Description
The focus on transactional systems in the earlier decades of information management is beginning to shift toward decisions.
Organizational
Applications of Business
Intelligence Management
Emerging Trends
Richard Herschel '"
Saint Joseph's University, USA
lliiissi
S C I E N C E
Detailed Table of Contents
Preface xvi
S ection 1
Organizational I ssues
C hapter 1
Business Intelligence and Organizational Decisions.
Thomas H. Davenport, Babson College, USA
The focus on transactional systems in the earlier decades of information management is beginning to
shift toward decisions. In order to study the relationship between information and decisions, the author
interviewed 32 managers in 27 organizations where an attempt to use information to support decision-
making had been made. A framework involving three different relationships between information and
decisions is introduced: loosely-coupled, structured human, and automated. It is suggested that loosely-
coupled information and decision environments, while productive for information providers, may require
too'much knowledge on the part of information users to be effective. A four-step process for bringing,
information and decisions in closer alignment is also advanced.
C hapter 2
Business Plus Intelligence Plus Technology Equals Business Intelligence 13
Ira Yermish, Saint Joseph's University, USA
Virginia Miori, Saint Joseph's University, USA
John Yi, Saint Joseph's University, USA
Rashmi Malhotra, Saint Joseph's University, USA
Ronald Klimberg, Saint Joseph's University, USA
In this article the authors will show how the parallel developments of information technology at the
operational business level and decision support concepts progressed through the decades of the twentieth
century with only minimal success at strategic application. They will posit that the twin technological
developments of the world-wide-web and very inexpensive mass storage provided the environment to
facilitate the convergence of business operations and decision support into the strategic application of
business intelligence.
C hapter 3
Business Intelligence in the Bayou: Recovering Costs in the Wake of Hurricane Katrina 29
Gregory Smith, Xavier University, USA
Thilini Ariyachandra, Xavier University, USA
Mark Frolick, Xavier University, USA
During the 2005 Atlantic hurricane season, Hurricane Katrina wreaked havoc on New Orleans. Sig-
nificant damage to the Gulf region forced the Federal Emergency Management Agency (FEMA) to
begin an unprecedented cleanup effort. The removal and disposal of debris was not only a challenge
for landfill capacity but also for the administration of drivers, trucks, and debris type. With the debris
removal workforce and certified hauling vehicles changing rapidly, record keeping and fraud detection
proved difficult. This paper introduces the results of a data driven manpower audit for one parish in the
greater New Orleans area that consolidated records and reconciled multiple record keeping systems.
The authors' findings bring to light the failings in record keeping during this disaster and highlight how
a simple business intelligence application can improve the accuracy and quality of data and save costs.
C hapter 4 ""
The Role of Culture in Business Intelligence 38
Jore Park, IndaSea, Inc., USA
Wylci Fables, IndaSea, Inc., USA
Kevin R. Parker, Idaho State University, USA
Philip S. Nitse, Idaho State University, USA
Global business intelligence will struggle to live up to its potential if it fails to take into account, and
accurately interpret, cultural differences. This paper supports this assertion by considering the concept
of culture, explaining its importance in the business intelligence process, especially in foreign markets,
and demonstrating that attention to culture is currently inadequate in most international business intel-
ligence.efforts. Without a tool capable of modeling social interaction in disparate cultures, BI efforts
will under perform when extended to the global arena. The Cultural Simulation Modeler is examined as
a means of enhancing essential cultural awareness. The core components of the modeler are explained,
as are the limitations of automated information gathering and analysis systems.
