Showing papers on "Business analytics published in 2012"

Business intelligence and analytics: from big data to big impact

Abstract: Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.

Learning analytics: drivers, developments and challenges

Abstract: Learning analytics is a significant area of technology-enhanced learning that has emerged during the last decade. This review of the field begins with an examination of the technological, educational and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, the development of data-driven analytics, the rise of learning-focused perspectives and the influence of national economic concerns. It next focuses on the relationships between learning analytics, educational data mining and academic analytics. Finally, it examines developing areas of learning analytics research, and identifies a series of future challenges.

Learning analytics and educational data mining: towards communication and collaboration

Abstract: Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. Two research communities -- Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.

Translating Learning into Numbers: A Generic Framework for Learning Analytics

Abstract: Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15(3), 42–57.

A reference model for learning analytics

Abstract: Recently, there is an increasing interest in learning analytics in Technology-Enhanced Learning TEL. Generally, learning analytics deals with the development of methods that harness educational datasets to support the learning process. Learning analytics LA is a multi-disciplinary field involving machine learning, artificial intelligence, information retrieval, statistics and visualisation. LA is also a field in which several related areas of research in TEL converge. These include academic analytics, action analytics and educational data mining. In this paper, we investigate the connections between LA and these related fields. We describe a reference model for LA based on four dimensions, namely data and environments what?, stakeholders who?, objectives why? and methods how?. We then review recent publications on LA and its related fields and map them to the four dimensions of the reference model. Furthermore, we identify various challenges and research opportunities in the area of LA in relation to each dimension.

The learning analytics cycle: closing the loop effectively

Abstract: This paper develops Campbell and Oblinger's [4] five-step model of learning analytics (Capture, Report, Predict, Act, Refine) and other theorisations of the field, and draws on broader educational theory (including Kolb and Schon) to articulate an incrementally more developed, explicit and theoretically-grounded Learning Analytics Cycle.This cycle conceptualises successful learning analytics work as four linked steps: learners (1) generating data (2) that is used to produce metrics, analytics or visualisations (3). The key step is 'closing the loop' by feeding back this product to learners through one or more interventions (4).This paper seeks to begin to place learning analytics practice on a base of established learning theory, and draws several implications from this theory for the improvement of learning analytics projects. These include speeding up or shortening the cycle so feedback happens more quickly, and widening the audience for feedback (in particular, considering learners and teachers as audiences for analytics) so that it can have a larger impact.

Learning analytics: envisioning a research discipline and a domain of practice

Abstract: Learning analytics are rapidly being implemented in different educational settings, often without the guidance of a research base. Vendors incorporate analytics practices, models, and algorithms from datamining, business intelligence, and the emerging "big data" fields. Researchers, in contrast, have built up a substantial base of techniques for analyzing discourse, social networks, sentiments, predictive models, and in semantic content (i.e., "intelligent" curriculum). In spite of the currently limited knowledge exchange and dialogue between researchers, vendors, and practitioners, existing learning analytics implementations indicate significant potential for generating novel insight into learning and vital educational practices. This paper presents an integrated and holistic vision for advancing learning analytics as a research discipline and a domain of practices. Potential areas of collaboration and overlap are presented with the intent of increasing the impact of analytics on teaching, learning, and the education system.

Social learning analytics: five approaches

Abstract: This paper proposes that Social Learning Analytics (SLA) can be usefully thought of as a subset of learning analytics approaches. SLA focuses on how learners build knowledge together in their cultural and social settings. In the context of online social learning, it takes into account both formal and informal educational environments, including networks and communities. The paper introduces the broad rationale for SLA by reviewing some of the key drivers that make social learning so important today. Five forms of SLA are identified, including those which are inherently social, and others which have social dimensions. The paper goes on to describe early work towards implementing these analytics on SocialLearn, an online learning space in use at the UK's Open University, and the challenges that this is raising. This work takes an iterative approach to analytics, encouraging learners to respond to and help to shape not only the analytics but also their associated recommendations.

Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses

Abstract: Unique prospective on the big data analytics phenomenon for both business and IT professionalsThe availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability.The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics.Learn more about the trends in big data and how they are impacting the business world (Risk, Marketing, Healthcare, Financial Services, etc.)Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insightsExplores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more.

