Showing papers on "Business analytics published in 2003"

Essentials of Business Research Methods

Abstract: Managers increasingly must make decisions based on almost unlimited information. How can they navigate and organize this vast amount of data? Essentials of Business Research Methods provides research techniques for people who aren't data analysts. The authors offer a straightforward, hands-on approach to the vital managerial process of gathering and using data to make clear business decisions. They include such critical topics as the increasing role of online research, ethical issues, data mining, customer relationship management, and how to conduct information-gathering activities more effectively in a rapidly changing business environment. This is the only such book that includes a chapter on qualitative data analysis, and the coverage of quantitative data analysis is more extensive and much easier to understand than in other works. The book features a realistic continuing case throughout the text that enables students to see how business research information is used in the real world. It includes applied research examples in all chapters, as well as Ethical Dilemma mini - cases, and interactive Internet applications and exercises.

Computer system and method for business data processing

Abstract: The system enables business people to understand the impact of business transactions, changes and events in real-time using advanced rules and analytics to filter, categorize and interpret the significance of streams of real-time information. Most business performance analysis today is done manually and this process is a time consuming and skilled task leading to a time delay in producing the analysis. This time lag between the transaction or event happening and being able to take action on the analysis is measured in weeks or months at many companies. By blending real-time information with historical data and performance goals, this system enables business users to assess business events and collaborate within teams to drive optimal business performance. Using forecasting techniques enables business managers to predict the likelihood of achieving a particular goal without relying on manual analysis by a skilled analyst. The system automatically updates the forecast based on real-time changing data, enabling the business manager to have an up to the minute and statistically valid projection of future business performance.

Business Intelligence: The Savvy Manager's Guide

Abstract: Following the footsteps of the first edition, the second edition of Business Intelligence is a full overview of what comprises business intelligence. It is intended to provide an introduction to the concepts to uncomplicate the learning process when implementing a business intelligence program. Over a relatively long lifetime (7 years), the current edition of book has received numerous accolades from across the industry for its straightforward introduction to both business and technical aspects of business intelligence. As an author, David Loshin has a distinct ability to translate challenging topics into a framework that is easily digestible by managers, business analysts, and technologists alike. In addition, his material has developed a following (such as the recent Master Data Management book) among practitioners and key figures in the industry (both analysts and vendors) and that magnifies our ability to convey the value of this book. Guides managers through developing, administering, or simply understanding business intelligence technology Keeps pace with the changes in best practices, tools, methods and processes used to transform an organization's data into actionable knowledge Contains a handy, quick-reference to technologies and terminology. Table of Contents 1. Business Intelligence - An Introduction 2. Value Drivers 3. Planning for Success 4. Developing a Business Intelligence Roadmap 5. The Business Intelligence Environment 6. Business Models and Information Flow 7. Data Requirements Analysis 8. Data Warehouses and the Technical BI Architecture 9. Business Metadata 10. Data Profiling 11. Business Rules 12. Data Quality 13. Data Integration 14. High Performance BI 15. Alternate Information Contexts 16. Location Intelligence and Spatial analysis? 17. Knowledge Discovery, Data Mining, and Analytics 18. Using Publicly Available Data 19. Knowledge Delivery 20. New and Emerging Techniques 21. Quick Reference

Automatic generation of a dimensional model for business analytics from an object model for online transaction processing

Abstract: The present invention automatically generates a dimensional model from an object model. The relationships in the object model are used to infer foreign key relationships in the dimensional model.

An agent-based architecture for analyzing business processes of real-time enterprises

Abstract: As the desire for business intelligence capabilities for e-business processes expands, existing workflow management systems and decision support systems are not able to provide continuous, real-time analytics for decision makers Business intelligence requirements may appear to be different across the various industries, but the underlying requirements are similar nformation that is integrated, current, detailed, and immediately accessible In this paper we introduce an agent-based architecture that supports a complete business intelligence process to sense, interpret, predict, automate and respond to business processes and aims to decrease the time it takes to make business decisions In fact, there should be almost zero-latency between the cause and effect of a business decision Our architecture enables analysis across corporate business processes notifies the business of auctionable recommendations or automatically triggers business operations, effectively closing the gap between business intelligence systems and business processes

