Showing papers on "Business analytics published in 2013"

Business Intelligence: Data Mining and Optimization for Decision Making

Abstract: Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.

RapidMiner: Data Mining Use Cases and Business Analytics Applications

Abstract: Powerful, Flexible Tools for a Data-Driven WorldAs the data deluge continues in todays world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making and forecasting, and ultimately the solution of increasingly complex problems. Learn from the Creators of the RapidMiner Software Written by leaders in the data mining community, including the developers of the RapidMiner software, RapidMiner: Data Mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The software and their extensions can be freely downloaded at www.RapidMiner.com. Understand Each Stage of the Data Mining ProcessThe book and software tools cover all relevant steps of the data mining process, from data loading, transformation, integration, aggregation, and visualization to automated feature selection, automated parameter and process optimization, and integration with other tools, such as R packages or your IT infrastructure via web services. The book and software also extensively discuss the analysis of unstructured data, including text and image mining. Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve similar problems.

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Abstract: Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. Youll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your companys data science projects. Youll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organizationand how you can use it for competitive advantage Treat data as a business asset that requires careful investment if youre to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

Data, information and analytics as services

Abstract: While organizations are trying to become more agile to better respond to market changes in the midst of rapidly globalizing competition by adopting service orientation-commoditization of business processes, architectures, software, infrastructures and platforms-they are also facing new challenges. In this article, we provide a conceptual framework for service oriented managerial decision making process, and briefly explain the potential impact of service oriented architecture (SOA) and cloud computing on data, information and analytics. Today, SOA, cloud computing, Web 2.0 and Web 3.0 are converging, and transforming the information technology ecosystem for the better while imposing new complexities. With this convergence, a large amount of structured and unstructured data is being created and shared over disparate networks and virtual communities. To cope and/or to take advantage of these changes, we are in need of finding new and more efficient ways to collect, store, transform, share, utilize and dispose data, information and analytics.

An overview of learning analytics

Abstract: Learning analytics, the analysis and representation of data about learners in order to improve learning, is a new lens through which teachers can understand education. It is rooted in the dramatic increase in the quantity of data about learners and linked to management approaches that focus on quantitative metrics, which are sometimes antithetical to an educational sense of teaching. However, learning analytics offers new routes for teachers to understand their students and, hence, to make effective use of their limited resources. This paper explores these issues and describes a series of examples of learning analytics to illustrate the potential. It argues that teachers can and should engage with learning analytics as a way of influencing the metrics agenda towards richer conceptions of learning and to improve their teaching.

Using learning analytics to predict (and improve) student success: a faculty perspective

Abstract: Learning analytics is receiving increased attention, in part because it offers to assist educational institutions in increasing student retention, improving student success, and easing the burden of accountability. Although these large-scale issues are worthy of consideration, faculty might also be interested in how they can use learning analytics in their own courses to help their students succeed. In this paper, we define learning analytics, how it has been used in educational institutions, what learning analytics tools are available, and how faculty can make use of data in their courses to monitor and predict student performance. Finally, we discuss several issues and concerns with the use of learning analytics in higher education.

A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges

Abstract: Visual analytics employs interactive visualizations to integrate users’ knowledge and inference capability into numerical/algorithmic data analysis processes. It is an active research field that has applications in many sectors, such as security, finance, and business. The growing popularity of visual analytics in recent years creates the need for a broad survey that reviews and assesses the recent developments in the field. This report reviews and classifies recent work into a set of application categories including space and time, multivariate, text, graph and network, and other applications. More importantly, this report presents analytics space, inspired by design space, which relates each application category to the key steps in visual analytics, including visual mapping, model-based analysis, and user interactions. We explore and discuss the analytics space to add the current understanding and better understand research trends in the field.

Business Intelligence and Analytics: Research Directions

Abstract: Business intelligence and analytics (BIA) is about the development of technologies, systems, practices, and applications to analyze critical business data so as to gain new insights about business and markets. The new insights can be used for improving products and services, achieving better operational efficiency, and fostering customer relationships. In this article, we will categorize BIA research activities into three broad research directions: (a) big data analytics, (b) text analytics, and (c) network analytics. The article aims to review the state-of-the-art techniques and models and to summarize their use in BIA applications. For each research direction, we will also determine a few important questions to be addressed in future research.

Leveraging Big Data Analytics to Reduce Healthcare Costs

Abstract: The healthcare sector deals with large volumes of electronic data related to patient services. This article describes two novel applications that leverage big data to detect fraud, abuse, waste, and errors in health insurance claims, thus reducing recurrent losses and facilitating enhanced patient care. The results indicate that claim anomalies detected using these applications help private health insurance funds recover hidden cost overruns that aren't detectable using transaction processing systems. This article is part of a special issue on leveraging big data and business analytics.

