business intelligence and agile methodologies, asim abdel, 2012

8/21/2019 Business Intelligence and Agile Methodologies, Asim Abdel, 2012

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Asim Abdel Rahman El Sheikh

Arab Academy for Banking and Financial Sciences, Jordan

Mouhib Alnoukari

Arab International University, Syria

Business Intelligenceand Agile Methodologiesfor Knowledge-BasedOrganizations:

Cross-Disciplinary Applications


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Business intelligence and agile methodologies for knowledge-basedorganizations: cross-disciplinary applications / Asim Abdel Rahman El Sheikh and Mouhib Alnoukari, editors.

p. cm.Includes bibliographical references and index.

Summary: “This book highlights the marriage between business intelligence and knowledge management through the useof agile methodologies, offering perspectives on the integration between process modeling, agile methodologies, businessintelligence, knowledge management, and strategic management”--Provided by publisher.ISBN 978-1-61350-050-7 (hardcover) -- ISBN 978-1-61350-051-4 (ebook) -- ISBN 978-1-61350-052-1 (print & perpetual

access) 1. Business intelligence. 2. Knowledge management. I. El Sheikh, Asim Abdel Rahman. II. Alnoukari, Mouhib,1965-HD38.7.B8715 2012658.4’72--dc23

2011023040

British Cataloguing in Publication DataA Cataloguing in Publication record for this book is available from the British Library.

All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of theauthors, but not necessarily of the publisher.

Senior Editorial Director: Kristin Klinger Director of Book Publications: Julia MosemannEditorial Director: Lindsay JohnstonAcquisitions Editor: Erika Carter Development Editor: Myla HartyProduction Editor: Sean WoznickiTypesetters: Christen Croley, Adrienne FreelandPrint Coordinator: Jamie SnavelyCover Design: Nick Newcomer

Published in the United States of America byBusiness Science Reference (an imprint of IGI Global)701 E. Chocolate AvenueHershey PA 17033Tel: 717-533-8845Fax: 717-533-8661E-mail: [email protected] site: http://www.igi-global.com

Copyright © 2012 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed inany form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher.Product or company names used in this set are for identification purposes only. Inclusion of the names of the products orcompanies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark.

Library of Congress Cataloging-in-Publication Data


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Editorial Advisory Board

Basel Ojjeh, Yahoo, Inc., USABasel Solaiman ,Telecom, FranceFaek Diko, Arab International University, SyriaGhassan Kanaan, Arab Academy for Banking and Financial Sciences, JordanMoustafa Ghanem, Imperial College, UK Moutasem Shafaamry, International University for Sciences and Technology, SyriaRakan Razouk, Damascus University, SyriaRamez Hajislam, Arab International University, SyriaRiyad Al-Shalabi, Arab Academy for Banking and Financial Sciences, JordanSalah Dowaji, United Nations Development Programme (UNDP), Syria

List of Reviewers

Basel Ojjeh, Yahoo, Inc., USABasel Solaiman, Telecom, FranceDeanne Larson, Larson & Associates, LLC, USAEnric Mayol Sarroca, Technical University of Catalonia, SpainFaek Diko, Arab International University, SyriaGhassan Kanaan, Arab Academy for Banking and Financial Sciences, JordanJorge Fernández-González, Technical University of Catalonia, SpainMoustafa Ghanem, Imperial College, UK Moutasem Shafaamry, International University for Sciences and Technology, SyriaKinaz Aytouni, Arab International University, SyriaMartin Molhanec, Czech Technical University, Czech Republic

Rakan Razouk, Damascus University, SyriaRamez Hajislam, Arab International University, SyriaRiyad Al-Shalabi, Arab Academy for Banking and Financial Sciences, JordanSalah Dowaji, United Nations Development Programme (UNDP), SyriaSamir Hammami, International University for Sciences and Technology, SyriaZaidoun Alzoabi, Martin Luther University, Germany


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Table of Contents

Foreword ..............................................................................................................................................vii

Preface .................................................................................................................................................... x

Acknowledgment ................................................................................................................................. xv

Chapter 1

Business Intelligence: Body of Knowledge ............................................................................................ 1 Mouhib Alnoukari, Arab International University, Syria

Humam Alhammami Alhawasli, Arab Academy for Banking and Financial Sciences, Syria

Hatem Abd Alnafea, Arab Academy for Banking and Financial Sciences, Syria

Amjad Jalal Zamreek, Arab Academy for Banking and Financial Sciences, Syria

Chapter 2

Agile Software: Body of Knowledge .................................................................................................... 14 Zaidoun Alzoabi, Martin Luther University, Germany

Chapter 3

Knowledge Management in Agile Methods Context: What Type of Knowledgeis Used by Agilests? .............................................................................................................................. 35

Zaidoun Alzoabi, Martin Luther University, Germany

Chapter 4

Knowledge Discovery Process Models: From Traditional to Agile Modeling ..................................... 72 Mouhib Alnoukari, Arab International University, Syria

Asim El Sheikh, The Arab Academy for Banking and Financial Sciences, Jordan

Chapter 5

Agile Methodologies for Business Intelligence .................................................................................. 101 Deanne Larson, Larson & Associates, LLC, USA


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Chapter 6

BORM: Agile Modeling for Business Intelligence ............................................................................. 120 Martin Molhanec, Czech Technical University, Czech Republic

Vojtěch Merunka, Czech University of Life Sciences, Czech Republic

Chapter 7

Agile Approach to Business Intelligence as a Way to Success ........................................................... 132 J. Fernández, Technical University of Catalonia, Spain

E. Mayol, Technical University of Catalonia, Spain

J.A. Pastor, Universitat Oberta de Catalunya, Spain

Chapter 8

Enhancing BI Systems Application through the Integration of IT Governance and KnowledgeCapabilities of the Organization ......................................................................................................... 161

Samir Hammami, The International University for Sciences and Technology, Syria Firas Alkhaldi, King Saud University, Saudi Arabia

Chapter 9

ASD-BI: A Knowledge Discovery Process Modeling Based on Adaptive Software DevelopmentAgile Methodology ............................................................................................................................. 183

Mouhib Alnoukari, Arab International University, Syria

Chapter 10

Measurement of Brand Lift from a Display Advertising Campaign ................................................... 208 Jagdish Chand, Yahoo! Inc, USA

Chapter 11

Suggested Model for Business Intelligence in Higher Education ...................................................... 223 Zaidoun Alzoabi, Arab International University, Syria

Faek Diko, Arab International University, Syria

Saiid Hanna, Arab International University, Syria

Chapter 12

Business Intelligence and Agile Methodology for Risk Management in Knowledge-BasedOrganizations ......................................................................................................................................240

Muhammad Mazen Almustafa, The Arab Academy for Banking and Financial Sciences, Syria

Dania Alkhaldi, The Arab Academy for Banking and Financial Sciences, Syria

Chapter 13

Towards a Business Intelligence Governance Framework within E-Government System ................ 268 Kamal Atieh, Arab Academy for Banking and Financial Sciences, Syria

Elias Farzali, Arab Academy for Banking and Financial Sciences, Syria


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Chapter 14

Business Intelligence in Higher Education: An Ontological Approach .............................................. 285Constanta-Nicoleta Bodea, Academy of Economic Studies, Romania

Chapter 15

Web Engineering and Business Intelligence: Agile Web Engineering Development and Practice.........313 Haroon Altarawneh, Arab International University, Syria

Sattam Alamaro, Arab International University, Syria

Asim El Sheikh, Arab Academy for Banking and Financial Sciences, Jordan

About the Contributors .................................................................................................................... 345

Index ................................................................................................................................................... 351


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vii

Foreword

Business intelligence is made possible by the existence of Information Technology. Business intelligenceaims to support better business decision-making. To use agile methodologies and to develop InformationTechnology faster and cheaper is to put an icing on the cake. I doubt that Hans Peter Luhn was awareof the consequences of Business intelligence when he coined the term in 1958. Today, 50 years later,“Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disciplin-ary Applications” is coming out.

