Showing papers on "Business analytics published in 2009"

Advanced analytics: opportunities and challenges

Abstract: – Advanced analytics‐driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics., – A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice., – The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses., – The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.

Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy

Abstract: About the Contributors. Preface. Acknowledgments. PART ONE The Landscape. Chapter 1 The Evolving Business Landscape. Navigating Uncharted Waters. Shifting from Reactive to Proactive. Strategies for Capturing Value. Motivation for Change. The Evolving Organization. Chapter 2 Models from Science and Nature. Quantum Physics. Evolutionary Biology and Living Systems. Complexity Science and Chaos. Systems Theory and Systems Thinking. PART TWO The Success Factors. Chapter 3 Effective Communication. Benefits of Effective Communication. Principles of Communication. Communication in a High-Tech Economy. Nonverbal Communication. Theory of Relational Coordination. Principles of Dialogue. Art of Listening. Storytelling. Chapter 4 Collaboration. Collaborating for the Future. Creating a Collaborative Culture. Building Collaborative Teams. Value of Trust. Collaborative Technologies. Collaboration in Action: A Case Study. Chapter 5 Innovation. Creativity. Innovation in the Marketplace. Tips from the Field. Chapter 6 Adaptability. The Shifting Paradigm. Traditional Methods. The New Paradigm. Models for Adaptive Organizations. Leveraging Chaos in Organizations. Conflict Resolution: A Living Systems Approach. The Learning Organization. A New Global Organization. Chapter 7 Leadership. The Conscious Leader. Social Intelligence. Paradox of Empowerment. 10 Principles for Leading a Dynamic Organization. Tapping Organizational Wisdom. PART THREE Models and Practices. Chapter 8 Systems Thinking. Basics of Systems Thinking. Systems View of Business Analytics. Chapter 9 Holacracy. Evolving the Organization. Introducing Holacracy. Organizational Structure. Organizational Control. Core Practices. Shared Language and Meaning. PART FOUR Beyond Our Corporate Borders. Chapter 10 Possibilities. Holacracy in the World. Edgewalkers. Organizations on Purpose. Bottom Line. Appendix A: Resources. Appendix B: Suggested Reading. About the Author. Index.

Understanding User-Web Interactions via Web Analytics

Abstract: This lecture presents an overview of the Web analytics process, with a focus on providing insight and actionable outcomes from collecting and analyzing Internet data. The lecture first provides an overview of Web analytics, providing in essence, a condensed version of the entire lecture. The lecture then outlines the theoretical and methodological foundations of Web analytics in order to make obvious the strengths and shortcomings of Web analytics as an approach. These foundational elements include the psychological basis in behaviorism and methodological underpinning of trace data as an empirical method. These foundational elements are illuminated further through a brief history of Web analytics from the original transaction log studies in the 1960s through the information science investigations of library systems to the focus on Websites, systems, and applications. Following a discussion of on-going interaction data within the clickstream created using log files and page tagging for analytics of Website and search logs, the lecture then presents a Web analytic process to convert these basic data to meaningful key performance indicators in order to measure likely converts that are tailored to the organizational goals or potential opportunities. Supplementary data collection techniques are addressed, including surveys and laboratory studies. The overall goal of this lecture is to provide implementable information and a methodology for understanding Web analytics in order to improve Web systems, increase customer satisfaction, and target revenue through effective analysis of userWebsite interactions. Table of Contents: Understanding Web Analytics / The Foundations of Web Analytics: Theory and Methods / The History of Web Analytics / Data Collection for Web Analytics / Web Analytics Fundamentals / Web Analytics Strategy / Web Analytics as Competitive Intelligence / Supplementary Methods for Augmenting Web Analytics / Search Log Analytics / Conclusion / Key Terms / Blogs for Further Reading / References