C hapter 5
What is Business Intelligence? 52
Eric Foley, Universite de Sherbrooke, Canada
Manon G. Guillemette, Universite de Sherbrooke, Canada —
There has been growing corporate interest in business intelligence (BI) as a path to reduced costs, im-
proved service quality, and better decision-making processes. However, while BI has existed for years, it
has difficulties reaching what specialists in the field consider its full potential. In this paper, the authors
examine disparities in how the constructs of business intelligence are defined and understood, which
may impede an understanding of what BI represents to business leaders and researchers. The main
objective of this study is to clearly understand this emerging concept of BI. In this regard, the authors
analyze articles from the scientific and professional literature to have a comprehensive understanding
of business intelligence as both a product and a process. This research proposes a global overview of
the conceptual foundations of BI, which can help companies understand their BI initiative and leverage
them to the strategic level.
C hapter 6
The Importance of Process Thinking in Business Intelligence 76
Olivera Marjanovic, University of Sydney, Australia
The growing field of Operational Business Intelligence (BI) has resulted in increasing interest in BI-
supported Business Processes (BPs), including their management and ongoing improvement. This has
led BI practitioners to consider another field-Business Process Management (BPM)-that is closely
related to business performance management. However, current approaches to the BPM and operational
BI integration have been limited and reduced to the problem of technical integration of BPM and BI
systems. This paper argues that by adopting process- thinking in BI, further opportunities for business
value creation could be discovered through systematic analysis of the non-technical aspects of BI and
BPM integration, including strategy alignment, human-centered knowledge management, and ongoing
improvement of BI supported processes. The authors propose a theoretical framework founded in the
related research in BPM, BI, and Knowledge Management (KM) fields, describing the ways it has been
used to guide ongoing empirical research in diverse case organizations across different industry sectors.
S ection 2
Analytic I ssues
C hapter 7
Strategies for Document Management 96
Karen Corral, Boise State University, USA
David Schuff, Temple University, USA
Gregory Schymik, Arizona State University, USA
Robert St. Louis, Arizona State University, USA
Keyword search has failed to adequately meet the needs of enterprise users. This is largely due to the
size of document stores, the distribution of word frequencies, and the indeterminate nature of languages.
The authors argue a different approach needs to be taken, and draw on the successes of dimensional data
modeling and subject indexing to propose a solution. They test our solution by performing search queries
on a large research database. By incorporating readily available subject indexes into the search process,
they obtain order of magnitude improvements in the performance of search queries. Their performance
measure is the ratio of the number of documents returned without using subject indexes to the number
of documents returned when subject indexes are used. The authors explain why the observed tenfold
improvement in search performance on our research database can be expected to occur for searches on
a wide variety of enterprise document stores.
C hapter 8
The Current State of Analytics in the Corporation: The View from Industry Leaders 115
Thomas Coghlan, Villanova University, USA
George Diehl, Villanova University, USA
Eric Karson, Villanova University, USA
Matthew Liberatore, Villanova University, USA
Wenhong Luo, Villanova University, USA
Robert Nydick, Villanova University, USA
Bruce Pollack-Johnson, Villanova University, USA
William Wagner, Villanova University, USA
Business intelligence and analytics in general are currently experiencing a resurgence in interest from
both the business and academic communities. As a response, a Business Analytics Special Interest Group
(SIG) was formed at Villanova University in 2007 to better link these two communities and support
the growing needs of business. As a multi-disciplinary group composed of both analytics professionals
and academics, one of the first tasks was to investigate how businesses viewed analytics and how they
were incorporating them in actual practice. With this in mind, an interview questionnaire was developed
and senior-level executives from a diverse group of sixteen different firms were interviewed in a group
context. Their responses led to the development of a new, integrated analytics curriculum and the es-
tablishment of a new Analytics Round Table. The results from this series of semi-structured interviews
are presented in this paper.
C hapter 9
Application of Triplet Notation and Dynamic Programming to Single-Line,
Multi-Product Dairy Production Scheduling 121
Virginia M. Miori, St. Joseph's University, USA
Brian Segulin, RoviSys Co., USA ^
The application of optimal methods for production scheduling in the dairy industry has been limited.
