Showing papers on "Business analytics published in 2014"

The power of social media analytics

Abstract: How to use, and influence, consumer social communications to improve business performance, reputation, and profit.

Integration of Online and Offline Channels in Retail: The Impact of Sharing Reliable Inventory Availability Information

Abstract: Using a proprietary data set, we analyze the impact of the implementation of a “buy-online, pick-up-in-store” BOPS project. The implementation of this project is associated with a reduction in online sales and an increase in store sales and traffic. These results can be explained by two simultaneous phenomena: 1 additional store sales from customers who use the BOPS functionality and buy additional products in the stores cross-selling effect and 2 the shift of some customers from the online to the brick-and-mortar channel and the conversion of noncustomers into store customers channel-shift effect. We explain these channel-shift patterns as an increase in “research online, purchase offline” behavior enabled by BOPS implementation, and we validate this explanation with evidence from the change of cart abandonment and conversion rates of the brick-and-mortar and online channels. We interpret these results in light of recent operations management literature that analyzes the impact of sharing inventory availability information. Our analysis illustrates the limitations of drawing conclusions about complex interventions using single-channel data. This paper was accepted by Alok Gupta, special issue on business analytics.

Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations

Abstract: Much attention is currently being paid in both the academic and practitioner literatures to the value that organisations could create through the use of big data and business analytics (Gillon et a...

Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment

Abstract: We leverage the newly emerging business analytical capability to rapidly deploy and iterate large-scale, microlevel, in vivo randomized experiments to understand how social influence in networks impacts consumer demand. Understanding peer influence is critical to estimating product demand and diffusion, creating effective viral marketing, and designing “network interventions” to promote positive social change. But several statistical challenges make it difficult to econometrically identify peer influence in networks. Though some recent studies use experiments to identify influence, they have not investigated the social or structural conditions under which influence is strongest. By randomly manipulating messages sent by adopters of a Facebook application to their 1.3 million peers, we identify the moderating effect of tie strength and structural embeddedness on the strength of peer influence. We find that both embeddedness and tie strength increase influence. However, the amount of physical interaction between friends, measured by coappearance in photos, does not have an effect. This work presents some of the first large-scale in vivo experimental evidence investigating the social and structural moderators of peer influence in networks. The methods and results could enable more effective marketing strategies and social policy built around a new understanding of how social structure and peer influence spread behaviors in society. This paper was accepted by Alok Gupta, special issue on business analytics.

Big Data Analytics: A Literature Review Paper

Abstract: . In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network da-ta. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyze some of the different analytics methods and tools which can be applied to big data, as well as the opportunities provided by the application of big data analytics in various decision domains. Keywords: big data, data mining, analytics, decision making.

A unified foundation for business analytics

Abstract: Synthesizing prior research, this paper designs a relatively comprehensive and holistic characterization of business analytics - one that serves as a foundation on which researchers, practitioners, and educators can base their studies of business analytics. As such, it serves as an initial ontology for business analytics as a field of study. The foundation has three main parts dealing with the whence and whither of business analytics: identification of dimensions along which business analytics possibilities can be examined, derivation of a six-class taxonomy that covers business analytics perspectives in the literature, and design of an inclusive framework for the field of business analytics. In addition to unifying the literature, a major contribution of the designed framework is that it can stimulate thinking about the nature, roles, and future of business analytics initiatives. We show how this is done by deducing a host of unresolved issues for consideration by researchers, practitioners, and educators. We find that business analytics involves issues quite aside from data management, number crunching, technology use, systematic reasoning, and so forth. Holistic foundation of business analytics introduced, as an ontology for the fieldCharacterization business analytics as evidence-based problem recognition and solvingAdvance a framework of business analytics for a cohesive unifying view of the fieldStimulates thinking about nature, role and future of business analytics initiativesDeduces unresolved issues for considerations by researchers/educators/practitioners

Digitisation, Big Data and the transformation of accounting information

Abstract: Developments in digitisation, software and processing power and the accompanying data explosion create significant alterations, dilemmas and possibilities for enterprises and their finance function. The article discusses a model for understanding data, information and knowledge relationships. We apply the model to examine developments in strategy, organisational and cost structures, digitisation, business analytics, outsourcing, offshoring and cloud computing. We argue that organisations need to be sensitised to different types of knowledge, the challenges in creating and applying that knowledge, and be more circumspect about what can be achieved through advances in information-based technologies and software. We point to both the potential of and the complexities presented by Big Data in relation to the finance function generally and to management accounting information provision specifically. We suggest that ‘Big Data’ and data analysis techniques enable executives to act on structured and unstructured ...