Quality dimensions, capabilities and business strategy: An empirical study in high- tech industry

Abstract: Excellence in quality helps firms gain customer loyalty and achieve competitive edge. Previous studies have suggested the need to develop quality capabilities to improve business performance. However, quality is multi-dimensional, and the development of each dimension requires different sets of resources. It is important for a firm to develop quality capabilities with a focus on a particular set of quality dimensions to support its strategic needs. This study hypothesizes that the relative contribution of the different quality dimensions to business performance is contingent on a given business strategy. We have identified the theoretical relationship between quality capabilities (expressed as a set of dimensions) and business strategy. Using the data collected from 113 high-tech manufacturing firms in Taiwan, we have analyzed and prescribed the matching of seven quality dimensions with three business strategies. Statistical results indicate that the business performance of quality management is strategy ...

Business intelligence : the savvy manager's guide, getting onboard with emerging IT

Abstract: Business Intelligence and Information Exploitation. The Value of Information. Business Models and Information Flow. Data Models and Meta Data. Business Intelligence Environments. Business Rules. Data Profiling. Data Quality and Information Compliance. Information Integration. Alternate Information Contexts. Data Enhancement. Knowledge Discovery. Guide to Public Data. References.

Intelligent Business Analytics — A Tool to Build Decision-Support Systems for eBusinesses

Abstract: An important principle in managing any business is ‘What can't be measured, can't be managed’. The complexity of businesses today means that in order to measure business performance one needs to perform considerable analysis of data gathered in vast quantities on a regular basis. Therefore data analysis is at the heart of decision making in all business applications. There is, however, a significant degree of manual intervention in preparing, presenting and analysing business data. Recent advances in intelligent software technology have produced a number of novel techniques to model the human decision-making process. Data analysis tools have in the past been used by businesses mainly as a reactive tool. The pace of change and increased competition means that those businesses that can turn data into information and then into action quickly will have a better chance to survive and out-manoeuvre their rivals. This requires a fundamental change in the way data is used in the enterprise, from a reactive manner to a proactive one. The implications of recent changes can be significant on the level of skilled resources required as well as the cost of such operations. Intelligent software can play an important role in automating the analysis process and up-skill the business users. In this paper we will describe the intelligent business analytics (IBA) platform and two applications developed using it. The paper will focus on soft computing as an emerging technology suitable for incorporation into business analytics applications to model hidden patterns in data and to explain such patterns automatically.

Using data analytics for greater profits

Abstract: Looks at Harrah’s casino business and how it is run, with the aid of analytics: accessing; aggregating; and analysing large amounts of data from many sources. States Harrah’s converts data into knowledge to enhance its ability to make effective business decisions. Lists out five “rules of the road” with descriptions of them in full. Concludes that advances in analytics are breathing new life into companies’ efforts in creating new businesses and line extensions.

The Benefits of Business Planning in Early Stage Small Enterprises

Abstract: This article reports on the results of an empirical study into the nature and role of business planning within the new venture. In order to explore the benefits of business planning and compare the experiences of surviving versus failed businesses, the researchers analyzed 327 micro-enterprises that had been launched during the 1997–1999 period. There was no direct association between early stage planning and survival/failure during the company’s first three years. While the learning and efficiency benefits of the business plan were quite high, these benefits were not found to be directly associated with survival or failure. The business plan’s impact, both in terms of helping the entrepreneur learn more about managing his/her business and enabling him/her to organize the business more efficiently was significantly higherwhen the plan (i) incorporated a market research component or (ii) had been prepared with the intent of being used to guide decision-making.

E-CRM Analytics: The Role of Data Integration

Abstract: Electronic Customer Relationship Management (e-CRM) Analytics is the process of analyzing and reporting online customer/visitor behavior patterns with the objective of acquiring and retaining customers through stronger customer relationships. To better understand the role of data integration in achieving the goals of e-CRM, the authors conducted a study by means of a survey. The results of this study propose that although online, offline and external data integration has its complexities, the value added is significant. This survey of CRM professionals is composed of two parts. The first part investigated the nature of the data integrated and the data architecture deployed. The second part analyzed the technological and organizational value added with respect to the e-CRM initiative. The findings suggest that organizations that integrate data from various customer touch-points have significantly higher benefits, user satisfaction and return on their investment (ROI) than those that do not. Additional insights are also presented exploring the role of data integration in e-CRM projects at both business to business (B2B) and business-to-consumer (B2C) firms. For organizations implementing e-CRM, this study reveals that data integration is worth their time, money and efforts.