Within supply chain terminology, dairy production was generally considered a push process but with
advancements in automation, the industry is slowly transforming to a pull process. In this paper, the
authors present triplet notation applied to the production scheduling of a single production line used for
milk, juice, and carnival drinks. Once production and cleaning cycles are characterized as triplets, the
problem is formulated. Lagrange relaxation is applied and the final solution is generated using dynamic
programming.
C hapter 10
Data Mining for Health Care Professionals: MBA Course Projects Resulting
in Hospital Improvements 133
Alan Olinsky, Bryant University, USA
Phyllis A. Schumacher, Bryant University, USA
In this paper, the authors discuss a data mining course that was offered for a cohort of health care profes-
sionals employed by a hospital consortium as an elective in a synchronous online MBA program. The
students learned to use data mining to analyze data on two platforms, Enterprise Miner, SAS (2008) and
XLMiner (an EXCEL add-in). The final assignment for the semester was for the students to analyze a
data set from their place of employment. This paper describes the projects and resulting benefits to the
companies for which the students worked.
C hapter 11
Data-Driven Decision Making for New Drugs: A Collaborative Learning Experience 144
George P. Sillup, Saint Joseph's University, USA
Ronald K. Klimberg, Saint Joseph's University, USA
David P. McSweeney, Healthcare Data Management, Inc., USA
Two courses, advanced decision-making and pharmaceutical marketing, were combined in a collabora-
tive process to mimic how the pharmaceutical industry determines the potential of new drugs. Integrated
student teams worked together to complete semester-long projects and taught each other their respective
knowledge areas—marketing and statistics. Real-world data for medical and pharmacy claims pay-
ments were "cleaned" and mined by students to analyze usage and cost patterns for anti-hypertensive
and anti-hypercholesterolemia drugs currently on the market. Analyses included merging the medical
and pharmaceutical data records to derive individual electronic patient records, which were the basis
of financial projections for the new drugs. Importantly, the single patient record is congruent with the
needs of the stakeholders currently working to reform U.S. healthcare delivery.
C hapter 12
Towards Private-Public Research Partnerships Combining Rigor and Relevance
in DWH/BI Research: The Competence Center Approach 163
Anne Cleven, University of St. Gallen, Switzerland
Robert Winter, University of St. Gallen, Switzerland
Felix Wortmann, University of St. Gallen, Switzerland
Business intelligence (BI) and data warehousing (DWH) research represent two increasingly popular,
but still emerging fields in the information systems (IS) academic discipline. As such, they raise two
substantial questions: Firstly, "how rigorous, i.e., fundamental, constituent, and explanatory, is DWH BI
research?" and, secondly, "how relevant, i.e., useful and purposeful, is this research to practitioners?"
In this article, the authors uphold the position that relevance and rigor are by no means dichotomous,
but two sides of the same coin. Naturally, this requires well-defined approaches and guidelines—for
scholarship in general and DWH/BI research in particular. Therefore, this paper proposes the competence
center (CC) approach—a private-public partnership between academiaand practice. The authors illustrate
how the CC approach can be applied within the field of DWH/BI and suggest that a close link between
research and practice supports both enhancing relevance to practice and strengthening rigor of research.
C hapter 13
Improving Business Intelligence: The Six Sigma Way 176
Dorothy Miller, Industry Consultant in Business Intelligence, USA
Business Intelligence has never been examined with the same rigor as demanded for any other organiza-
tion investments. Although global investment in Business Intelligence has reached over 6 billion dollars,
business managers continue to follow tradition and leave the management of business intelligence to
the technocrats. In this paper, the author proposes that a critical need exists to apply the same six sigma
methods, which have worked for the rest of the organization to business intelligence operations and prod-
ucts. This proven structured approach, including the associated rigor and metrics, can be customized and
integrated into a program which will allow effective management of business intelligence. The proposed
Six Sigma program for business intelligence will ensure that an organization can gain control, improve
understanding of operations and products, and improve the value of this crucial organization investment.