Business process intelligence

Abstract: Business Process Intelligence (BPI,) is an emerging area that is getting increasingly popularfor enterprises The need to improve business process efficiency, to react quickly to changes and to meet regulatory compliance is among the main drivers for BPI BPI refers to the application of Business Intelligence techniques to businessprocesses andcomprises a large range ofapplication areas spanning from process monitoring and analysis to process discovery, conformance checking, prediction and optimization This chapter provides an introductory overview of BPI and its application areas and delivers an understanding of how to apply BPI in one's own setting In particular it shows how process mining techniques such as process discovery and conformance checking can be used to support process modeling and process redesign In addition, it illustrates how processes can be improved and optimized over time using analytics for explanation, prediction, optimization and what-if-analysis Throughout the chapter a strong emphasis is given to describe tools that use these techniques to support BPI Finally, major challenges for applying BPI in practice and future trends are discussed

Cognition-Driven Decision Support for Business Intelligence

Abstract: Cognition-driven decision support system (DSS) has been recognized as a paradigm in the research and development of business intelligence (BI). Cognitive decision support aims to help managers in their decision making from human cognitive aspects, such as thinking, sensing, understanding and predicting, and fully reuse their experience. Among these cognitive aspects, decision makers situation awareness (SA) and mental models are considered to be two important prerequisites for decision making, particularly in ill-structured and dynamic decision situations with uncertainties, time pressure and high personal stake. In todays business domain, decision making is becoming increasingly complex. To make a successful decision, managers SA about their business environments becomes a critical factor. This book presents theoretical models as well practical techniques of cognitiondriven DSS. It first introduces some important concepts of cognition orientation in decision making process and some techniques in related research areas including DSS, data warehouse and BI, offering readers a preliminary for moving forward in this book. It then proposes a cognition-driven decision process (CDDP) model which incorporates SA and experience (mental models) as its central components. The goal of the CDDP model is to facilitate cognitive decision support to managers on the basis of BI systems. It also presents relevant techniques developed to support the implementation of the CDDP model in a BI environment. Key issues addressed of a typical business decision cycle in the CDDP model include: natural language interface for a managers SA input, extraction of SA semantics, construction of data warehouse queries based on the mangers SA and experience, situation information retrieval from data warehouse, how the manager perceives situation information and update SA, how the managers SA leads to a final decision. Finally, a cognition-driven DSS, FACETS, and two illustrative applications of this system are discussed.

The New Know: Innovation Powered by Analytics

Abstract: Learn to manage and grow successful analytical teams within your business Examining analytics-one of the hottest business topics today-The New KNOW argues that analytics is needed by all enterprises in order to be successful. Until now, enterprises have been required to know what happened in the past, but in today's environment, your organization is expected to have a good knowledge of what happens next. This innovative book covers Where analytics live in the enterprise The value of analytics Relationships betwixt and between Technologies of analytics Markets and marketers of analytics The New KNOW is a timely, essential resource to staying competitive in your field.

Visual Analytics for the Strategic Decision Making Process

Abstract: The collection and storage of huge amounts of data is no longer a challenge by itself. However, rapidly growing data repositories are creating considerable challenges in many application areas. Visualizations that worked well with a few data items now produce confusing or illegible displays. Decision-makers struggle to act based on a severely restricted understanding of the situation. The goal of Visual Analytics is to overcome this information overload and create new opportunities with these large amounts of data and information. The key challenge is to intelligently combine visualization techniques and analytic algorithms, and to enable the human expert to guide the decision making process. This chapter covers interesting and relevant previous work on situation awareness, naturalistic decision making, and decision-centred visualization. These concepts are put into the context of Visual Analytics research and are further illustrated by application examples.

Predictive business – fresh initiative or old wine in a new bottle

Abstract: Purpose – The purpose of this paper is to present a conceptual analysis of the theoretical and managerial bases and objectives of predictive business. Predictive business refers to operational decision‐making and the development of business processes on the basis of business event analysis. It supports the early recognition of business opportunities and threats, better customer intimacy and agile reaction to changes in business environment. An underlying rationale for predictive business is the attainment of competitive advantage through better management of information and knowledge.Design/methodology/approach – The approach to this article is conceptual and theoretical. The literature‐based discussion and analysis combines the perspectives of business performance management, business intelligence, and knowledge management to provide a new model of thinking and operation.Findings – For a company predictive business is simultaneously a practical challenge and an epistemic one. It is a practical challenge ...

