data for designing and delivering state-of-the-art services will enable innovative business models and management approaches (Boyd and Crawford, 2012; Brynjolfsson and McAfee, 2014) and yield an array of consequences. Among other consequences, digitization and big data analytics reshape business models and impact employment amongst knowledge workers - just as automation did for manufacturing workers. This Viewpoint paper considers the mechanisms underlying how digitization and big data analytics drive the transformation of business and society and outlines the potential effects of digitization and big data analytics on employment - especially in the context of cognitive tasks. Its aim is to outline a critical research agenda to explore and conceptualize evident changes in business models and society arising from these technological advances.

Big data analytics: a survey

Abstract: The age of big data is now coming. But the traditional data analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to efficiently analyze big data and how to design an appropriate mining algorithm to find the useful things from big data. To deeply discuss this issue, this paper begins with a brief introduction to data analytics, followed by the discussions of big data analytics. Some important open issues and further research directions will also be presented for the next step of big data analytics.

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Abstract: Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.

Data science, predictive analytics, and big data in supply chain management: Current state and future potential

Abstract: While data science, predictive analytics, and big data have been frequently used buzzwords, rigorous academic investigations into these areas are just emerging. In this forward thinking article, we discuss the results of a recent large-scale survey on these topics among supply chain management (SCM) professionals, complemented with our experiences in developing, implementing, and administering one of the first master's degree programs in predictive analytics. As such, we effectively provide an assessment of the current state of the field via a large-scale survey, and offer insight into its future potential via the discussion of how a research university is training next-generation data scientists. Specifically, we report on the current use of predictive analytics in SCM and the underlying motivations, as well as perceived benefits and barriers. In addition, we highlight skills desired for successful data scientists, and provide illustrations of how predictive analytics can be implemented in the curriculum. Relying on one of the largest data sets of predictive analytics users in SCM collected to date and our experiences with one of the first master's degree programs in predictive analytics, it is our intent to provide a timely assessment of the field, illustrate its future potential, and motivate additional research and pedagogical advancements in this domain.

Big Data Analytics in Financial Statement Audits

Abstract: SYNOPSIS: Big Data analytics is the process of inspecting, cleaning, transforming, and modeling Big Data to discover and communicate useful information and patterns, suggest conclusions, and support decision making. Big Data has been used for advanced analytics in many domains but hardly, if at all, by auditors. This article hypothesizes that Big Data analytics can improve the efficiency and effectiveness of financial statement audits. We explain how Big Data analytics applied in other domains might be applied in auditing. We also discuss the characteristics of Big Data analytics, which set it apart from traditional auditing, and its implications for practical implementation.

Introduction to Statistical Analysis

Abstract: This and subsequent chapters will delve into the details of business analytics techniques. It has already been established in the previous chapter that statistics forms a major portion of this art. This chapter will begin with the basic definition of statistics. It will also refer to a few web sites to access data sets, which you can use for the examples. By the end of this chapter, you will be able to comprehend the following concepts that are essential for proceeding with business analytics techniques:

Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance

Abstract: Introduction: Welcome to a SmarterWorld 1 1 Smarter Business 9 2 S = STARTWITH STRATEGY 23 3 M = MEASURE METRICS AND DATA 57 4 A = APPLY ANALYTICS 105 5 R = REPORT RESULTS 155 6 T = TRANSFORM BUSINESS 199 Conclusion 231 About the Author 236 Acknowledgements 238 Index 239

Research Directions on the Adoption, Usage, and Impact of the Internet of Things through the Use of Big Data Analytics

Abstract: The number of devices connected to the Internet of Things (IoT) by the year 2020 may be as high as 75 billion. Long before that, big data analytics will be needed to make use of the data generated by the Internet of Things. While the technical issues needed to create the Internet of Things are substantial, little attention has been given to the behavioral, organizational and business issues that are necessary for a better understanding of the adoption, usage and impact of the IoT. We propose a framework based on the idea that this technology will evolve from monitored "Things", to "Networks of Things", and ultimately to an "Internet of Things". Each of these instantiations of the technology raises adoption, usage and impact issues that can be scrutinized at four levels of analysis: individual, organization, industry, and society. We apply the framework to propose research questions that need to be addressed by researchers.