Development of an electronic-business planning model for small and medium-sized enterprises

Abstract: Many companies want to conduct more business and improve performance using electronic, computer-based and telecommunication-enabled business processes. However, many small and medium-sized enterprises (SMEs) have little understanding of, and cannot comprehend, future technological developments. This paper presents the findings of a research project funded by the Centre for Enterprise in 2000–2001 into developing an e-business planning model for SMEs. The model architecture is top-down in the sense of moving from a high-level strategic appraisal of e-business opportunities in level 1 to the selection of particular e-business technologies in level 4; but it is also critical for a company to integrate the different levels of decision-making from the identification of business value to the implementation of relevant technologies in order to facilitate the delivery of products/services to customers. The paper also reports the findings of a case study conducted in a SME in order to illustrate how the e-business...

A Method to Extend BPEL4WS to Enable Business Performance Measurement.

Abstract: The web services paradigm provides organizations with an environment to enhance B2B communications. The aim is to create modularized services supporting the business processes within their organization and also those external entities participating in these same business processes. BPEL4WS provides a notation for expressing business process behaviour based on web services. The current BPEL4WS framework does not include the functionality required for web service execution performance measurement from an organization perspective. As such, a shift to this paradigm is at the expense of the organization’s performance knowledge, as this knowledge will become buried within the internal processing of the web service platform. This research provides a method to incorporate the definition of business process performance measures together with a method to capture web service audit trail information within the structure of the BPEL4WS business process.

Game Development Toolkit for Business People in Japan

Abstract: This article describes the authors' experience of developing a business gaming course for business people in Japan. The course that was developed consists of (a) simple gaming experiments among multiple students using ALEXANDER ISLANDS, a tiny business game on the World Wide Web; (b) lectures to make the students understand the core concepts of business management through the simulation; and (c) a business game developed by the students themselves using business model description language (BMDL), business model development system (BMDS), and automated agent players (AAPs). The course is distinctive in the sense that, through the business gaming, the authors aim to develop students' skills (a) to implement their own models for specific business firms and (b) to understand business processes among companies. This article describes the background and motivation, basic principles, the architecture and implementation of BMDL/BMDS/AAPs, and the experimental results.

Enterprise Management with SAP SEM™/ Business Analytics

Abstract: Current Problems and Requirements Business Management Background Business Management Instruments Information Technology Instruments Components of the SAP Solution Case Studies Resume.

Business intelligence for small business: assessment, framework & agenda

Abstract: Small businesses, businesses with 20 or less personnel, account for a significant proportion of econo mic activity and it is the economic sector that employs the most people in OECD countries. Small businesses are fundamentally different than larger organizations in their operations and decision-making processes. Business intelligence (BI) is the current approach to management decision support in large organizations. It includes personal decision support, analytics, enterprise reporting, and data warehousing. BI is also arguably the biggest current growth area in IT investment. This paper explores why BI applications are not widely used in small businesses. It develops a research model that may explain BI adoption, or lack of adoption, in small business. A research agenda to investigate this economically important issue is presented.

DEA-based performance predictive design of complex dynamic system usiness process improvement

Abstract: Business performance prediction draws a vision that accounts for the measures of business process improvement through benchmarking which is to find best practices in an industry or organization and suggest solutions for business process improvement. Data envelopment analysis (DEA), a nonparametric mathematical programming, is utilized for business performance prediction that gives the best benchmark of business process improvement. The DEA-based benchmarking procedure and SA (slacks-adjusted)-AR (assurance region)-DEA model-based performance predictive design models are developed in this paper. The predictive design method considering business constraints on efficient frontiers of decision-making units (DMUs) is a kind of quantitative business knowledge discovery based on historical business data that synthetically takes both successful and impolitic business practices of various organizations or within an organization into consideration.

A Methodological Framework for Business Modelling.