The book is comprised of fifteen chapters and is a collaboration work of 29 scholars from 8 differ-ent countries and 11 different research organizations. The end product is made possible by the use ofthe impossible ideas dreamt by visionaries, where the first two chapters discuss the body of knowledgeof both business intelligence & agile software, followed by chapter 3 and 4, which discuss knowledgemanagement and discovery in relation to agility essence. Subsequently, Business intelligence agilemethodologies, agile modeling, agile approach, and governance are discussed in chapters 5, 6, 7, and 8.Business intelligence & adaptive software development are covered in chapter 9, followed by chapter10, which covers yahoo experience in brand lifting. Throughout the next three chapters, the authorstackle issues like: risk management in business intelligence and agile methodology, business intelligencegovernance in e-government system, and business intelligence in higher education. Ultimately, the lastchapter discusses Web engineering and business intelligence.

The 1st chapter, Business Intelligence: Body of Knowledge, attempts to define Business Intelligence body of knowledge. The chapter starts with a historical overview of Business Intelligence stating itsdifferent stages and progressions. Then, the authors present an overview of what Business Intelligenceis, architecture, goals, and main components including: data mining, data warehousing, and data marts.Finally, the Business Intelligence ‘marriage’ with knowledge management is discussed in details.

The 2nd chapter entitled: Agile Software: Body of Knowledge. The chapter explains agile methodolo-gies, its general characteristics, and quick description of the famous agile methods known in the industryand research.

The 3rd chapter with the topic: Knowledge Management in Agile Methods Context: What Type of Knowledge is Used by Agilests? Provides an overview on the knowledge management techniques usedin different software development processes with focus on agile methods. Then tests the claim of moreinformal knowledge sharing, and see the mechanisms used to exchange and document knowledge.

The 4th chapter: Knowledge Discovery Process Models: From Traditional to Agile Modeling , pro-vides a detailed discussion on the Knowledge Discovery (KD) process models that have innovative lifecycle steps. The chapter proposes a categorization of the existing KD models. Furthermore, the chapter


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deeply analyzes the strengths and weaknesses of the leading KD process models, with the supportedcommercial systems and reported applications, and their matrix characteristics.

The 5th chapter Agile Methodologies for Business Intelligence explores the application of agile meth-

odologies and principles to business intelligence delivery. The practice of business intelligence deliverywith an Agile methodology has yet to be proved to the point of maturity and stability; the chapter outlinedAgile principles and practices that have emerged as best practices and formulate a framework to outlinehow an Agile methodology could be applied to business intelligence delivery.

Likewise, the 6th chapter has the title of: BORM: Agile Modeling for Business Intelligence, wherebyBORM (Business and Object Relation Modeling) method is described and presented through an appli-cation example created in Craft a CASE analysis and modeling tool. The chapter begins by introducingfundamental principles of BORM method. Then the chapter goes on to highlights most important con-cepts of BORM. In order to further enhance the understanding of BROM, the chapter applies BROMon a simple, descriptive example. .

The 7th chapter entitled: Agile Approach to Business Intelligence as a Way to Success presents an

overview of several methodological approaches used in Business Intelligence (BI) projects, as well asData Warehouse projects. In this chapter, the authors show that there is a strong relationship betweenthe so-called Critical Success Factors of BI projects and the Agile Principles. As such, with basis onsound analysis, the authors conclude that successful BI methodologies must follow an agile approach.

In this context, the 8th chapter, with the title: Enhancing BI Systems Application through the Integrationof IT Governance and Knowledge Capabilities of the Organization, cites a study reports the results of anempirical examination of the effect of IT governance framework based on COBIT and OrganizationalKnowledge Pillars in enhancing the IT Governance framework (Business / IT Strategic alignment, Busi-ness value delivery, risk management, Resource management, performance measurement) to improve theBusiness Intelligence Application and Usability within the organization. Quantitative method is adoptedfor answering the research questions.

The 9th chapter: ASD-BI: A Knowledge Discovery Process Modeling Based on Adaptive Software Development Agile Methodology proposes a new knowledge discovery process model named “ASD-BI”that is based on Adaptive Software Development (ASD) agile methodology. ASD-BI process model was proposed to enhance the way of building Business Intelligence and Data Mining applications.

While the 10th chapter: Measurement of Brand Lift from a Display Advertising Campaign, describes anAdvanced Business Intelligence System have been built at Yahoo! to measure the lift in brand awarenessdriven from the display advertising campaigns on Yahoo network. It helped us to show to the advertisersthat display advertising is working in lifting awareness and brand affinity.

Whereas, the 11th chapter entitled: Suggested Model for Business Intelligence in Higher Education,describes a data mining approach as one of the business intelligence methodologies for possible use inhigher education. The importance of this model arises from the fact that it starts from a system approach

to the university management, looking at the university as input, processing, output, and feedback, andthen applies different business intelligence tools and methods to every part of the system in order toenhance the business decision making process.

The 12th chapter: Business Intelligence and Agile Methodology for Risk Management in Knowledge- Based Organizations, discusses and explores the role of Business Intelligence and Agile methodologyin managing risk effectively and efficiently. It explores the risk management traditional tools that arecommonly used, the role of Business Intelligence in risk management, and the role of agile methodol-ogy in risk management.


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The 13th chapter: Towards a Business Intelligence Governance Framework within E-GovernmentSystem, will take E-Government project in Syria as case study to explore, empirically, the main barriersof E-Government project in developing countries; how to take benefits from business intelligence (BI)

to build a framework, which could be adopted by developing countries in their E-Government projects.In the same context, the 14 th chapter: Business Intelligence in Higher Education – an Ontological Ap-

proach, presents an ontology-based knowledge management system developed for a Romanian university.The starting point for the development knowledge management system is the classic Information Man-agement System (IMS), which is used for the education & training and research portfolio management. .