Analytics integration server within a comprehensive framework for composing and executing analytics applications in business level languages

Abstract: Systems, methods and articles of manufacture are disclosed for building and executing analytics solutions. Such a solution may provide a comprehensive analytics solution (e.g., a risk assessment, fraud detection solution, dynamic operational risk evaluations, regulatory compliance assessments, etc.). The analytics solution may perform an analytics task using operational data distributed across a variety of independently created and governed data repositories in different departments of an organization. A framework is disclosed which allows a user (e.g., a risk analyst) to compose analytical tools that can access data from a variety of sources (both internal and external to an enterprise) and perform a variety of analytic functions.

Providing collaborative business intelligence

Abstract: To provide collaborative business intelligence in an enterprise, an analytics platform is provided to receive an event associated with the enterprise and to perform analysis in response to the event. A virtual collaboration platform is provided to enable plural users to interact with each other and to use resources of the analytics platform.

A model for Business Intelligence Systems' Development

Abstract: Often, Business Intelligence Systems (BIS) require historical data or data collected from var-ious sources. The solution is found in data warehouses, which are the main technology used to extract, transform, load and store data in the organizational Business Intelligence projects. The development cycle of a data warehouse involves lots of resources, time, high costs and above all, it is built only for some specific tasks. In this paper, we’ll present some of the aspects of the BI systems’ development such as: architecture, lifecycle, modeling techniques and finally, some evaluation criteria for the system’s performance.

The Measurement of Business Intelligence 1

Abstract: In today’s rapidly changing business environment, the need for timely and effective business information is recognized as essential for organizations not only to succeed, but even to survive. In this article, business intelligence (BI) refers to a managerial philosophy and a tool used to help organizations manage and refine business information with the objective of making more effective business decisions (Ghoshal and Kim, 1986; Gilad and Gilad, 1986). The term BI can be used to refer to:

Extending BPM Environments of Your Choice with Performance Related Decision Support

Abstract: What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools, process optimizations or a combination of such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.

Enabling analysts in managed services for CRM analytics

Abstract: Data analytics tools and frameworks abound, yet rapid deployment of analytics solutions that deliver actionable insights from business data remains a challenge. The primary reason is that on-field practitioners are required to be both technically proficient and knowledgeable about the business. The recent abundance of unstructured business data has thrown up new opportunities for analytics, but has also multiplied the deployment challenge, since interpretation of concepts derived from textual sources require a deep understanding of the business. In such a scenario, a managed service for analytics comes up as the best alternative. A managed analytics service is centered around a business analyst who acts as a liaison between the business and the technology. This calls for new tools that assist the analyst to be efficient in the tasks that she needs to execute. Also, the analytics needs to be repeatable, in that the delivered insights should not depend heavily on the expertise of specific analysts. These factors lead us to identify new areas that open up for KDD research in terms of 'time-to-insight' and repeatability for these analysts. We present our analytics framework in the form of a managed service offering for CRM analytics. We describe different analyst-centric tools using a case study from real-life engagements and demonstrate their effectiveness.

SIMPLE: A Strategic Information Mining Platform for Licensing and Execution

Abstract: Intellectual Properties (IP), such as patents and trademarks, are one of the most critical assets in today’s enterprises and research organizations. They represent the core innovation and differentiators of an organization. When leveraged effectively, they not only protect a business from its competition, but also generate significant opportunities in licensing, execution, long term research and innovation. In certain industries, e. g., Pharmaceutical industry, patents lead to multi-billion dollar revenue per year. In this paper, we present a holistic information mining solution, called SIMPLE, which mines large corpus of patents and scientific literature for insights. Unlike much prior work that deals with specific aspects of analytics, SIMPLE is an integrated and end-to-end IP analytics solution which addresses a wide range of challenges in patent analytics such as the data complexity, scale, and nomenclature issues. It encompasses techniques for patent data processing and modeling, analytics algorithms, web interface and web services for analytics service delivery and end-user interaction. We use real-world case studies to demonstrate the effectiveness of SIMPLE.

Progressive Methods in Data Warehousing and Business Intelligence: Concepts and Competitive Analytics

Abstract: Recent technological advancements in data warehousing have been contributing to the emergence of business intelligence useful for managerial decision making. Progressive Methods in Data Warehousing and Business Intelligence: Concepts and Competitive Analytics presents the latest trends, studies, and developments in business intelligence and data warehousing contributed by experts from around the globe. Consisting of four main sections, this book covers crucial topics within the field such as OLAP and patterns, spatio-temporal data warehousing, and benchmarking of the subject.