S ection 3
Technology I ssues
C hapter 14
The BI-Based Organization 193
Barbara Wixom, University of Virginia, USA
Hugh Watson, University of Georgia, USA
Business intelligence (BI) is an umbrella term that is commonly used to describe the technologies, ap-
plications, and processes for gathering, storing, accessing, and analyzing data to help users make better
decisions. For Bl-based firms, BI is a prerequisite for competing in the marketplace. Though there are
several possible BI targets, it is important to understand how they differ in terms of strategic vision,
level of sponsorship, required resources, impact on people and processes, and benefits. Some companies
like Harrah's Entertainment, Continental Airlines, Norfolk Southern, and Blue Cross and Blue Shield
of North Carolina are exemplars of BI best practices. Despite the progress made with BI, there are still
many opportunities for academic research.
C hapter 15
Do Users Go Both Ways? BI User Profiles Fit BI Tools 209
Hamid Nemati, University of North Carolina at Greensboro, USA
Brad Earle, KB Earle Associates, USA
Satya Arekapudi, CommScope, Inc., USA
Sanjay Mamani, Republic Mortgage Insurance Company, USA
A challenging task for a data warehouse team is identifying users by their information needs and skills,
and then providing the BI (Business Intelligence) tools that support each group to do their job effectively
and efficiently. Recent studies have shown that the BI market place is saturated with a bewildering array
of capabilities, functions and software suites. The current lack of consistent interpretation of Business
Intelligence has created some confusion in the market place. This paper defines a framework to identify
different user groups in an organization and map their needs and requirements to the different function-
alities offered by different BI tool vendors. Through literature review, clear definitions of users were
created and a set of BI tools that identifies functional needs was established. From that information, a
questionnaire was developed that probed for the relationships between user types, tools, functions and
other perceived values. Responses from 154 professionals were then used to develop a road map for the
data warehouse project team in BI tool selection.
C hapter 16
Enterprise Information System and Data Mining 228
Kenneth D. Lawrence, New Jersey Institute of Technology, USA
Dinesh R. Pai, Penn State Lehigh Valley, USA
RonaldKlimberg, Saint Joseph's University, USA
Sheila M. Lawrence, Rutgers University, USA
The advent of information technology and the consequent proliferation of information systems have lead
to generation of vast amounts of data, both within the organization and across its supply chain. Enter-
prise information systems (EIS) have added to organizational complexity, and at the same time, created
opportunities for enhancing its competitive advantage by utilizing this data for business intelligence
purposes. Various data mining tools have been used to gain a competitive edge through these large data
bases. In this paper, the authors discuss ElS-aided business intelligence and data mining as applicable to
organizational functions, such as supply chain management (SCM), marketing, and customer relation-
ship management (CRM) in the context of EIS.
C hapter 17
Historical Data Analysis through Data Mining from an Outsourcing Perspective:
The Three-Phases Model 236
Arjen Vleugel, Utrecht University, The Netherlands
Marco Spruit, Utrecht University, The Netherlands
Anton van Daal, In Summa, The Netherlands
The process of historical data analysis through data mining has proven valuable for the industrial envi-
ronment. There are many models available that describe the in-house process of data mining. However,
many companies either do not have in-house skills or do not wish to invest in performing in-house data
mining. This paper investigates the applicability of two well-established data mining process models in
an outsourcing context. The authors observe that both models cannot properly accommodate several key
aspects in this context; therefore, this paper proposes the Three-phases method, which consists of data
retrieval, data mining and results implementation within an organization. Each element is presented as
a visual method fragment, and the model is validated through expert interviews and an extensive case
study at a large Dutch staffing company. Both validation techniques substantiate the authors' claim that
the Three-phases model accurately describes the data mining process from an outsourcing perspective.