In conclusion, the last chapter entitled Web Engineering and Business Intelligence: Agile Web Engi-neering Development and Practice highlights the main issues related to Web engineering practices andhow they support business intelligence projects, the need for Web engineering, and the developmentmethods used in Web engineering. Web Engineering is a response to the early, chaotic development ofWeb sites and applications as well as recognition of the deference between web developers and con-ventional software developers. Viewed broadly, Web Engineering is both a conscious and pro-active

approach and a growing collection of theoretical and empirical researches.

Evon M. O. Abu-Taieh

International Journal of Aviation Technology, Engineering and Management (IJATEM)

Evon M. O. Abu-Taieh currently manages the SDI/GIS World Bank project in Jordan and lectures in AIU, after serving for 3

years as Economic Commissioner for Air Transport in the Civil Aviation Regulatory Commission-Jordan. She has a PhD in

simulation and is a USA graduate for both her Master of Science and Bachelor’s degrees with a total experience of 21 years.

Dr. Abu-Taieh is an author of many renowned research papers in the airline, IT, PM, KM, GIS, AI, simulation, security, and

ciphering. She is the editor/author of Utilizing Information Technology Systems across Disciplines: Advancements in the

Application of Computer Science, Handbook of Research on Discrete Event Simulation Environments: Technologies and Ap- plications, and Simulation and Modeling: Current Technologies and Applications. She is Editor-in-Chief of the InternationalJournal of Aviation Technology, and Engineering and Management and has been a guest editor for the Journal of InformationTechnology Research. Dr. Abu-Taieh holds positions on the editorial board of the International Journal of E-Services and MobileApplications, International Journal of Information Technology Project Management, and International Journal of InformationSystems and Social Change. In her capacity as head of IT department in the ministry of transport for 10 years, she developed

systems such as ministry of transport databank, auditing system for airline reservation systems, and maritime databank, among

others. Furthermore, she has worked in the Arab Academy as an Assistant Professor, a Dean’s Assistant, and London School

of Economics (LSE) Program Director in AABFS. She has been appointed many times as track chair and reviewer in many

international conferences: IRMA, CISTA, WMSCI, and Chaired AITEM2010.


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x

Preface

More than 2300 years ago Aristotle said that:” All men by nature desire knowledge”. No doubt Aristo-tle was right because until now with all advanced sciences that we have today in the 21st century human beings are still looking for knowledge.

Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disci-

plinary Applications is one of the first essays that highlight the “marriage” between business intelligenceand knowledge management through the use of agile methodologies.

In 1996, the Chinese Organization for Economic Cooperation and Development (OECD) redefined“knowledge-based economies” as: economies which are directly based on the production, distribu-tion and use of knowledge and information. According to the definition, data mining and knowledgemanagement, and more generally Business Intelligence (BI), should be the foundations for building theknowledge economy.

Business Intelligence applications are of vital importance for many organizations and can make thedifference in any organization. You can collect, clean and integrate all your data, you can also, analyze,mine and dig more into your data, and you can make right decision, at the right time by using BI dash- boards, alerts and reports.

Business Intelligence can also help organizations managing, developing and communicating theirintangible assets such as information and knowledge. Thus, it can be considered as an imperative frame-work in the current knowledge-based economy arena. Organizations such as Continental Airlines haveinvested in Business Intelligence generate increases in revenue and cost saving equivalent to 1000%return on investment (ROI).

Business Intelligence can be also considered as a strategic framework, as it is becoming increasinglyimportant in strategic management, and in supporting business strategies. IT-enabled strategic manage-ment addresses the business intelligence role in strategy formulation and implementation processes.Drucker, the pioneer of “management by objectives”, was one of the first who recognized the dramaticchanges IT brought to management.

However, Business Intelligence applications still face failures in determining the process modeladopted. As the world becomes increasingly dynamic, the traditional static modeling may not be able todeal with it. Traditional process modeling requires a lot of documentation and reports. This makes tradi-tional methodology unable to fulfill dynamic requirement changes in our rapidly changing environment.

One solution is to use agile modeling that is characterized by flexibility and adaptability. On theother hand, Business Intelligence applications require greater diversity of technology, business skills,and knowledge than the typical applications, which means it may benefit a lot from features of agilesoftware development.


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To successfully implement Business Intelligence applications in our agile era, different areas should be examined in addition to considering the transition into knowledge-based economy. The areas to beexamined in this book are: methodologies, architecture, components, technologies, agility, adaptability,

tools, strategies, applications, knowledge and history.In Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-

Disciplinary Applications, Business Intelligence is discussed from a new point of view, as it will tackle,and for the first time, the agility character of Business Intelligence applications. This book highlights,through its fifteen chapters, the integration between: process modeling, agile methodologies, businessintelligence, knowledge management, and strategic management.

Now, the main question is: why our book will create added value in the field? Our response is:

• Most organizations are using business intelligence and data mining applications to enhance stra-tegic decision making and knowledge creation and sharing.

• Data mining is at the core of business intelligence and knowledge discovery.

• Most of current business intelligence applications are unable to fulll the dynamic requirementchanges in our complex environment.

• Finally, knowledge is the result of intelligence and agility…

Though, the overall objectives of this book are: to provide a comprehensive view of business intel-ligence and agile methodologies, to provide cutting edge research on applying agile methodologies on business intelligence applications by leading scholars and practitioners in the field, to provide a deepanalysis for the relationship between business intelligence, agile methodologies and knowledge manage-ment, and to demonstrate the previous objectives through both theory and practice.

The book caters the needs of scholars, PhD candidates, researchers, as well as graduate level studentsof computer science, Information Science, Information Technology, operations research, business and

economics disciplines. The target audience of this book is academic libraries throughout the world thatare interested in cutting edge research on business intelligence, agile methodologies, and knowledgemanagement. Another important market is Master of Business Administration (MBA), Master of Execu-tive Business Administration (EMBA), and Master of E-Business programs which have InformationSystems components as part of their curriculum.

The book encompasses 15 chapters. On the whole, the chapters of this book fall into six categories,while crossing paths with different disciplines. The 1st category, business intelligence, concentrates on business intelligence theories, tools, architecture, and applications. The 2nd category, agile methodologies, concentrates on agile theories, methods, and characteristics, while the 3rd concentrates on knowl-edge management in agile methods context , whereas the 4th concentrates on knowledge discovery and business intelligence process modeling , surveying all the used processes used from traditional till agile

methodologies, The 5th category tackle the main focus of this book, the use of agile methodologies forbusiness intelligence. This category was highlighted by more than six chapters. The last and 6th categorydiscusses the application of agile methodologies and business intelligence in different areas including:higher education, e-government, public regional management systems, risk management, e-marketing,IT governance, and web engineering.

Chapter 1, Business Intelligence: Body of Knowledge, provides an overview of the business intelli-gence history, definitions, architecture, goals, and components including: data mining, data warehousing,


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and data marts. It also highlights the close relationship between business intelligence and knowledgemanagement.

Chapter 2, Agile Software: Body of Knowledge, provides an overview of the agile methodology his-

tory, principles, techniques, characteristics, and methods. The chapter explains in details the main agilemethods including: eXtreme Programming (XP), Scrum, Crystal, Feature-Driven Development (FDD),Adaptive Software Development (ASD), and DSDM. For each agile method, the author explains itslifecycle, its principles and techniques, and its roles and responsibilities.