Using business intelligence to improve performance.

Abstract: Cleveland Clinic's enterprise performance management program offers proof that comparisons of actual performance against strategic objectives can enable healthcare organization to achieve rapid organizational change. Here are four lessons Cleveland Clinic learned from this initiative: Align performance metrics with strategic initiatives. Structure dashboards for the CEO. Link performance to annual reviews. Customize dashboard views to the specific user.

Enhancing business intelligence with unstructured data

Abstract: Quality early warning and proactive customer churn detection are two examples of applications that can benefit from insights gained from unstructured text data. The term “Unstructured Business Intelligence” describes methods and tools that enable data warehouse applications to use unstructured information. This article introduces the key components of Unstructured Business Intelligence and describes its realization within IBM InfoSphere Warehouse Enterprise Edition. 1 Unstructured Business Intelligence

EDI-based and XML-based business-to-business integration: a statistical analysis

Abstract: This paper analyses six factors that are expected to facilitate Electronic Data Interchange (EDI)-based or Extensible Markup Language (XML)-based Business-to-Business (B2B) integration. Following the Technology-Organisation-Environment (TOE) framework, the paper focuses on one technological factor, four organisational factors and one environmental factor. An e-business framework is a standard for business documents, business processes and messaging interfaces in B2B integration. Six hypotheses on the use of EDI-based or XML-based e-business frameworks are based on the literature. These hypotheses are tested using logistic regression analysis. The data involve 3619 European companies. Large numbers of enterprise information systems and company employees facilitate the use of e-business frameworks. A high volume of sales and educational level of the employees in the company facilitate especially the use of XML-based e-business frameworks. Moreover, if the company primarily has other companies as customers, it is more likely to use an e-business framework. The number of sites in the company has no effects on this use.

Semantic wiki aided business process specification

Abstract: This paper formulates a collaborative system for modeling business application. The system uses a Semantic Wiki to enable collaboration between the various stakeholders involved in the design of the system and translates the captured intelligence into business models which are used for designing a business system.

A conceptual and analytical framework for management of integrated knowledge based logistics providers

Abstract: Driven by increasing competitive forces and the business transformation brought about by internet-based technologies, the structure and landscape of the logistics industry has changed drastically in the last few years. A new breed of logistics service providers is emerging, who has developed and adopted a new operating model which we term Integrated Knowledge Based Logistics (IKL). An IKL is characterised by its complete shift in focus, from the asset intensive operational aspects of moving goods to a variety of knowledge-based tasks such as synchronising activities between various parties in the supply chain, and ensuring supply chain continuity even in the face of disruptions. In this paper, we present the structure of such an entity, discuss its attributes and identify critical factors that impact its success. We also show how mathematical modelling tools can be employed to solve some decision problems the logistics companies face in synchronising various activities across the network.

Information Quality Improvement as a Measure of Business Intelligence System Benefits

Abstract: Since business intelligence systems' impact on performance is first of all long-termed and indirect, most measures of business value are not sufficiently close to immediate influence of such systems and therefore not suitable to justify investments into business intelligence systems in real business environments. Thus, measures related to increased information quality as a result of business intelligence systems introduction are commonly used. The purpose of this study is to test how much does implementation of business intelligence systems actually contribute to solving the major issues regarding information quality. Empirical data were collected through a survey of Slovenian medium and large size organizations. Quantitative analysis was carried out on the data, which related to 181 medium and large size organizations. The results of the analysis show that business intelligence systems actually have a positive impact on both segments of information quality, namely content quality and media quality. However, the impact of business intelligence systems on media quality is stronger, while the quality of content is more important for making better business decisions and providing higher business value of business intelligence systems. Thus, there is still a gap between available information quality and knowledge workers' needs, in other words - key information quality problems still exist.