Fast Fashion: Business Model Overview and Research Opportunities

Abstract: Fast fashion is a business model that offers (the perception of) fashionable clothes at affordable prices. From an operations standpoint, fast fashion requires a highly responsive supply chain that can support a product assortment that is periodically changing. Though the underlying principles are simple, the successful execution of the fast-fashion business model poses numerous challenges. We present a careful examination of this business model and discuss its execution by analyzing the most prominent firms in the industry. We then survey the academic literature for research that is specifically relevant or directly related to fast fashion. Our goal is to expose the main components of fast fashion and to identify untapped research opportunities.

Big data analytics and business analytics

Abstract: Over the past few decades, with the development of automatic identification, data capture and storage technologies, people generate data much faster and collect data much bigger than ever before in...

Linking Business Analytics to Decision Making Effectiveness: A Path Model Analysis

Abstract: While business analytics is being increasingly used to gain data-driven insights to support decision making, little research exists regarding the mechanism through which business analytics can be used to improve decision-making effectiveness (DME) at the organizational level. Drawing on the information processing view and contingency theory, this paper develops a research model linking business analytics to organizational DME. The research model is tested using structural equation modeling based on 740 responses collected from U.K. businesses. The key findings demonstrate that business analytics, through the mediation of a data-driven environment, positively influences information processing capability, which in turn has a positive effect on DME. The findings also demonstrate that the paths from business analytics to DME have no statistical differences between large and medium companies, but some differences between manufacturing and professional service industries. Our findings contribute to the business analytics literature by providing useful insights into business analytics applications and the facilitation of data-driven decision making. They also contribute to managers’ knowledge and understanding by demonstrating how business analytics should be implemented to improve DME.

Business Analytics in the Context of Big Data: A Roadmap for Research

Abstract: This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice.

Infer, Predict, and Assure: Accounting Opportunities in Data Analytics

Abstract: SYNOPSIS: The business use of data analytics is growing rapidly in the accounting environment. Similar to many new systems that involve accounting information, data analytics has fundamentally changed task processes, particularly those tasks that provide inference, prediction, and assurance to decision-makers. Thus, accounting researchers and practitioners must consider data analytics and its impact on accounting practice in their work. This paper uses the organizing principles from Mauldin and Ruchala's (1999) meta-theory of accounting information systems (AIS) to identify current data analytics use, examine how data analytics impacts the accounting environment, and discuss challenges and research opportunities.

Commentaries on Big Data's Importance for Accounting and Auditing

Abstract: The commentaries in this forum on Big Data confront one of our profession’s most pressing challenges. How should we respond to Big Data? Big Data and business analytics now permeate almost all aspects of major companies’ decision making and business strategies. A large U.S. company, for example, might process a billion data elements every day to understand its competitive environment. Moreover, by its very nature, Big Data cannot avoid running head-on into the traditional systems of accounting and auditing that have served our profession so well in the past. What are the threats and opportunities for accounting and auditing generated by this fundamentally different way of understanding information and reporting by business organizations? This issue presents eight commentaries by expert academics and business professionals, who have studied and thought much about the core issues and challenges. Collectively, these commentaries not only define and frame the important issues for accounting and auditing but, also, they identify many feasible, yet difficult, pathways forward, wherein Big Data and traditional accounting and auditing might meld to better serve firms, stakeholders, and the public. These commentaries also

Minding the analytics gap

Abstract: Senior executives increasingly recognize the importance of analytics to creating business value. XL Group plc, a global insurance and reinsurance company based in Dublin, Ireland, is a case in point. Like others in the insurance industry, XL has long relied heavily on data analysis to understand and price its products. XL produces increasingly complex analytics, and demand for analytical insights progressively permeates the organization. To understand the challenges and opportunities associated with the use of business analytics, MIT Sloan Management Review, in partnership with SAS Institute Inc., conducted its third annual survey of 2,719 business executives, managers and analytics professionals from organizations located around the world. The survey and interviews reveal a sizable gap between the production and consumption of analytics. Furthermore, this gap persists and may even grow as organizations mature analytically. Producers of analytics will likely continue to improve their ability to make more sophisticated analytical results, so managers need to find ways to become comfortable making decisions based on analytical results that they do not fully understand

Big data analytics: a literature review

Abstract: With more and more data generated, it has become a big challenge for traditional architectures and infrastructures to process large amounts of data within an acceptable time and resources. In order...