How Does Business Analytics Contribute to Business Value

Abstract: This paper presents a model, synthesized from the literature, of factors that explain how business analytics contributes to business value. It also reports results from a preliminary assessment of that model. The model consists of two parts: a process and a variance model. The process model depicts the analyze‐insight‐decision‐action process through which use of an organization's business analytic capabilities is intended to create business value. The variance model proposes that the five factors in Davenport et al.'s DELTA model of business analytics success factors, six from Watson & Wixom and three from Seddon et al.'s model of organizational benefits from enterprise systems, assist a firm to gain business value from business analytics. A preliminary assessment of the model was conducted using data from 100 customer success stories from vendors such as IBM, SAP and Teradata. Our conclusion is that the business analytics success model is likely to be a useful basis for future research.

Business Intelligence and Analytics Education, and Program Development: A Unique Opportunity for the Information Systems Discipline

Abstract: “Big Data,” huge volumes of data in both structured and unstructured forms generated by the Internet, social media, and computerized transactions, is straining our technical capacity to manage it. More importantly, the new challenge is to develop the capability to understand and interpret the burgeoning volume of data to take advantage of the opportunities it provides in many human endeavors, ranging from science to business. Data Science, and in business schools, Business Intelligence and Analytics (BI&A) are emerging disciplines that seek to address the demands of this new era. Big Data and BI&A present unique challenges and opportunities not only for the research community, but also for Information Systems (IS) programs at business schools. In this essay, we provide a brief overview of BI&A, speculate on the role of BI&A education in business schools, present the challenges facing IS departments, and discuss the role of IS curricula and program development, in delivering BI&A education. We contend that a new vision for the IS discipline should address these challenges.

From web analytics to digital marketing optimization: Increasing the commercial value of digital analytics

Abstract: The use of web analytics to improve online marketing dates back to the 1990s when the first web analytics systems were developed. Yet, recent research suggests that many companies are failing to utilize core web analytics best practices and are therefore not getting the potential return from web analytics that they could. This paper reviews the opportunities for companies to better apply web analytics to improve digital marketing performance. An approach is defined to create a strategy to improve the value contributed by web analytics. The paper describes techniques that can be used to set up a digital marketing optimization programme, including a review of how people, process, measures and tools can be combined.

A framework for business analytics in performance management

Abstract: Purpose – Increased business competition requires even more rapid and sophisticated information and data analysis. These requirements challenge performance management to effectively support the decision making process. Business analytics is an emerging field that can potentially extend the domain of performance management to provide an improved understanding of business dynamics and lead to a better decision making. The purpose of this positional paper is to introduce performance management analytics as a potential extension of performance management research and practice. The paper clarifies the possible application areas of business analytics and their advantages within the context of performance management.Design/methodology/approach – The paper employs a literature based analysis and from this a conceptual argument is established. Finally, a business analytical model is presented to be used to undertake future research.Findings – The paper clarifies the possible application areas of business analytics...

The pulse of learning analytics understandings and expectations from the stakeholders

Abstract: While there is currently much buzz about the new field of learning analytics [19] and the potential it holds for benefiting teaching and learning, the impression one currently gets is that there is also much uncertainty and hesitation, even extending to scepticism. A clear common understanding and vision for the domain has not yet formed among the educator and research community. To investigate this situation, we distributed a stakeholder survey in September 2011 to an international audience from different sectors of education. The findings provide some further insights into the current level of understanding and expectations toward learning analytics among stakeholders. The survey was scaffolded by a conceptual framework on learning analytics that was developed based on a recent literature review. It divides the domain of learning analytics into six critical dimensions. The preliminary survey among 156 educational practitioners and researchers mostly from the higher education sector reveals substantial uncertainties in learning analytics.In this article, we first briefly introduce the learning analytics framework and its six domains that formed the backbone structure to our survey. Afterwards, we describe the method and key results of the learning analytics questionnaire and draw further conclusions for the field in research and practice. The article finishes with plans for future research on the questionnaire and the publication of both data and the questions for others to utilize.

Towards a business analytics capability maturity model

Abstract: Business analytics (BA) systems are an important strategic investment for many organisations and can potentially contribute significantly to firm performance. Establishing strong BA capabilities is currently one of the major concerns of chief information officers. This research project aims to develop a BA capability maturity model (BACMM). The BACMM will help organisations to scope and evaluate their BA initiatives. This research-in-progress paper describes the current BACMM, relates it to existing capability maturity models and explains its theoretical base. It also discusses the design science research approach being used to develop the BACMM and provides details of further work within the research project. Finally, the paper concludes with a discussion of how the BACMM might be used in practice.