Chapter 3, Knowledge Management in Agile Methods Context: What Type of Knowledge is Used by Agilests? provides an overview on the knowledge management techniques used in different softwaredevelopment processes with focus on agile methods. In this chapter, the author has demonstrated theresults of email-based panel of experts’ survey. The survey was published in July 2008 on Scott Ambler’swebsite www.ambysoft.com. More than 300 agile practitioners was asked about the mechanisms usedto exchange and document knowledge and in which context every mechanism is applied

Chapter 4, Knowledge Discovery Process Models: From Traditional to Agile Modeling , provides a

detailed discussion on the Knowledge Discovery (KD) process models that have innovative life cyclesteps. It proposes a categorization of the existing KD models. The chapter deeply analyzes the strengthsand weaknesses of the leading KD process models, with the supported commercial systems and reportedapplications, and their matrix characteristics.

Chapter 5, Agile Methodologies for Business Intelligence, explores the application of agile methodolo-gies and principles to business intelligence delivery. The practice of business intelligence delivery withan agile methodology has yet to be proven to the point of maturity and stability; this chapter outlinesagile principles and practices that have emerged as best practices and formulate a framework to outlinehow an agile methodology could be applied to business intelligence delivery.

Chapter 6, BORM: Agile Modeling for Business Intelligence, proposes a new business intelligencemodel based on agile modeling. The proposed model named BORM (Business and Object Relation

Modeling) is described in details by explaining its fundamental principles and its most important con-cepts. The chapter will then explore the three areas of BORM modeling in Model-Driven Approach(MDA) perspective. The chapter will also describe the business model, scenarios, and diagram. Finally,the model validation will be explained using one of the recent BORM applications of organizationalmodeling and simulation. The aim of the project is the improvement of decision-making on the levelof mayors and local administrations. It offers the possibility to model and simulate real life situationsin small settlements.

Chapter 7, Agile Approach to Business Intelligence as a Way to Success, presents an overview ofseveral methodological approaches used in business intelligence and data warehousing projects. In thischapter, the authors have presented and analyzed the Critical Success Factors of Business Intelligence projects. On the other side, the authors have collected all Agile Principles that guide Agile development

methodologies. Finally they have analysed the relationships between these two sources, respectivelyBI success factors and agile principles, to evaluate how adequate may be to use an Agile Approachto manage Business Intelligence projects. As a result, the authors show a strong relationship betweenthe so-called Critical Success Factors for BI projects and the Agile Principles. Hence, based on soundanalysis, concluding that successful BI methodologies must follow an agile approach.

Chapter 8, Enhancing BI Systems Application through the Integration of IT Governance and Knowl-edge Capabilities of the Organization, reports the results of an empirical examination of the effect ofIT governance framework based on COBIT and Organizational Knowledge Pillars in enhancing the IT


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Governance framework (Business / IT Strategic alignment, Business value delivery, risk management,Resource management, performance measurement) to enhance the Business Intelligence Application andUsability within the organization. Quantitative method is adopted for answering the research questions.

Using confirmatory factor analysis techniques, the effects of the combination between IT governancefactors seen by ITGI and organizational knowledge pillars of the firm on BI Systems application in itwere tested and confirmed and the models were verified.

Chapter 9, ASD-BI: A Knowledge Discovery Process Modeling Based on Adaptive Software De-velopment Agile Methodology, proposes a new knowledge discovery process model named “ASD-BI”that is based on Adaptive Software Development (ASD) agile methodology. ASD-BI process modelwas proposed to enhance the way of building Business Intelligence and Data Mining applications. Themain contribution of this chapter is the demonstration that ASD-BI is adaptive to environment changes,enhances knowledge capturing and sharing, and helps in implementing and achieving organization’sstrategy. ASD-BI process model will be validated by using a case study on higher education.

Chapter 10, Measurement of Brand Lift from a Display Advertising Campaign, describes an advanced

Business Intelligence System; built at Yahoo to measure the lift in brand awareness driven from thedisplay advertising campaigns on Yahoo network. The author describes the methodology to measure thelift in Brand Awareness from a Display Ad campaign and a system to compute this metric. This systemis a great help to any sales team, when they are working with advertisers to show them the value of theirmarketing investments and want to get bigger return business.

Chapter 11, Suggested Model for Business Intelligence in Higher Education, describes a data min-ing approach as one of the business intelligence core components for possible use in higher education.The importance of the model arises from the reality that it starts from a system approach to universitymanagement, looking at the university as input, processing, output, and feedback, and then applies dif-ferent business intelligence tools and methods to every part of the system in order to enhance the busi-ness decision making process. The suggested model was validated using a real case study at the Arab

International University.Chapter 12, Business Intelligence and Agile Methodology for Risk Management in Knowledge-Based

Organizations, discusses and explores the role of Business Intelligence and Agile methodology to managerisks effectively and efficiently. The authors describe, highlight and investigate the different techniquesand tools that are mostly used in Risk Management giving the focus for the Business Intelligence basedon providing examples on some of the mostly used tools. The authors also shed lights on the role ofagile in managing risk in this knowledge based economy.

Chapter 13, Towards a Business Intelligence Governance Framework within E-Government System, presents a BI governance framework within E-Government system derived from an empirical studywith academics and experts from public and private sector. An analysis of the findings demonstratedthat the business/IT alignment is very important to E-Government success and the important role of BI

use in E-Government system.Chapter 14, Business Intelligence in Higher Education: An Ontological Approach, presents an ontol-

ogy-based knowledge management system developed for a Romanian university. The ontologies wereimplemented using Protege. The results are very encouraging and suggest several future developments.

Chapter 15, Web Engineering and Business Intelligence: Agile Web Engineering Development and Practice, highlights the main issues related to Web engineering practices and how they support businessintelligence projects. It also explains the need for Web engineering, and the development methods usedin Web engineering.


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In conclusion, the book is one of the first attempts to highlight the importance of using agile meth-odologies for business intelligence applications. Although, the research direction is new, the book’schapters raise very important research results in different areas. The editors are proud of the book’s

research methodologies and the high level of work provided.



Mouhib Alnoukari



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Acknowledgment

The editors would like to acknowledge the relentless support of the IGI team, for their significant help.Moreover, the authors would like to extend their gratitude to Mrs. Jan Travers, Director of IntellectualProperty and Contracts at IGI Global. Likewise, the authors would like to extend their gratitude to theDevelopment Division at IGI Global; namely Myla Harty, editorial assistant.

In this regard, the authors also express their recognition to their respective organizations and col-leagues for their moral and continuous support. By the same token, the editors would like to thankthe reviewers for their great work and their valuable notes and evaluations. Special thanks would beforwarded to Dr. Salah Dowaji, Dr. Zaidoun Alzoabi, and Dr. Ramez Hajislam for their hard work, andtheir constant demand for perfection.