Data science for business

Abstract: Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. You'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization - and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you're to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

Click Here for a Data Scientist: Big Data, Predictive Analytics, and Theory Development in the Era of a Maker Movement Supply Chain

Abstract: Predictive analytics is impacting many diverse areas, ranging from baseball and epidemiology to forecasting and customer relationship management. Manufacturers, retailers, software companies, and consultants are creatively discovering new applications of big data using predictive analytics in supply chain management and logistics. In practice, predictive analytics is generally atheoretical, however, we develop a 2 X 2 model to explain the role of predictive analytics in the theory development process. This 2 X 2 model shows that in our discipline we have traditionally taken one path to theory development but that predictive analytics can be a salient component of a comprehensive theory development process. The model points to a number of research questions that need to be addressed by our research community. These questions are not just highly relevant to the academic community but also in urgent need of answers to help practitioners execute the right strategies with greater precision and efficiency. We also discuss how one disruptive trend, the maker movement (MM), changes the nature of who the producers are in the supply chain, making big data even more valuable. As we engage in higher levels of dialogue we will be able to make meaningful progress addressing these vital research topics.

Big Data and Transformational Government

Abstract: The big data phenomenon is growing throughout private and public sector domains. Profit motives make it urgent for companies in the private sector to learn how to leverage big data. However, in the public sector, government services could also be greatly improved through the use of big data. Here, the authors describe some drivers, barriers, and best practices affecting the use of big data and associated analytics in the government domain. They present a model that illustrates how big data can result in transformational government through increased efficiency and effectiveness in the delivery of services. Their empirical basis for this model uses a case vignette from the US Department of Veterans Affairs, while the theoretical basis is a balanced view of big data that takes into account the continuous growth and use of such data. This article is part of a special issue on big data and business analytics.

Business analytics for supply chain: a dynamic-capabilities framework

Abstract: Supply chain management has become more important as an academic topic due to trends in globalization leading to massive reallocation of production related advantages. Because of the massive amount of data that is generated in the global economy, new tools need to be developed in order to manage and analyze the data, as well as to monitor organizational performance worldwide. This paper proposes a framework of business analytics for supply chain analytics (SCA) as IT-enabled, analytical dynamic capabilities composed of data management capability, analytical supply chain process capability, and supply chain performance management capability. This paper also presents a dynamic-capabilities view of SCA and extensively describes a set of its three capabilities: data management capability, analytical supply chain process capability, and supply chain performance management capability. Next, using the SCM best practice, sales & operations planning (S&OP), the paper demonstrates opportunities to apply SCA in an integrated way. In discussing the implications of the proposed framework, finally, the paper examines several propositions predicting the positive impact of SCA and its individual capability on SCM performance.

Data Analytics: Hyped Up Aspirations or True Potential?

Abstract: 1 A nalytics is usually defined, in practice as any fact-based deliberation which leads to insights (diagnostics) and possible implications for planning future action in an organizational set up. It could range from routine tracking and monitoring of business performance and “nice-to-know” validation facts regarding the business domain, to more directed diagnosis of “root cause” of business problems as well as strategic prediction about future business initiatives. The commonality across all these exercises is that it is driven significantly by facts (“rational” by nature) obtained as a part of business and market data collection initiatives by firms.

Enhancing the Business Value of Business Intelligence: The Role of Shared Knowledge and Assimilation

Abstract: Business intelligence (BI) systems have attracted significant interest from senior executives and consultants for their ability to exploit organizational data and provide operational and strategic benefits through improved management control systems. A large body of literature indicates that organizations have largely failed to use their business intelligence investments effectively to exploit the wealth of data they capture in their ERP systems. As a result, BI has too often failed to support organizations' managerial decision making at both the strategic and operational levels and, thus, failed to enhance business value. Whether and how organizations achieve business benefits from their BI investments remains unclear. This study draws on the strategic alignment and IT assimilation literature to develop a research model that theorizes the importance of BI systems assimilation, and the need for shared knowledge among the strategic and operational levels as the drivers of BI business value. Resul...

Business Process Analytics Using a Big Data Approach

Abstract: Continuous improvement of business processes is a challenging task that requires complex and robust supporting systems. Using advanced analytics methods and emerging technologies--such as business intelligence systems, business activity monitoring, predictive analytics, behavioral pattern recognition, and "type simulations"--can help business users continuously improve their processes. However, the high volumes of event data produced by the execution of processes during the business lifetime prevent business users from efficiently accessing timely analytics data. This article presents a technological solution using a big data approach to provide business analysts with visibility on distributed process and business performance. The proposed architecture lets users analyze business performance in highly distributed environments with a short time response. This article is part of a special issue on leveraging big data and business analytics.