C hapter 18
Data Warehousing Requirements Collection and Definition: Analysis of a Failure 261
Nenad Jukic, Loyola University Chicago, USA
Miguel Velasco, University of Minnesota, USA
Defining data warehouse requirements is widely recognized as one of the most important steps in the
larger data warehouse system development process. This paper examines the potential risks and pitfalls
within the data warehouse requirement collection and definition process. A real scenario of a large-scale
data warehouse implementation is given, and details of this project, which ultimately failed due to inad-
equate requirement collection and definition process, are described. The presented case underscores and
illustrates the impact of the requirement collection and definition process on the data warehouse imple-
mentation, while the case is analyzed within the context of the existing approaches, methodologies, and
best practices for prevention and avoidance of typical data warehouse requirement errors and oversights.
C hapter 19
Business Intelligence 2.0: The extensible Markup Language as Strategic Enabler 272
Ruben A. Mendoza, Saint Joseph's University, USA
Business Intelligence 2.0 is an umbrella term used to refer to a collection of tools that help organizations
extend their BI capabilities using Internet platforms. BI 2.0 tools can enable the automatic discovery
of distributed software services and data stores, greatly increasing the range of market options for an
organization. The development cycle for these tools is still in its early stage, and much work remains.
However, some technologies and standards are already well understood in order to make a significant
impact. This paper provides an overview of the extensible Markup Language (XML) and related tech-
nologies supporting the deployment of web services and service-oriented architectures (SOA). The
author summarizes the critical importance of these technologies to the emergence of BI 2.0 tools. This
paper also explores the current state of Internet-enabled BI activities and strategic considerations for
firms considering BI 2.0 options.
C ompilation of References 286
About the C ontributors 309
I ndex 316
doc_234329986.pdf
The focus on transactional systems in the earlier decades of information management is beginning to shift toward decisions.
Organizational
Applications of Business
Intelligence Management
Emerging Trends
Richard Herschel '"
Saint Joseph's University, USA
lliiissi
S C I E N C E
Detailed Table of Contents
Preface xvi
S ection 1
Organizational I ssues
C hapter 1
Business Intelligence and Organizational Decisions.
Thomas H. Davenport, Babson College, USA
The focus on transactional systems in the earlier decades of information management is beginning to
shift toward decisions. In order to study the relationship between information and decisions, the author
interviewed 32 managers in 27 organizations where an attempt to use information to support decision-
making had been made. A framework involving three different relationships between information and
decisions is introduced: loosely-coupled, structured human, and automated. It is suggested that loosely-
coupled information and decision environments, while productive for information providers, may require
too'much knowledge on the part of information users to be effective. A four-step process for bringing,
information and decisions in closer alignment is also advanced.
C hapter 2
Business Plus Intelligence Plus Technology Equals Business Intelligence 13
Ira Yermish, Saint Joseph's University, USA
Virginia Miori, Saint Joseph's University, USA
John Yi, Saint Joseph's University, USA
Rashmi Malhotra, Saint Joseph's University, USA
Ronald Klimberg, Saint Joseph's University, USA
In this article the authors will show how the parallel developments of information technology at the
operational business level and decision support concepts progressed through the decades of the twentieth
century with only minimal success at strategic application. They will posit that the twin technological
developments of the world-wide-web and very inexpensive mass storage provided the environment to
facilitate the convergence of business operations and decision support into the strategic application of
business intelligence.
C hapter 3
Business Intelligence in the Bayou: Recovering Costs in the Wake of Hurricane Katrina 29
Gregory Smith, Xavier University, USA
Thilini Ariyachandra, Xavier University, USA
Mark Frolick, Xavier University, USA
During the 2005 Atlantic hurricane season, Hurricane Katrina wreaked havoc on New Orleans. Sig-
nificant damage to the Gulf region forced the Federal Emergency Management Agency (FEMA) to
begin an unprecedented cleanup effort. The removal and disposal of debris was not only a challenge
for landfill capacity but also for the administration of drivers, trucks, and debris type. With the debris
removal workforce and certified hauling vehicles changing rapidly, record keeping and fraud detection
proved difficult. This paper introduces the results of a data driven manpower audit for one parish in the
greater New Orleans area that consolidated records and reconciled multiple record keeping systems.