Finally, the editors would like to thank Mrs. Sara Al-Ahmad for all the time she spent spell checkingimportant parts of this book.



Mouhib Alnoukari



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Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Chapter 1

DOI: 10.4018/978-1-61350-050-7.ch001

INTRODUCTION

Business Intelligence is becoming an important ITframework that can help organizations managing,developing and communicating their intangibleassets such as information and knowledge. Thus,it can be considered as an imperative frameworkin the current knowledge-based economy era.

Business Intelligence applications are mainlycharacterized by flexibility and adaptability in

which traditional applications are not able to dealwith. Traditional process modeling requires alot of documentation and reports and this makestraditional methodology unable to fulfill the dy-namic requirements of changes of our high-speed,high-change environment (Gersten, Wirth, andArndt, 2000).

Mouhib Alnoukari Arab International University, Syria

Humam Alhammami Alhawasli Arab Academy for Banking and Financial Sciences, Syria

Hatem Abd Alnafea Arab Academy for Banking and Financial Sciences, Syria

Amjad Jalal Zamreek Arab Academy for Banking and Financial Sciences, Syria

Business Intelligence:Body of Knowledge

ABSTRACT

This chapter attempts to dene the knowledge body of Business Intelligence. It provides an overview of the

context we have been working in. The chapter starts with a historical overview of Business Intelligence

stating its different stages and progressions. Then, the authors present an overview of what Business

Intelligence is, its architecture and goals, and its main components including: data mining, data ware-

housing, and data marts. Finally, the Business Intelligence ‘marriage’ with knowledge management is

discussed in details. The authors hope to contribute to the recent discussions about Business Intelligence

goals, concepts, architecture, and components.


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Business Intelligence

An important question raised by many re-searchers (Power, 2007; Shariat & Hightower,2007) as to what was the main reason pushing

company to search for BI solutions, and whatdifferentiates BI from Decision Support System(DSS) systems? In fact, over the last decades,organizations developed a lot of OperationalInformation Systems (OIS), resulting in a hugeamount of disparate data that are located in dif-ferent geographic locations, on different storage platforms, with different forms. This situation prevents organization from building a common,integrated, correlated, and immediate access toinformation at its global level. DSS have been

evolved during the 1970s, with the objective of providing organization’s decision makers withthe required data to support decision-making process. In the 1980s, Executive InformationSystem (EIS) was evolved to provide executiveofficers with the information needed to supportstrategic decision-making process. in 1990s BIwas created as data-driven DSS, sharing some ofthe objectives and tools of DSS and EIS systems.

BI architectures include data warehousing, business analytics, business performance man-agement, and data mining. Most of BI solutionsare dealing with structured data (Alnoukari, andAlhussan, 2008). However, many applicationdomains require the use of unstructured data (or atleast semi-structured data), e.g. customer e-mails,web pages, competitor information, sales reports,research paper repositories, and so on (Baars, andKemper, 2007).

Any BI solution can be divided into the fol-lowing three layers (Alnoukari, and Alhussan,2008): data layer, which is responsible for storingstructured and unstructured data for decision support purposes. Structured data is usually stored inOperational Data Stores (ODS), Data Warehouses(DW), and Data Marts (DM) while unstructureddata are handled by using Content and Docu-ment Management Systems. Data are extractedfrom operational data sources, e.g. SCM, ERP,CRM, or from external data sources, e.g. market

research data. Data extracted from data sourcesare then transformed and loaded into DW usingETL tools. The second layer is the analytical layer

which provides functionality in order to analyzedata and provide knowledge including OLAP anddata mining. The third layer is the visualizationlayer which can be realized using some sort ofsoftware portals (BI portal).

Our main focus in this chapter is to provide anoverview of Business Intelligence by focusing onits body of knowledge. The authors start by provid-ing a historical overview of Business Intelligenceexplaining the evolution of its concepts, followed by a brief discussion about different definitions and

concepts of this field. The authors will describethe different layers and components of BusinessIntelligence application. Finally, the core body ofknowledge, and the marriage between BusinessIntelligence and Knowledge Management will bediscussed in details.

HISTORICAL OVERVIEW

In his article “A Business Intelligence System.”Which have been published in IBM Journal, Luhnhad defined intelligence as: “the ability to appre-hend the interrelationships of presented facts insuch a way as to guide action towards a desiredgoal.”, (Luhn, 1958).

Business Intelligence is considered as a resultof Decision Support Systems progression (DSS).DSS was mainly evolved in the 1970s. Model-driven DSS was the first DSS models that uselimited data and parameters to help decision mak-ers analyzing a situation (Power, 2007).

Data-driven DSS was also introduced as a newDSS direction by the end of the 1970s. It focusedmore on using all available data (including histori-cal data) to provide executives with more insightsabout their organization’s current and future situ-ation. Executive Information Systems (EIS) andExecutive Decision Support (ESS) are examplesof data-derived DSS (Power, 2007).


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In the late of 1980s, the client/server era hashelped BI concept to evolve specially when Busi-ness Process Reengineering became the main

trend of the industry, and the implementations ofrelational technologies – especially SQL skills -were transported between systems (Biere, 2003).During this period, the new idea of informationwarehousing was raised. Although the conceptitself was brilliant, the data was never convertedinto clear information, the idea was simply toleave the data as it was and where it was but tohave an access to it from anywhere using the earlyBusiness Intelligence tools.

In the 1990s, after the information warehous-

ing quickly vanished, the data warehousing eratakeover. This era introduced a way to not onlyreorganize data but to transform it into a muchcleaner and easier to follow form. Data Ware-housing is actually a set of processes designedto extract, clean, and reorganize data, enablingusers to get a clearer idea of exactly what kindof data they are dealing with and its relevance tothe issue they are addressing.

In this era, DSS was pushed notably by theintroduction of Data Warehousing (DW) andOn-Line analytical Processing (OLAP) which provide a new category of data-driven DSS. OLAPtools provide users with the way to browse andsummarize data in an efficient and dynamic way(Shariat, and Hightower, 2007). In other word,OLAP tools provide an aggregated approach toanalyze large amount of data (Hofmann, 2003).Data Warehousing is mainly composed of twocomponents, data repository, or data warehouse,and metadata. Data warehouse is a logical col-lection of integrated data gathered from variousoperational data sources. Metadata is a set of rulesthat guide all data preparation operations (Shariat,and Hightower, 2007).

In the year 1989, Howard Dresner, the memberof the Gartner group, was the first who introducedthe term “Business Intelligence”(BI) as an umbrella term that “describe a set of concepts and

methods to improve business decision making byusing fact-based support systems” (Power, 2007).

Taking common BI concepts with data ware-

house technologies, well developed enterpriseapplication tools and on line analytical processing(OLAP) assists in faster collection, analysis or dataresearch (Flanglin, 2005). Hence, BI technologyassists in extracting information from the availabledata and using them as knowledge in developinginnovative business strategies. But the growingcompetition in market is forcing small to largeorganizations to adopt BI to understand economictrends and have an in depth knowledge about theoperation of a business.