Business Analytics Curriculum for Undergraduate Majors

Abstract: Analytics has become a new source of competitive advantage for many corporations. Today's work force therefore must be cognizant of its power and value to effectively perform their jobs. In this paper, we define the appropriate skill level and breadth of knowledge required for business school graduates to be successful. An undergraduate course of study in analytics is proposed for students with average to above-average analytical skills. Implementation guidelines are also addressed to ensure a successful program.

A business analytics capability framework

Abstract: Business analytics (BA) capabilities can potentially provide value and lead to better organisational performance. This paper develops a holistic, theoretically-grounded and practically relevant business analytics capability framework (BACF) that specifies, defines and ranks the capabilities that constitute an organisational BA initiative. The BACF was developed in two phases. First, an a priori conceptual framework was developed based on the Resource-Based View theory of the firm and a thematic content analysis of the BA literature. Second, the conceptual framework was further developed and refined using a three round Delphi study involving 16 BA experts. Changes from the Delphi study resulted in a refined and confirmed framework including detailed capability definitions, together with a ranking of the capabilities based on importance. The BACF will help academic researchers and industry practitioners to better understand the capabilities that constitute an organisational BA initiative and their relative importance. In future work, the capabilities in the BACF will be operationalised to measure their as-is status, thus enabling organisations to identify key areas of strength and weakness and prioritise future capability improvement efforts.

Business Models and the Internet of Things (Extended Abstract)

Abstract: In this extended abstract we aim on providing an overview on business models based on the Internet of Things for assisting companies that are currently focused on non-digital industries. In the first section the role of the Internet as an innovation driver for business models is reflected, secondly it is shown how business model patterns from digital industries are becoming relevant to physical industries as well. General business model logic patterns for the Internet of Things are shown and the challenges of implementing such patterns in hybrid business models are addressed.

Business Intelligence and Analytics

Abstract: A business intelligence system is a data-driven decision support system Managing data is especially important for business intelligence and analytics Data warehouses, marts or data-driven decision support systems are intended to help managers transform data into information and knowledge Routinely data is moved from source systems to a decision support data store Some comparison reports include external data on competitors or other relevant data Analytics refers to quantitative analysis of data There are three components of business analytics: i) descriptive analytics, ii) predictive analytics and iii) prescriptive analytics Besides statistical analysis techniques, data mining is the key component of predictive analytics Data mining is a process of analyzing large amounts of data to identify data content relationships Cloud computing and “Big” data are changing business intelligence and analytics Columnar data bases let analysts work with data from web logs and other nonrelational data sources Keywords: analytics; business intelligence; decision support; decision sciene

A perspective on applications of in-memory analytics in supply chain management

Abstract: Big data, advanced analytics, and in-memory database technology are on the agenda of top management since they are seen as key enablers for enhanced business decision-making. In this paper, we provide a comprehensive perspective on applications of in-memory analytics in the field of supply chain management (SCM) that use the aforementioned concepts. Our contribution is threefold: First, we develop a top-down framework to position in-memory analytics applications against extant IT systems in SCM. Second, we conduct a bottom-up categorization of 41 in-memory analytics applications in SCM to provide supporting empirical evidence of the efficacy of the framework. Third, by contrasting top-down and bottom-up perspectives we derive implications for research and industrial practice. In-memory analytics applications in SCM can be structured along four use cases.Real-time analytics is the predominant focus of emerging in-memory applications.Integrated data models further support functional integration in adjacent domains.Emerging applications do not substitute but complement current APS systems.A stochastic planning approach in APS systems still remains open for research.