The authors' findings bring to light the failings in record keeping during this disaster and highlight how
a simple business intelligence application can improve the accuracy and quality of data and save costs.
C hapter 4 ""
The Role of Culture in Business Intelligence 38
Jore Park, IndaSea, Inc., USA
Wylci Fables, IndaSea, Inc., USA
Kevin R. Parker, Idaho State University, USA
Philip S. Nitse, Idaho State University, USA
Global business intelligence will struggle to live up to its potential if it fails to take into account, and
accurately interpret, cultural differences. This paper supports this assertion by considering the concept
of culture, explaining its importance in the business intelligence process, especially in foreign markets,
and demonstrating that attention to culture is currently inadequate in most international business intel-
ligence.efforts. Without a tool capable of modeling social interaction in disparate cultures, BI efforts
will under perform when extended to the global arena. The Cultural Simulation Modeler is examined as
a means of enhancing essential cultural awareness. The core components of the modeler are explained,
as are the limitations of automated information gathering and analysis systems.
C hapter 5
What is Business Intelligence? 52
Eric Foley, Universite de Sherbrooke, Canada
Manon G. Guillemette, Universite de Sherbrooke, Canada —
There has been growing corporate interest in business intelligence (BI) as a path to reduced costs, im-
proved service quality, and better decision-making processes. However, while BI has existed for years, it
has difficulties reaching what specialists in the field consider its full potential. In this paper, the authors
examine disparities in how the constructs of business intelligence are defined and understood, which
may impede an understanding of what BI represents to business leaders and researchers. The main
objective of this study is to clearly understand this emerging concept of BI. In this regard, the authors
analyze articles from the scientific and professional literature to have a comprehensive understanding
of business intelligence as both a product and a process. This research proposes a global overview of
the conceptual foundations of BI, which can help companies understand their BI initiative and leverage
them to the strategic level.
C hapter 6
The Importance of Process Thinking in Business Intelligence 76
Olivera Marjanovic, University of Sydney, Australia
The growing field of Operational Business Intelligence (BI) has resulted in increasing interest in BI-
supported Business Processes (BPs), including their management and ongoing improvement. This has
led BI practitioners to consider another field-Business Process Management (BPM)-that is closely
related to business performance management. However, current approaches to the BPM and operational
BI integration have been limited and reduced to the problem of technical integration of BPM and BI
systems. This paper argues that by adopting process- thinking in BI, further opportunities for business
value creation could be discovered through systematic analysis of the non-technical aspects of BI and
BPM integration, including strategy alignment, human-centered knowledge management, and ongoing
improvement of BI supported processes. The authors propose a theoretical framework founded in the
related research in BPM, BI, and Knowledge Management (KM) fields, describing the ways it has been
used to guide ongoing empirical research in diverse case organizations across different industry sectors.
S ection 2
Analytic I ssues
C hapter 7
Strategies for Document Management 96
Karen Corral, Boise State University, USA
David Schuff, Temple University, USA
Gregory Schymik, Arizona State University, USA
Robert St. Louis, Arizona State University, USA
Keyword search has failed to adequately meet the needs of enterprise users. This is largely due to the
size of document stores, the distribution of word frequencies, and the indeterminate nature of languages.
The authors argue a different approach needs to be taken, and draw on the successes of dimensional data
modeling and subject indexing to propose a solution. They test our solution by performing search queries
on a large research database. By incorporating readily available subject indexes into the search process,
they obtain order of magnitude improvements in the performance of search queries. Their performance
measure is the ratio of the number of documents returned without using subject indexes to the number
of documents returned when subject indexes are used. The authors explain why the observed tenfold
improvement in search performance on our research database can be expected to occur for searches on
a wide variety of enterprise document stores.