Those years has considered a new era for BI,where packaged Business Intelligence solutionsare provided on demand. Golfarelli had described anew approach of BI called “Business PerformanceManagement (BPM)” which “requires a reactivecomponent capable of monitoring the time-criticaloperational processes to allow tactical and op-erational decision-makers to tune their actionsaccording to the company strategy”, (Golfarelli,Stefano, and Iuris, 2004).

Colin in her paper ” The Next Generation ofBusiness Intelligence: Operational BI” describesthe term “Operational BI”, that is used to reactfaster to business needs and to anticipate business problems in advance before they become majorissues, (Colin, 2005).

Similarly, many researchers were talking aboutthe term “Real-time Business Intelligence” whichhas a very close relationship with the OperationalBI, and targeting to reach the almost real-timedecision making and a much higher degrees ofanalytics involved within business intelligence(Azvine, Cui, and Nauck, 2005).

Many other concepts had appeared in manyareas: Ad-hoc and Collaborative BI (Berthold, etal., 2010), BI networks, Portals and thinner clients(Biere, 2003).


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BUSINESS INTELLIGENCE:CONCEPTS AND DEFINTIONS

Decision support is aimed at supporting managerstaking the right decisions (Jermol, Lavrac, andUrbancic, 2003). It provides a wide selectionof decision analysis, simulation and modelingtechniques, which include decision trees and belief networks. Also, decision support involvessoftware tools such as Decision Support Systems(DSS), Group Decision Support and MediationSystems (GDSMS), Expert Systems (ES), andBusiness Intelligence (BI) (Negash, 2004).

Decision makers depend on accurate informa-

tion when they have to make decisions. BusinessIntelligence can provide decision makers withsuch accurate information, and with the appropri-ate tools for data analysis (Jermol, Lavrac, andUrbancic, 2003; Negash, 2004). It is the processof transforming various types of business datainto meaningful information that can help, deci-sion makers at all levels, getting deeper insight of business (Power, 2007; Girija, and Srivatsa, 2006).

In 1996, the Organization for Economic Co-operation and Development (OECD) redefined“knowledge-based economies” as: “Economieswhich are directly based on the production, dis-tribution and use of knowledge and information”(Weiss, Buckley, Kapoor, and Damgaard, 2003).

According to the definition, Data Mining andKnowledge Management, and more generallyBusiness Intelligence (BI), should be the founda-tions for building the knowledge economy.

BI is becoming vital for many organizations,especially those have extremely large amount ofdata (Shariat, and Hightower, 2007). Organizationssuch as Continental Airlines have seen invest-ment in Business Intelligence generate increasesin revenue and cost saving equivalent to 1000%return on investment (ROI) (Watson, Wixom,Hoffer, Anderson-Lehman, and Reynolds, 2006).

Business Intelligence is becoming an impor-tant IT framework that can help organizationsmanaging, developing and communicating their

intangible assets such as information and knowl-edge. Thus it can be considered as an impera-tive framework in the current knowledge-based

economy era.BI is an umbrella term that combines archi-

tectures, tools, data bases, applications, practices,and methodologies (Turban, Aronson, Liang,and Sharda, 2007; Cody, Kreulen, Krishna, andSpangler, 2002).

Weiss defined BI as the: “Combination of datamining, data warehousing, knowledge manage-ment, and traditional decision support systems”(Weiss, Buckley, Kapoor, and Damgaard, 2003).

According to Stevan Dedijer (the father of

BI), Knowledge management emerged in partfrom the thinking of the “intelligence approach”to business. Dedijer thinks that “Intelligence” ismore descriptive than knowledge. “Knowledge isstatic, intelligence is dynamic” (Marren, 2004).

For the purpose of this dissertation the follow-ing definition of BI applies: “The use of all the or-ganization’s resources: data, applications, peopleand processes in order to increase its knowledge,implement and achieve its strategy, and adapt tothe environment’s dynamism” (Authors).

THE GOAL OF BUSINESSINTELLIGENCE

The goal for any BI solution is to access datafrom multiple sources, transform these data intoinformation and then into knowledge. The mainfocus of any BI solution is to improve organiza-tion’s decision making capabilities. This can bedone using the knowledge discovered from thedata mining phase for the purpose to supportdecision makers by explaining current behavior,or predicting future results (Kerdprasop, andKerdprasop, 2007).

The main complex part in any BI system isin its intelligence ability. This is mainly found inthe post data mining phase where the system hasto interpret its data mining results using a visual


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environment. The measure of any business intel-ligence solution is its ability to derive knowledgefrom data. The challenge is to meet the ability of

identifying patterns, trends, rules, and relation-ships from large amount of information which istoo large to be processed by human analysis alone.

BUSINESS INTELLIGENCEARCHITECTURE

Any Business Intelligence application can bedivided into the following three layers (Azvine,Cui, and Nauck, 2005; Baars, and Kemper, 2007;

Shariat, and Hightower, 2007):

1. Data layer: responsible for storing struc-tured and unstructured data for decisionsupport purposes. Structured data is usuallystored in Operational Data Stores (ODS),Data Warehouses (DW), and Data Marts(DM). Unstructured data are handled byusing Content and Document ManagementSystems. Data are extracted from operationaldata sources, e.g. SCM, ERP, CRM, or fromexternal data sources, e.g. market researchdata. Data are extracted from data sourcesthat are transformed and loaded into DW byETL tools.

2. Analytics layer: provides functionality toanalyze data and provide knowledge. Thisincludes OLAP, data mining, aggregations,etc.

3. Visualization layer: realized by some sortof BI applications or portals.

Data Warehouse and Data Mart

During the last two decades, data warehouses havegained a great reputation as a part of any decisionsupport systems. Data warehouse came as a resultof the failure of the mainframe systems to supportenterprise decision making, those systems clus-tered the business entities across many production

databases, aiming to enhance the performancelevel, but due to nature of the complex quires,the load generated create the need to separate the

operational data from the data required to generatethe DSS reports.

Ralph Kimball has defined the data warehouseas “A copy of transaction data, specifically struc-tured for query and analysis” (Kimball, 2002).Barry Devlin defined it as: “A data warehouse isa simple, complete and consistent store of dataobtained from a variety of sources and made avail-able to users in a way they can understand anduse it in a business context” (Devlin, 1997). BillInmon (the father of the data warehouse) defined

data warehouse as: “a collection of integrated,subject-oriented databases designed to support theDSS (Decision Support Systems) function, whereeach unit of data is relevant to some moment intime. The data warehouse contains atomic dataand lightly summarized data…” (Inmon, 2005).

Data marts were viewed as limited alternativesto fully populated enterprise data warehouses.Today, data marts have surged in popularity.Frequently, they serve as more manageable, cost-effective stepping-stones to the data warehouse. Adata mart is a collection of subject areas organizedfor decision support based on the needs of a givendepartment. Inmon defines Data Mart as follows:“a departmentalized structure of data feeding fromthe data warehouse where data is de-normalised based on the department’s need for information”(Inmon, 2005).