Abstract: Globalisation phenomenon has created a very competitive environment for modern business organisations. In order to survive and continue being competitive in that environment, an organisation has to adapt to it quickly with a minimal negative impact over its current ways of working and organising. A business model contains the knowledge needed not only to support managers’ decisions concerned with change and adaptation, but to ensure the opportunity and relevance of the information produced by the automated systems supporting them. The purpose of this paper is to present a methodological framework for business modelling. This framework allows its users to represent organisation’s elements from different perspectives taking into account their relationships. A business model is presented as a set of three interrelated models – the Business Goals model, the Business Processes model, and the Information Systems model. The main contribution of our paper is to make visible and explicit the relationships among the three levels: goals, business processes and information systems. These relationships are commonly hidden or implicit in most business modelling methods. Our proposition has proven its usefulness as a strategic management tool in two studies cases. 1 Published in the Proceedings of the 5th International Conference on Enterprise Information Systems (ICEIS’2003), Angers, France, 23-26 April, 2003.

Facilitating Electronic Business Planning with Decision Making Support Systems

Abstract: Decision making support has evolved over time and across disciplines [3] with much research focusing on ways to improve such support. In the process, there has been an evolution from simple data access and reporting to complex analytical, creative support. Meanwhile electronic business planning, a complex process, that can be facilitated through management science, economics, statistics, and other technological tools, has shown great promise for overall business control and management in the new E-era. However, managers are rarely aware of these tools, are unskillful in their use, unwilling to, or incapable of, acquiring the proficiency. In this ongoing project, we intend to explore how decision making support systems (DMSS) can facilitate electronic business planning by deriving an operational e-business model and illustrating how the model can be delivered through a DMSS. Such delivery will enable managers to measure and evaluate the impact of plans on electronic business performance.

Computer system and method for detecting and processing changes in data

Abstract: The system enables business people to understand the impact of business transactions, changes and events in real-time using advanced rules and analytics to filter, categorize and interpret the significance of streams of real-time information. Most business performance analysis today is done manually and this process is a time consuming and skilled task leading to a time delay in producing the analysis. This time lag between the transaction or event happening and being able to take action on the analysis is measured in weeks or months at many companies. By blending real-time information with historical data and performance goals, this system enables business users to assess business events and collaborate within teams to drive optimal business performance. Using forecasting techniques enables business managers to predict the likelihood of achieving a particular goal without relying on manual analysis by a skilled analyst. The system automatically updates the forecast based on real-time changing data, enabling the business manager to have an up to the minute and statistically valid projection of future business performance.

Monitoring and Managing Business Performance from a Portfolio Perspective

Abstract: This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Abstract Corporations often begin each year with a corporate strategy, a business plan and high expectations of success from their pending business operations. As they execute the business plan they meet with successes and failures, all the time wondering if they should reassess future business plans. Are they better off drilling the remainder of the proposed opportunities, or should they reconsider other opportunities? These decisions are frequently impacted by emotion and intuition as much as by technical analysis. Throughout the year, corporate executives routinely share information with analysts and stakeholders about their results to date. The information reflects their reactions to the results received to date as tempered by their expectations for the remainder of the year. This system of information collection, analysis, decisions and communication frequently lacks consistency and can be quite time consuming. This paper will illustrate how companies can use portfolio management techniques to improve the efficiency of their business plan monitoring and the quality of the information available to the decision process. We will show how portfolio management can be used to track corporate performance and continually reassess the remaining business plans throughout the year. As results are derived, the company can use portfolio processes to monitor the expected business results and the probability of meeting their goals. We will illustrate how portfolio management can help decision makers determine when to change plans and when to stay on the planned course.

Research of spatial decision support system based on business

Abstract: Because of the market competition and the complex socioeconomic environment, it is emphasized by economists and geographers that the decisions of business making should consider spatial factors. This paper introduces the conceptions and contents of spatial decision support system and business intelligence, then discusses the integration possibility of SDSS and BI, and analyzes the spatial influence factors and uses Huff model to express the attraction of business branches.

Automated economic data processing in the changing business world

Abstract: An attempt is made for exploring some strands of the automated economic data processing in the changing business world. The new realities in business management and automated economic data processing are outlined. Some strands for intellectual support in making business management decisions are revealed. Interesting are the challenges of e-business to automated economic data processing as well.