C hapter 8
The Current State of Analytics in the Corporation: The View from Industry Leaders 115
Thomas Coghlan, Villanova University, USA
George Diehl, Villanova University, USA
Eric Karson, Villanova University, USA
Matthew Liberatore, Villanova University, USA
Wenhong Luo, Villanova University, USA
Robert Nydick, Villanova University, USA
Bruce Pollack-Johnson, Villanova University, USA
William Wagner, Villanova University, USA
Business intelligence and analytics in general are currently experiencing a resurgence in interest from
both the business and academic communities. As a response, a Business Analytics Special Interest Group
(SIG) was formed at Villanova University in 2007 to better link these two communities and support
the growing needs of business. As a multi-disciplinary group composed of both analytics professionals
and academics, one of the first tasks was to investigate how businesses viewed analytics and how they
were incorporating them in actual practice. With this in mind, an interview questionnaire was developed
and senior-level executives from a diverse group of sixteen different firms were interviewed in a group
context. Their responses led to the development of a new, integrated analytics curriculum and the es-
tablishment of a new Analytics Round Table. The results from this series of semi-structured interviews
are presented in this paper.
C hapter 9
Application of Triplet Notation and Dynamic Programming to Single-Line,
Multi-Product Dairy Production Scheduling 121
Virginia M. Miori, St. Joseph's University, USA
Brian Segulin, RoviSys Co., USA ^
The application of optimal methods for production scheduling in the dairy industry has been limited.
Within supply chain terminology, dairy production was generally considered a push process but with
advancements in automation, the industry is slowly transforming to a pull process. In this paper, the
authors present triplet notation applied to the production scheduling of a single production line used for
milk, juice, and carnival drinks. Once production and cleaning cycles are characterized as triplets, the
problem is formulated. Lagrange relaxation is applied and the final solution is generated using dynamic
programming.
C hapter 10
Data Mining for Health Care Professionals: MBA Course Projects Resulting
in Hospital Improvements 133
Alan Olinsky, Bryant University, USA
Phyllis A. Schumacher, Bryant University, USA
In this paper, the authors discuss a data mining course that was offered for a cohort of health care profes-
sionals employed by a hospital consortium as an elective in a synchronous online MBA program. The
students learned to use data mining to analyze data on two platforms, Enterprise Miner, SAS (2008) and
XLMiner (an EXCEL add-in). The final assignment for the semester was for the students to analyze a
data set from their place of employment. This paper describes the projects and resulting benefits to the
companies for which the students worked.
C hapter 11
Data-Driven Decision Making for New Drugs: A Collaborative Learning Experience 144
George P. Sillup, Saint Joseph's University, USA
Ronald K. Klimberg, Saint Joseph's University, USA
David P. McSweeney, Healthcare Data Management, Inc., USA
Two courses, advanced decision-making and pharmaceutical marketing, were combined in a collabora-
tive process to mimic how the pharmaceutical industry determines the potential of new drugs. Integrated
student teams worked together to complete semester-long projects and taught each other their respective
knowledge areas—marketing and statistics. Real-world data for medical and pharmacy claims pay-
ments were "cleaned" and mined by students to analyze usage and cost patterns for anti-hypertensive
and anti-hypercholesterolemia drugs currently on the market. Analyses included merging the medical
and pharmaceutical data records to derive individual electronic patient records, which were the basis
of financial projections for the new drugs. Importantly, the single patient record is congruent with the
needs of the stakeholders currently working to reform U.S. healthcare delivery.
C hapter 12
Towards Private-Public Research Partnerships Combining Rigor and Relevance
in DWH/BI Research: The Competence Center Approach 163
Anne Cleven, University of St. Gallen, Switzerland
Robert Winter, University of St. Gallen, Switzerland
Felix Wortmann, University of St. Gallen, Switzerland
Business intelligence (BI) and data warehousing (DWH) research represent two increasingly popular,
but still emerging fields in the information systems (IS) academic discipline. As such, they raise two
substantial questions: Firstly, "how rigorous, i.e., fundamental, constituent, and explanatory, is DWH BI
research?" and, secondly, "how relevant, i.e., useful and purposeful, is this research to practitioners?"