Broadening the scope and increasing the usefulness of learning analytics: The case for assessment analytics

Abstract: The author offers opinions learning and learning assessment analytics, which are defined as the measurement and analysis of data about learners for the purpose of understanding learning. It is argued that learning analytics have a limited usefulness in higher education because it is based on data generated by student interaction in online learning and social media environments, educational technology whose use remains far from universal. It is argued that the use of data analytics generated from educational assessment will be of more value in higher education, as that is a process in which all teachers and students are engaged.

An Overview of Health Analytics

Abstract: Objectives: We examine the emerging health analytics field by describing the different health analytics and providing examples of various applications. Methods: The paper discusses different definitions of health analytics, describes the four stages of health analytics, its architectural framework, development methodology, and examples in public health. Results: The paper provides a broad overview of health analytics for researchers and practitioners. Conclusions: Health analytics is rapidly emerging as a key and distinct application of health information technology. The key objective of health analytics is to gain insight for making informed healthcare decisions.

Supporting action research with learning analytics

Abstract: Learning analytics tools should be useful, i.e., they should be usable and provide the functionality for reaching the goals attributed to learning analytics. This paper seeks to unite learning analytics and action research. Based on this, we investigate how the multitude of questions that arise during technology-enhanced teaching and learning systematically can be mapped to sets of indicators. We examine, which questions are not yet supported and propose concepts of indicators that have a high potential of positively influencing teachers' didactical considerations. Our investigation shows that many questions of teachers cannot be answered with currently available research tools. Furthermore, few learning analytics studies report about measuring impact. We describe which effects learning analytics should have on teaching and discuss how this could be evaluated.

Business Intelligence: Practices, Technologies, and Management

Abstract: Preface. Acknowledgments. About the Authors. Part I Introduction to Business Intelligence. Chapter 1 Business Intelligence and Its Impacts. Chapter 2 Business Intelligence Capabilities. Part II Technologies Enabling Business Intelligence. Chapter 3 Technologies Enabling Organizational Memory. Chapter 4 Technologies Enabling Information Integration. Chapter 5 Technologies Enabling Insights and Decisions. Chapter 6 Technologies Enabling Presentation. Part III Management and Future of BusinessIntelligence. Chapter 7 Business Intelligence Tools and Vendors. Chapter 8 Development of Business Intelligence. Chapter 9 Management of Business Intelligence. Chapter 10 The Future of Business Intelligence.

Policy Analytics: An Agenda for Research and Practice

Abstract: In recent years the field of decision analysis has been heavily influenced by the "analytics'' perspective, which integrates advanced data-mining and learning methods, often associated with increasing access to "Big-Data", with decision support systems. This rapidly growing and very successful field of Analytics has been strongly business-oriented since its origin and is typically focussed on data-driven decision processes. In public decisions, however, issues such as individual and social values, culture and public engagement play a much bigger role and, to a large extent, characterise the policy cycle of design, testing, implementation, evaluation and review. From this perspective public policy making seems to be a much more socially complex process than has hitherto been considered by most analytics methods and applications. In this paper we thus suggest a framework for the use of analytics in supporting the policy cycle - and conceptualise it as "Business Analytics".

Policy analytics: an agenda for research and practice

Abstract: The growing impact of the “analytics” perspective in recent years, which integrates advanced data-mining and learning methods, is often associated with increasing access to large databases and with decision support systems. Since its origin, the field of analytics has been strongly business-oriented, with a typical focus on data-driven decision processes. In public decisions, however, issues such as individual and social values, culture and public engagement are more important and, to a large extent, characterise the policy cycle of design, testing, implementation, evaluation and review of public policies. Therefore public policy making seems to be a much more socially complex process than has hitherto been considered by most analytics methods and applications. In this paper, we thus suggest a framework for the use of analytics in supporting the policy cycle—and conceptualise it as “Policy Analytics”.

Data Mining and Business Analytics with R

Abstract: Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.

Leveraging Big Data and Business Analytics [Guest editors' introduction]

Abstract: Big data projects and programs provide tremendous opportunity for organizations looking to transform their operations, innovate in their markets, and better serve their customers; however, these initiatives must be based on sound approaches and principles and not fads or empty vendor claims. This special issue aims to promote a better understanding of big data to foster wider deployment of big data approaches and a new era of business analytics capabilities.