Education and training for successful career in Big Data and Business Analytics

Abstract: Purpose – The purpose of this paper is to identify Big Data and Business Analytics (BDBA) skills and further propose an education and training framework for a successful career in BDBA. Design/methodology/approach – The present study adopts a review of extant literature and appreciative enquiry (AI) which is a quasi-ethnographic approach to identify the skills required for BDBA. Findings – The study helps to identify skills for BDBA and based on extant literature and AI, proposes a theoretical framework for education and training for a successful career in BDBA. Further research directions are outlined which can help take the present research to the next level. Research limitations/implications – The paper presents a theoretical framework, but it has to be validated through empirical data. This research will generate a lot of interest to develop a more practical framework and conduct empirical and case study research. Practical implications – The present study has outlined skills for BDBA. The authors have also proposed a theoretical framework which can further help an educational or training institute to embrace the framework to train young undergraduates or graduates to acquire BDBA skills. It may also motivate an institution to structure their curriculum for a BDBA program. Social implications – This research is a timely one to develop necessary skills for being successful in BDBA career and in turn contribute to the well-being of business community and society. Originality/value – This research is a novel one as there is no research done earlier on this new and emerging areas of research, namely, education and training for BDBA.

The talent dividend

Abstract: The 2015 Data & Analytics Report by MIT Sloan Management Review and SAS finds that talent management is critical to realizing analytics benefits. This fifth annual survey of business executives, managers and analytics professionals from organizations located around the world captured insights from 2,719 respondents. It finds that organizations achieving the greatest benefits from analytics are also much more likely to have a plan for building their talent bench. That talent plan includes (1) giving preference to people with analytical skills when hiring and promoting, (2) developing analytical skills through formal training, and (3) integrating new talent with more traditional data workers

Understanding the Impact of Business Analytics on Innovation.

Abstract: Advances in Business Analytics in the era of Big Data have provided unprecedented opportunities for organizations to innovate. With insights gained from Business Analytics, companies are able to develop new or improved products/services. However, few studies have investigated the mechanism through which Business Analytics contributes to a firm's innovation success. This research aims to address this gap by theoretically and empirically investigating the relationship between Business Analytics and innovation. To achieve this aim, absorptive capacity theory is used as a theoretical lens to inform the development of a research model. Absorptive capacity theory refers to a firm's ability to recognize the value of new, external information, assimilate it and apply it to commercial ends. The research model covers the use of Business Analytics, environmental scanning, data-driven culture, innovation (new product newness and meaningfulness), and competitive advantage. The research model is tested through a questionnaire survey of 218 UK businesses. The results suggest that Business Analytics directly improves environmental scanning which in turn helps to enhance a company's innovation. Business Analytics also directly enhances data-driven culture that in turn impacts on environmental scanning. Data-driven culture plays another important role by moderating the effect of environmental scanning on new product meaningfulness. The findings demonstrate the positive impact of business analytics on innovation and the pivotal roles of environmental scanning and data-driven culture. Organizations wishing to realize the potential of Business Analytics thus need changes in both their external and internal focus.

Business intelligence for cross-process knowledge extraction at tourism destinations

Abstract: Decision-relevant data stemming from various business processes within tourism destinations (e.g. booking or customer feedback) are usually extensively available in electronic form. However, these data are not typically utilized for product optimization and decision support by tourism managers. Although methods of business intelligence and knowledge extraction are employed in many travel and tourism domains, current applications usually deal with different business processes separately, which lacks a cross-process analysis approach. This study proposes a novel approach for business intelligence-based cross-process knowledge extraction and decision support for tourism destinations. The approach consists of (a) a homogeneous and comprehensive data model that serves as the basis of a central data warehouse, (b) mechanisms for extracting data from heterogeneous sources and integrating these data into the homogeneous data structures of the data warehouse, and (c) analysis methods for identifying important relationships and patterns across different business processes, thereby bringing to light new knowledge. A prototype of the proposed concepts was implemented for the leading Swedish mountain destination Are, which demonstrates the effectiveness of the proposed business intelligence architecture and the gained business benefits for a tourism destination.