The analytics mandate

Abstract: As more organizations make better use of data, the path to value with analytics is getting crowded and longer. Many companies find they must reconsider and refresh not only their analytical insights, but also the organizational factors necessary to turn insight into advantage. This report, based on a survey of 2,037 professionals and interviews with more than 30 executives, reveals the pressure companies are under to both improve their analytics capabilities and find unique and relevant insights in their data to try to be as good as their last prediction every single day. A joint collaboration between MIT Sloan Management Review and SAS Institute, this research analyzes the changing data landscape. It discusses the five key factors that can keep a company ahead of the analytics crowd. And it unravels the complexities of the most influential factor: the analytics culture.

Big Data Investment, Skills, and Firm Value

Abstract: This paper analyzes how labor market factors have shaped early returns on big data investment using a new data source---the LinkedIn skills database. The data source enables firm-level measurement of the employment of workers with technical skills such as Hadoop, MapReduce, and Apache Pig. From 2006 to 2011, Hadoop investments were associated with 3% faster productivity growth, but only for firms a with significant data assets and b in labor markets where similar investments by other firms helped to facilitate the development of a cadre of workers with complementary technical skills. The benefits of labor market concentration decline for investments in mature data technologies, such as Structured Query Language-based databases, for which the complementary skills can be acquired by workers through universities or other channels. These findings underscore the importance of geography, corporate investment, and skill acquisition channels for explaining productivity growth differences during the spread of new information technology innovations. This paper was accepted by Alok Gupta, special issue on business analytics.

Transformational issues of big data and analytics in networked business

Abstract: The era of big data and analytics is upon us and is changing the world dramatically. The field of Information Systems should be at the forefront of understanding and interpreting the impact of both technologies and management so as to lead the efforts of business research in the big data era. We need to prepare ourselves and our students for this changing world of business. In this discussion, we focus on exploring the technical and managerial issues of business transformation resulting from the insightful adoption and innovative applications of data sciences in business. We end by providing an overview of the papers included in this special issue and outline future research directions.

Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner

Abstract: Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining. Youll be able to: 1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process. 2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases. 3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Nave Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner toolsDiscusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics Includes practical use cases and examples

Encyclopedia of Business Analytics and Optimization

Abstract: As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data - volume, variety, velocity, volatility and veracity - and focus these dimensions towards one critical emphasis - value.The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualisation, interdisciplinary communication and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst or manager interested in making the most of the knowledge resources at their disposal.

Real-time system for multi-modal 3d geospatial mapping, object recognition, scene annotation and analytics

Abstract: A multi-sensor, multi-modal data collection, analysis, recognition, and visualization platform can be embodied in a navigation capable vehicle. The platform provides an automated tool that can integrate multi-modal sensor data including two-dimensional image data, three-dimensional image data, and motion, location, or orientation data, and create a visual representation of the integrated sensor data, in a live operational environment. An illustrative platform architecture incorporates modular domain-specific business analytics “plug ins” to provide real-time annotation of the visual representation with domain-specific markups.

The Current State of Business Intelligence in Academia: The Arrival of Big Data

Abstract: In December 2012, the AIS Special Interest Group on Decision Support, Knowledge and Data Management Systems (SIGDSS) and the Teradata University Network (TUN) cosponsored the Business Intelligence Congress 3 and conducted surveys to assess academia’s response to the growing market need for students with Business Intelligence (BI) and Business Analytics (BA) skill sets. This panel report describes the key findings and best practices that were identified, with an emphasis on what has changed since the BI Congress efforts in 2009 and 2010. The article also serves as a “call to action” for universities regarding the need to respond to emerging market needs in BI/BA, including “Big Data.” The IS field continues to be well positioned to be the leader in creating the next generation BI/BA workforce. To do so, we believe that IS leaders need to continuously refine BI/BA curriculum to keep pace with the turbulent BI/BA marketplace.

Development and Validation of a Learning Analytics Framework: Two Case Studies Using Support Vector Machines

Abstract: Interest in collecting and mining large sets of educational data on student background and performance to conduct research on learning and instruction has developed as an area generally referred to as learning analytics. Higher education leaders are recognizing the value of learning analytics for improving not only learning and teaching but also the entire educational arena. However, theoretical concepts and empirical evidence need to be generated within the fast evolving field of learning analytics. The purpose of the two reported cases studies is to identify alternative approaches to data analysis and to determine the validity and accuracy of a learning analytics framework and its corresponding student and learning profiles. The findings indicate that educational data for learning analytics is context specific and variables carry different meanings and can have different implications across educational institutions and area of studies. Benefits, concerns, and challenges of learning analytics are critically reflected, indicating that learning analytics frameworks need to be sensitive to idiosyncrasies of the educational institution and its stakeholders.

Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners

Abstract: With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

Real-Time Optimization of Personalized Assortments

Abstract: Motivated by the availability of real-time data on customer characteristics, we consider the problem of personalizing the assortment of products for each arriving customer. Using actual sales data from an online retailer, we demonstrate that personalization based on each customer's location can lead to over 10% improvements in revenue compared to a policy that treats all customers the same. We propose a family of index-based policies that effectively coordinate the real-time assortment decisions with the back-end supply chain constraints. We allow the demand process to be arbitrary and prove that our algorithms achieve an optimal competitive ratio. In addition, we show that our algorithms perform even better if the demand is known to be stationary. Our approach is also flexible and can be combined with existing methods in the literature, resulting in a hybrid algorithm that brings out the advantages of other methods while maintaining the worst-case performance guarantees. This paper was accepted by Dimitris Bertsimas, special issue on business analytics.

The impact of supply chain analytics on operational performance: a resource-based view

Abstract: This study seeks to better understand the role of supply chain analytics (SCA) on supply chain planning satisfaction and operational performance. We define the architecture of SCA as the integration of three sets of resources, data management resources (DMR), IT-enabled planning resources and performance management resources (PMR), from the perspective of a resource-based view. Based on the data collected from 537 manufacturing plants, we test hypotheses exploring the relationships among these resources, supply chain planning satisfaction, and operational performance. Our analysis supports that DMR should be considered a key building block of manufacturers’ business analytics initiatives for supply chains. The value of data is transmitted to outcome values through increasing supply chain planning and performance capabilities. Additionally, the deployment of advanced IT-enabled planning resources occurs after acquisition of DMR. Manufacturers with sophisticated planning technologies are likely to take adva...

The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective

Abstract: This study is interested in the impact of two specific business analytic (BA) resources-accurate manufacturing data and advanced analytics-on a firms' operational performance. The use of advanced analytics, such as mathematical optimization techniques, and the importance of manufacturing data accuracy have long been recognized as potential organizational resources or assets for improving the quality of manufacturing planning and control and of a firms' overall operational performance. This research adopted a contingent resource based theory (RBT), suggesting the moderating and mediating role of fact-based SCM initiatives as complementary resources. This research proposition was tested using Global Manufacturing Research Group (GMRG) survey data and was analyzed using partial least squares/structured equation modeling. The research findings shed light on the critical role of fact-based SCM initiatives as complementary resources, which moderate the impact of data accuracy on manufacturing planning quality and mediate the impact of advanced analytics on operational performance. The implication is that the impact of business analytics for manufacturing is contingent on contexts, specifically, the use of fact-based SCM initiatives such as TQM, JIT, and statistical process control. Moreover, in order for manufacturers to take advantage of the use of data and analytics for better operational performance, complementary resources such as fact-based SCM initiatives must be combined with BA initiatives focusing on data quality and advanced analytics.

Strategic business modeling: representation and reasoning

Abstract: Business intelligence (BI) offers tremendous potential for business organizations to gain insights into their day-to-day operations, as well as longer term opportunities and threats. However, most of today's BI tools are based on models that are too much data-oriented from the point of view of business decision makers. We propose an enterprise modeling approach to bridge the business-level understanding of the enterprise with its representations in databases and data warehouses. The business intelligence model (BIM) offers concepts familiar to business decision making--such as goals, strategies, processes, situations, influences, and indicators. Unlike many enterprise models which are meant to be used to derive, manage, or align with IT system implementations, BIM aims to help business users organize and make sense of the vast amounts of data about the enterprise and its external environment. In this paper, we present core BIM concepts, focusing especially on reasoning about situations, influences, and indicators. Such reasoning supports strategic analysis of business objectives in light of current enterprise data, allowing analysts to explore scenarios and find alternative strategies. We describe how goal reasoning techniques from conceptual modeling and requirements engineering have been applied to BIM. Techniques are also provided to support reasoning with indicators linked to business metrics, including cases where specifications of indicators are incomplete. Evaluation of the proposed modeling and reasoning framework includes an on-going prototype implementation, as well as case studies.

Applied Predictive Analytics : Principles and Techniques for the Professional Data Analyst

Abstract: Learn the art and science of predictive analytics techniques that get resultsPredictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included.The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition todayThis guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutionsExplains methods, principles, and techniques for conducting predictive analytics projects from start to finishIllustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenariosA companion website provides all the data sets used to generate the examples as well as a free trial version of softwareApplied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

Quality Indicators for Learning Analytics.

Abstract: This article proposes a framework of quality indicators for learning analytics that aims to standardise the evaluation of learning analytics tools and to provide a mean to capture evidence for the impact of learning analytics on educational practices in a standardised manner. The criteria of the framework and its quality indicators are based on the results of a Group Concept Mapping study conducted with experts from the field of learning analytics. The outcomes of this study are further extended with findings from a focused literature review.