Business Metrics: A Key to Competitive Advantage

Abstract: INTRODUCTION Business metrics and performance measures serve as dashboard gauges that help in guiding the strategic direction of a firm (Rubin, 1991). The dashboard consists of appropriate gauges, metrics, which indicate the current performance, baselines, directional trends, and targets. These gauges indicate where a business is headed if the current strategies were to continue unchanged. Any change in the environment in which the firm is competing in will affect the performance of the firm. This lowered performance of the firm should then be captured as measurements by the gauges, metrics, as long as the proper metrics are being deployed. These measures would be the pivotal sparks leading to changes in the firm's business strategies. Timely and appropriate steering of a firm's business strategies is a key matter for any firm in sustaining business success. Such tight management of an organization's strategies, however, is possible only by the knowledge and measurement of the appropriate metrics. Different metrics serve different purposes. In general, there are business metrics for accountability purposes and others for organizational improvement purposes (Irwin, 1997). Some measures are used for the efficient strategic steering of a firm, while other measures are used for communicating the proper worth of a business to all interested parties. Business metrics serve the different interests of different stakeholders. Some business metrics are used as the basis for an organizational tune-up, quality improvement, or business process reengineering. In such cases, the stakeholders are generally internal to the firm. There are other business metrics that provide accountability measures used by shareholders, customers, vendors, or creditors in evaluating the general quality of a provider or estimating the future growth of a firm. The employees within a firm are the ones who ultimately implement the business strategies. Proper individual performance measurements help define and promote desired behavior, activities, and attitudes within an organization (McChesney, 1996). The right behavior must be consistent with and in support of the strategies an organization has adopted to move it towards its preferred future. According to McChesney (1996), people do what is inspected and not what is expected, thus requiring the proper channeling of the various metrics to employees at all different levels, (Aggarwal, 2004). Unfortunately, many people do not understand what the information presented by the business metrics means. Most employees are not mid-level and senior managers and therefore are unlikely to have a grasp of core financial concepts, performance improvement practices, and the tenets of operational excellence. To help in clarifying the proper usage of business metrics, this paper presents a survey of the application of business metrics in various business activities, highlighting the various constructs that guide the adaptability of the metric to the business application, ultimately providing managers and practitioners with guidelines for the selection of the proper metrics that serve the strategic direction of the firm. The paper presents some of the limitations of the traditional use of metrics, followed by some guidelines for constructing and selecting good business metrics. Following this, the paper presents examples of the applications of business metrics and their implications in the steering of corporate strategic directions. Finally, the paper concludes by highlighting the importance of business metrics and providing guidelines for managers and practitioners for selecting the proper business metrics to steer corporate strategic directions. LIMITATION TRADITIONAL BUSINESS METRICS Traditionally, businesses have used financial performance measures as their mainstay in (a) tracking their gains and losses, (b) formulating business strategies, (c) communicating market value to shareholders, and (d) analyzing competitors' strengths and weaknesses, Figure 1. …

Practical approaches to the teaching of business english

Abstract: The term Business English is used to cover the English taught to a wide range of professional people, and students in full-time education preparing for a business career. This paper reviews the practices in the teaching of Business English to the undergraduates who have little or no experience of the business world. Generally, these pre-experienced learners gain their knowledge of business largely from books; as a result, such knowledge is incomplete and theoretical rather than practical. They are also less aware of their language needs in terms of communicating in real life business situations. This paper presents the effective strategies and practical approaches that can be incorporated in business English classrooms. The approaches are organized around a tripartite structure to create authentic business contexts. The three parts are (a) combining authentic, framework and tailor-made materials, (b) integrating business skills with simulations, and (c) implementing Information and Communication Technology (ICT) as teaching and learning tools. Sample activities will be provided in order to show the practicality of the approaches. Teaching Business English is far more than teaching Business or Language; it is about teaching communication in the authentic business contexts.

Collaborative business relationships and the use of ICT

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CRM Analytics Framework

Abstract: Implementing a CRM Analytics solution for a business involves many steps including data extraction, populating the extracted data into a warehouse, and running an appropriate mining algorithm. We propose a CRM Analytics Framework that provides an end-to-end framework for developing and deploying pre-packaged predictive modeling business solutions, intended to help in reducing the time and effort required for building the application. Standardization and metadata-driven development are used in the solution; this makes the framework accessible to nonexperts. We describe our framework that makes use of industry standard software products and present a case study of its application in the financial domain.