Business intelligence in enterprise computing environment

Abstract: Business intelligence (BI) is the process of gathering correct information in the correct format at the correct time; and delivering the results for decision-making purposes, or have a positive impact on business operations, tactics, and strategy in the enterprises. This paper is intended as a brief review of BI in an enterprise computing environment, with an emphasis on the algorithms and methods. The review points out the challenges to the broad and deep deployment of business intelligence systems, and provide proposals to make business intelligence more effective.

Data Analytics: Models and Algorithms for Intelligent Data Analysis

Abstract: This book is a comprehensive introduction to the methods and algorithms and approaches of modern data analytics. It covers data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. The text is designed for undergraduate and graduate courses on data analytics for engineering, computer science, and math students. It is also suitable for practitioners working on data analytics projects. This book has been used for more than ten years in numerous courses at the Technical University of Munich, Germany, in short courses at several other universities, and in tutorials at scientific conferences. Much of the content is based on the results of industrial research and development projects at Siemens.

A Taxonomy of Dirty Time-Oriented Data

Abstract: Data quality is a vital topic for business analytics in order to gain accurate insight and make correct decisions in many data-intensive industries. Albeit systematic approaches to categorize, detect, and avoid data quality problems exist, the special characteristics of time-oriented data are hardly considered. However, time is an important data dimension with distinct characteristics which affords special consideration in the context of dirty data. Building upon existing taxonomies of general data quality problems, we address ‘dirty’ time-oriented data, i.e., time-oriented data with potential quality problems. In particular, we investigated empirically derived problems that emerge with different types of time-oriented data (e.g., time points, time intervals) and provide various examples of quality problems of time-oriented data. By providing categorized information related to existing taxonomies, we establish a basis for further research in the field of dirty time-oriented data, and for the formulation of essential quality checks when preprocessing time-oriented data.

Big data, big business: bridging the gap

Abstract: Business analytics, occupying the intersection of the worlds of management science, computer science and statistical science, is a potent force for innovation in both the private and public sectors. The successes of business analytics in strategy, process optimization and competitive advantage has led to data being increasingly recognized as a valuable asset in many organizations. In recent years, thanks to a dramatic increase in the volume, variety and velocity of data, the loosely defined concept of "Big Data" has emerged as a topic of discussion in its own right -- with different viewpoints in both the business and technical worlds. From our perspective, it is important for discussions of "Big Data" to start from a well-defined business goal, and remain moored to fundamental principles of both cost/benefit analysis as well as core statistical science. This note discusses some business case considerations for analytics projects involving "Big Data", and proposes key questions that businesses should ask. With practical lessons from Big Data deployments in business, we also pose a number of research challenges that may be addressed to enable the business analytics community bring best data analytic practices when confronted with massive data sets.

Business analytics in supply chains - The contingent effect of business process maturity

Abstract: The paper analyzes the effect of the use of business analytics on supply chain performance. It investigates the changing information processing needs at different supply chain process maturity levels. The effects of analytics in each Supply Chain Operations Reference areas (Plan, Source, Make and Deliver) are analyzed with various statistical techniques. A worldwide sample of 788 companies from different industries is used. The results indicate the changing impact of business analytics use on performance, meaning that companies on different maturity levels should focus on different areas. The theoretical and practical implications of these findings are thoroughly discussed.

Learning analytics as a tool for closing the assessment loop in higher education

Abstract: This paper examines learning and academic analytics and its relevance to distance education in undergraduate and graduate programs as it impacts students and teaching faculty, and also academic institutions. The focus is to explore the measurement, collection, analysis, and reporting of data as predictors of student success and drivers of departmental process and program curriculum. Learning and academic analytics in higher education is used to predict student success by examining how and what students learn and how success is supported by academic programs and institutions. The paper examines what is being done to support students, whether or not it is effective, and if not why, and what educators can do. The paper also examines how these data can be used to create new metrics and inform a continuous cycle of improvement. It presents examples of working models from a sample of institutions of higher education: The Graduate School of Medicine at the University of Wollongong, the University of Michigan, Purdue University, and the University of Maryland, Baltimore County. Finally, the paper identifies considerations and recommendations for using analytics and offer suggestions for future research. https://doi.org/10.34105/j.kmel.2012.04.020

Learning Analytics Considered Harmful.

Abstract: This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.