The union of business process data marts isnot a data warehouse, as Ralph Kimball and hiscollaborators suggest because this union doesn’tnecessarily provide management decision supportfor departments, or for departmental interactionsamong themselves and with the external world.(Kimball, Reeves, Ross, and Thornthwaite, 1998).

Data warehousing, in practice, focuses on asingle large server or mainframe that providesa consolidation point for enterprise data comingfrom diverse production systems. It protects data production sources and gathers data into a single


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unified data model, but does not necessarily fo-cus on providing end-user with an access to thatdata. Conversely data mart ignores the practical

difficulties of protecting production systems fromthe impact of extraction. Instead it focuses on theknowledge needed from one or more areas of the business.

Data Mining

It is noted that the number of databases keepsgrowing rapidly because of the availability of pow-erful and affordable database systems. Millionsof databases have been used in business manage-

ment, government administration, scientific andengineering data management, and many otherapplications. This explosive growth in data anddatabases has generated an urgent need for newtechniques and tools that can intelligently andautomatically transform the processed data intouseful information and knowledge, which provideenterprises with a competitive advantage, work-ing asset that delivers new revenue, and to enablethem to better service and retain their customers(Stolba, and Tjoa, 2006).

Data mining is the search for relationshipsand distinct patterns that exist in datasets butthey are “hidden” among the vast amount of data(Jermol, Lavrac, and Urbancic, 2003; Turban,Aronson, Liang, & Sharda, 2007). Data miningcan be effectively applied to many areas (Al-noukari, and Alhussan, 2008; Watson, Wixom,Hoffer, Anderson-Lehman, and Reynolds, 2006)including: marketing (direct mail, cross-selling,customer acquisition and retention), fraud detec-tion, financial services (Srivastava, and Cooley,2003), inventory control, fault diagnosis, creditscoring (Shi, Peng, Kou, and Chen, 2005), networkmanagement, scheduling, medical diagnosis and prognosis. There are two main sets of tools usedfor data mining (Corbitt, 2003; Baars & Kemper,2007): discovery tools (Wixom, 2004; Chung,Chen, and Nunamaker jr, 2005), and verificationtools (Grigori, Casati, Castellanos, Dayal, Sayal,

and Shan, 2004). Discovery tools include datavisualization, neural networks, cluster analysisand factor analysis. Verification tools include

regression analysis, correlations, and predictions.Data mining application are characterized

by the ability to deal with the explosion of busi-ness data and accelerated market changes, thesecharacteristics help providing powerful toolsfor decision makers, such tools can be used by business users (not only statisticians) for analyz-ing huge amount of data for patterns and trends.Consequently, data mining has become a researcharea with increasing importance and it involved indetermining useful patterns from collected data or

determining a model that fits best on the collecteddata (Fayyad, Piatetsky-Shapiro, and Smyth, 1996;Mannila, 1997; Okuhara, Ishii, and Uchida, 2005).Different classification schemes can be used tocategorize data mining methods and systems basedon the kinds of databases to be studied, the kindsof knowledge to be discovered, and the kinds oftechniques to be utilized (Lange, 2006).

A data mining task includes pre-processing, theactual data mining process and post-processing.During the pre-processing stage, the data mining problem and all sources of data are identified, anda subset of data is generated from the accumulateddata. To ensure quality the data set is processedto remove noise, handle missing information andtransformed it to an appropriate format (Nayak,and Qiu, 2005). A data mining technique or acombination of techniques appropriate for the typeof knowledge to be discovered is applied to thederived data set. The last stage is post-processingin which the discovered knowledge is evaluatedand interpreted.

The most widely used methodology whenapplying data mining processes is named CRISP-DM. It was one of the first attempts towards stan-dardizing data mining process modeling (Shearer,2000). CRISP-DM has six main phases, starting by business understanding that can help in convert-ing the knowledge about the project objectivesand requirements into a data mining problem


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definition, followed by data understanding by performing different activities such as initial datacollection, identifying data quality problems, and

other preliminary activities that can help users be familiar with the data. The next and the mostimportant step is data preparation by performingdifferent activities to convert the initial raw datainto data that can be fed into modeling phase. This phase includes tasks such as data cleansing anddata transformation. Modeling is the core phasewhich can use a number of algorithmic techniques(decision trees, rule learning, neural networks,linear/logistic regression, association learning,instance-based/nearest-neighbor learning, unsu-

pervised learning, and probabilistic learning, etc.)available for each data mining approach, withfeatures that must be weighed against data char-acteristics and additional business requirements.The final two modules focus on the evaluation ofmodule results, and the deployment of the modelsinto production. Hence, users must decide onwhat and how they wish to disseminate/deployresults, and how they integrate data mining intotheir overall business strategy (Shearer, 2000).

THE KNOWLEDGE DIMENSIONOF BUSINESS INTELLIGENCE

Knowledge was defined as “justified true belief”(Nonaka, 1994), which is subjective, difficult tocodify, context-related, rooted in action, relational,and is about meaning. Knowledge differs frominformation as the later is objective and codifiedin any explicit forms such as documents, computerdatabases, and images.

Knowledge is usually identified to have twotypes: tacit and explicit (Nonaka, and Takeuchi,1995). Tacit knowledge is personal, context-specific, and resides in human beings minds, and istherefore difficult to formalize, codify and commu-nicate. It is personal knowledge that is embeddedin individual experience and involves intangiblefactors such as personal belief, perspective, and

value system. Tacit knowledge is difficult to com-municate and share in the organization and mustthus be converted into words or forms of explicit

knowledge. On the other hand explicit knowledgeis the knowledge that is transmittable in formal,systematic languages. It can be articulated informal languages, including grammatical state-ments, mathematical expressions, specifications,manuals and so forth. It can be transmitted acrossindividuals formally and easily.

Knapp defined Knowledge Management (KM)as “the process of making complete use of the valuegenerated by the transfer of intellectual capital,where this value can be viewed as knowledge

creation, acquisition, application and sharing”,(Knapp, 1998).

Business Intelligence is a good environmentin which ‘marrying’ business knowledge withdata mining could provide better results (Anand,Bell, and Hughes, 1995; Cody, Kreulen, Krishna,and Spangler, 2002; Weiss, Buckley, Kapoor, andDamgaard, 2003; Graco, Semenova, and Du- bossarsky, 2007). They all agree that knowledgecan enrich data by making it “intelligent”, thusmore manageable by data mining. They considerexpert knowledge as an asset that can provide datamining with the guidance to the discovery process.Thus, it says in a simple word, “data mining cannotwork without knowledge”. Weiss et al. clarifies therelationships between Business Intelligence, DataMining, and Knowledge Management (Weiss,Buckley, Kapoor, and Damgaard, 2003).

McKnight has organized KM under BI. Hesuggests that this is a good way to think about therelationship between them (McKnight, 2002). Heargues that KM is internal-facing BI, sharing theintelligence among employees about how effec-tively to perform the variety of functions requiredto make the organization go. Hence, knowledgeis managed using many BI techniques.