Business intelligence: an integrated approach

Abstract: Business intelligence systems combine operational and historical data with analytical tools to present valuable and competitive information to business planners and decision makers. The objective of Business intelligence (BI) is to improve the timeliness and quality of information, and enable managers to be able to better understand the position of their firm as in comparison to competitors. Business intelligence applications and technologies can help companies to analyze changing trends in market share; changes in customer behavior and spending patterns; customers’ preferences; company capabilities; and market conditions. Business intelligence can be used to help analysts and managers determine which adjustments are most likely to respond to changing trends. The emergence of the data warehouse as a repository, advances in data cleansing, increased capabilities of hardware and software, and the emergence of the web architecture all combine to create a richer business intelligence environment than was available previously. In this paper, an attempt has been made to present a framework for building a BI system. While the business world is rapidly changing and the business processes are becoming more and more complex making it more difficult for managers to have comprehensive understanding of business environment. The factors of globalization, deregulation, mergers and acquisitions, competition and technological innovation, have forced companies to re-think their business strategies and many large companies have resorted to Business Intelligence (BI) techniques to help them understand and control business processes to gain competitive advantage. BI is primarily used to improve the timeliness and quality of information, and enable managers better understand the position of their firm as in comparison to competitors. BI applications and technologies help companies to analyze changing trends in market share; changes in customer behavior and spending patterns; customers' preferences; company capabilities; and market conditions. It is used to help analysts and managers determine which adjustments are most likely to respond to changing trends. It has emerged as a concept for analyzing collected data with the purpose to help decision making units get a better comprehensive knowledge of an organization’s operations, and thereby make better business decisions. BI is an area of Decision Support System (DSS) that which is an information system that can be used to support complex decision making, and solving complex, semi-structured, or ill-structured problems (Azevedo &

System and method for conducting real-time and historical analysis of complex customer care processes

Abstract: A system for conducting real-time and historical analysis of complex customer care processes, comprising an event collector software module, a complex event processing software module adapted to receive events from the event collector software module, a distributed data storage layer, a business analytics software module adapted to receive and process data from the distributed data storage layer, a distributed configuration software module, and a user interface software module adapted to receive analytics results from the business analytics software module. Upon receiving an event from an event source, the event collector software module at least converts the event into a standard event data format suitable for use by the complex event processing software module and extracts or masks sensitive data from the event based on privacy rules maintained by the distributed configuration software module.

Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data

Abstract: Normal 0 false false false MicrosoftInternetExplorer4 The Definitive Guide to Enterprise-Level Analytics Strategy, Technology, Implementation, and Management Organizations are capturing exponentially larger amounts of data than ever, and now they have to figure out what to do with it. Using analytics, you can harness this data, discover hidden patterns, and use this knowledge to act meaningfully for competitive advantage. Suddenly, you can go beyond understanding how, when, and where events have occurred, to understand why and use this knowledge to reshape the future. Now, analytics pioneer Tom Davenport and the world-renowned experts at the International Institute for Analytics (IIA) have brought together the latest techniques, best practices, and research on analytics in a single primer for maximizing the value of enterprise data. Enterprise Analytics is todays definitive guide to analytics strategy, planning, organization, implementation, and usage. It covers everything from building better analytics organizations to gathering data; implementing predictive analytics to linking analysis with organizational performance. The authors offer specific insights for optimizing supply chains, online services, marketing, fraud detection, and many other business functions. They support their powerful techniques with many real-world examples, including chapter-length case studies from healthcare, retail, and financial services. Enterprise Analytics will be an invaluable resource for every business and technical professional who wants to make better data-driven decisions: operations, supply chain, and product managers; product, financial, and marketing analysts; CIOs and other IT leaders; data, web, and data warehouse specialists, and many others.

Statistical machine learning and business process models systems and methods

Abstract: Methods and systems for knowledge extraction that involve providing analytics and blending the analytics with analysis of one or more knowledge processes are provided. Knowledge extraction may be based on combining analytic approaches, such as statistical and machine learning approaches. Unstructured data, such as numerical, geo-spatial, text, speech, image, video, data, and music, may be used as input for these processes. The methods and systems may convert this unstructured data into a structured knowledge that has some specific utility to its user. Some embodiments may involve service requests delivery, information and knowledge extraction, information and knowledge retrieval, media mining, marketing, and other uses. Different granularity levels of knowledge and information extraction may be provided. This differentiation may be used for monetization of the service.