Haimila also sees KM as the “helping hand ofBI” (Haimila, 2001). He cites the use of BI by lawenforcement agencies as being a way to maximizetheir use of collected data, enabling them to make


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faster and better-informed decisions because theycan drill down into data to see trends, statisticsand match characteristics of related crimes.

Cook and Cook noted that many people forgetthat the concepts of KM and BI are both rootedin pre-software business management theoriesand practices. They claim that technology hasserved to cloud the definitions. Defining the roleof technology in KM and BI– rather than definingtechnology as KM and BI – is seen by Cook andCook as a way to clarify their distinction (Cook,and Cook 2000).

Text mining, seen primarily as a KM technol-ogy, adds a valuable component to existing BI

technology. Text mining, also known as intelligenttext analysis, text data mining or knowledge-discovery in text (KDT), refers generally to the process of extracting interesting and non-trivialinformation and knowledge from unstructuredtext. Text mining is a young interdisciplinary fieldthat draws on information retrieval, data mining,machine learning, statistics and computationallinguistics. As most information (over 80 percent)is stored as text, text mining is believed to havea high commercial potential value.

Text mining would seem to be a logical exten-sion to the capabilities of current BI products.

However, its seamless integration into BIsoftware is not quite so obvious. Even with the perfection and widespread use of text miningcapabilities, there are a number of issues thatCook and Cook contend that must be addressed before KM (text mining) and BI (data mining)capabilities truly merge into an effective combi-nation. In particular, they claim it is dependenton whether the software vendors are interestedin creating technology that supports the theoriesthat define KM and providing tools that delivercomplete strategic intelligence to decision-makersin companies. However, even if they do, Cook andCook believe that it is unlikely that technologywill ever fully replace the human analysis that

leads to stronger decision making in the upperechelons of the corporation.

The authors provide the following findings:

• BI focuses on explicit knowledge, butKM encompasses both tacit and explicitknowledge.

• Both concepts promote learning, decisionmaking, and understanding. Yet, KM caninuence the very nature of BI itself.

• Integration between BI and KM and makesit clear that BI should be viewed as a sub-set of KM.

• Fundamentally, Business Intelligence and

Knowledge Management have the sameobjective - to focus on improving business performance. If we agree that BusinessIntelligence is comprised of Customer,Competitor and Market Intelligence andthat the purpose of Business Intelligence isto support strategic decision-making, growthe business and monitor the organiza-tion’s competitors,

• The business intelligence concern of DSSin company and deal with customers andcompetitors where as knowledge manage-ment concern about employees

CONCLUSION

There are people who think that BI encapsulatesKM and they do believe so because they arguethat BI is the mean to manage the different knowl-edge in any organization “Share the knowledge”.actually it is a good way to see it, but if we aretrying to look deeper into the different types ofknowledge including tacit and explicit knowledge.Actually, KM can be seen as a boarder notationthan BI because BI deals mainly with structureddata, while KM deals with both structured andunstructured data.


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Conceptually, it is easy to understand howknowledge can be thought of as an integral component of BI and hence decision making. This

chapter argued that KM and BI, while differing,they need to be considered together as necessarilyintegrated and mutually critical components in themanagement of intellectual capital.

In this chapter, the authors provide a detailedoverview of Business Intelligence including: defi-nitions, concepts, goals, architecture, components,and mainly its body of knowledge.

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KEY TERMS AND DEFINITIONS

Body of Knowledge (BoK): The sum of bodyof all knowledge elements in a particular field.

Business Intelligence (BI): An umbrella termthat combines architectures, tools, data bases,applications, practices, and methodologies. Itis the process of transforming various types of business data into meaningful information thatcan help, decision makers at all levels, gettingdeeper insight of business.

Data Mining (DM): The process of discover-ing interesting information from the hidden data

that can either be used for future prediction and/orintelligently summarizing the details of the data.

Data Warehouse (DW): A physical repositorywhere relational data are specially organized to provide enterprise-wide, cleansed data in a stan-dardized format.

Decision Support System (DSS): An approach (or methodology) for supporting making. Ituses an interactive, flexible, adaptable computer- based information system especially developed forsupporting the solution to a specific nonstructuredmanagement problem.

Knowledge: About meaning. It is subjective,difficult to codify, context-related, rooted in ac-tion, and relational.

Knowledge Management (KM): The acquisi-tion, storage, retrieval, application, generation, andreview of the knowledge assets of an organizationin a controlled way.


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Chapter 2

INTRODUCTION

The term Agile Method of software developmentwas coined in the 2001 (Agile Manafesto). Thisapproach is characterized with creativity, flex-ibility, adaptability, responsiveness, and human-centricity (Abrahamsson, et al. 2002). Researchershave suggested that the complex, uncertain, andever-changing environment is pushing developers

to adopt agile methods rather than traditional soft-ware development. That is because the uncertainenvironment is pushing for flexibility in changingrequirements (Manninen & Berki 2004). More-over, the advancements made in developing usersknowledge of computers and computer applicationmade it possible for users to actively participatein the development process, a matter that is lack-ing in traditional software development processes(Monochristou and Vlachopoulou 2007).

Zaidoun Alzoabi Martin Luther University, Germany

Agile Software:Body of Knowledge

ABSTRACT

The term Agile Method of software development was coined in the 2001.This approach is character-

ized with creativity, exibility, adaptability, responsiveness, and human-centricity. Researchers have

suggested that the complex, uncertain, and ever-changing environment is pushing developers to adoptagile methods rather than traditional software development. Agile methodologist claim that their Agile

methods is the answer for the software engineering chaotic situation, in which projects are exceeding

their time and budget limits, requirements are not fullled, and consequently ending up with unsatised

customers. In this chapter we will explain agile methodology, its general characteristics, and quick

description of the famous agile methods known in the industry and research.

DOI: 10.4018/978-1-61350-050-7.ch002


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Agile Software

This agility, however, is challenged with somequality-related issues (Bass, 2006). That is, despiteof the quality features in agile methods, there is

some compromise on the amount of informa-tion and knowledge communicated to customersarising due to the lack of documentation thatstrongly characterizes agile methods (Ambler2005, McBreen 2003, Berki 2006). This was dueto the innate trend in agile methods to concentrateon human-based techniques in communicatingknowledge such as on-site-customer, pair pro-gramming, and daily short meetings.

The human-centricity of Agile methods impliesthat the main focus of the software production

process is to maximize the knowledge transferredand shared among various stakeholders of thesoftware project. Hence, we will investigate theknowledge component in the main Agile method:extreme programming, despite the fact the otherAgile methods show clear KM techniques.

Agile methods in fact came as response tothe failure software projects were facing. Agilemethods came after decades of applying tra-ditional, process-based software developmentmethodologies that are characterized with heavydocumentation, strong emphasis on the process,and less communication with customers (Beck,2000)

The rest of the chapter is organized as follows:first we will introduce agile methods histo

business intelligence and agile methodologies, asim abdel, 2012

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