In this article, the authors uphold the position that relevance and rigor are by no means dichotomous,
but two sides of the same coin. Naturally, this requires well-defined approaches and guidelines—for
scholarship in general and DWH/BI research in particular. Therefore, this paper proposes the competence
center (CC) approach—a private-public partnership between academiaand practice. The authors illustrate
how the CC approach can be applied within the field of DWH/BI and suggest that a close link between
research and practice supports both enhancing relevance to practice and strengthening rigor of research.
C hapter 13
Improving Business Intelligence: The Six Sigma Way 176
Dorothy Miller, Industry Consultant in Business Intelligence, USA
Business Intelligence has never been examined with the same rigor as demanded for any other organiza-
tion investments. Although global investment in Business Intelligence has reached over 6 billion dollars,
business managers continue to follow tradition and leave the management of business intelligence to
the technocrats. In this paper, the author proposes that a critical need exists to apply the same six sigma
methods, which have worked for the rest of the organization to business intelligence operations and prod-
ucts. This proven structured approach, including the associated rigor and metrics, can be customized and
integrated into a program which will allow effective management of business intelligence. The proposed
Six Sigma program for business intelligence will ensure that an organization can gain control, improve
understanding of operations and products, and improve the value of this crucial organization investment.
S ection 3
Technology I ssues
C hapter 14
The BI-Based Organization 193
Barbara Wixom, University of Virginia, USA
Hugh Watson, University of Georgia, USA
Business intelligence (BI) is an umbrella term that is commonly used to describe the technologies, ap-
plications, and processes for gathering, storing, accessing, and analyzing data to help users make better
decisions. For Bl-based firms, BI is a prerequisite for competing in the marketplace. Though there are
several possible BI targets, it is important to understand how they differ in terms of strategic vision,
level of sponsorship, required resources, impact on people and processes, and benefits. Some companies
like Harrah's Entertainment, Continental Airlines, Norfolk Southern, and Blue Cross and Blue Shield
of North Carolina are exemplars of BI best practices. Despite the progress made with BI, there are still
many opportunities for academic research.
C hapter 15
Do Users Go Both Ways? BI User Profiles Fit BI Tools 209
Hamid Nemati, University of North Carolina at Greensboro, USA
Brad Earle, KB Earle Associates, USA
Satya Arekapudi, CommScope, Inc., USA
Sanjay Mamani, Republic Mortgage Insurance Company, USA
A challenging task for a data warehouse team is identifying users by their information needs and skills,
and then providing the BI (Business Intelligence) tools that support each group to do their job effectively
and efficiently. Recent studies have shown that the BI market place is saturated with a bewildering array
of capabilities, functions and software suites. The current lack of consistent interpretation of Business
Intelligence has created some confusion in the market place. This paper defines a framework to identify
different user groups in an organization and map their needs and requirements to the different function-
alities offered by different BI tool vendors. Through literature review, clear definitions of users were
created and a set of BI tools that identifies functional needs was established. From that information, a
questionnaire was developed that probed for the relationships between user types, tools, functions and
other perceived values. Responses from 154 professionals were then used to develop a road map for the
data warehouse project team in BI tool selection.
C hapter 16
Enterprise Information System and Data Mining 228
Kenneth D. Lawrence, New Jersey Institute of Technology, USA
Dinesh R. Pai, Penn State Lehigh Valley, USA
RonaldKlimberg, Saint Joseph's University, USA
Sheila M. Lawrence, Rutgers University, USA
The advent of information technology and the consequent proliferation of information systems have lead
to generation of vast amounts of data, both within the organization and across its supply chain. Enter-
prise information systems (EIS) have added to organizational complexity, and at the same time, created
opportunities for enhancing its competitive advantage by utilizing this data for business intelligence
purposes. Various data mining tools have been used to gain a competitive edge through these large data
bases. In this paper, the authors discuss ElS-aided business intelligence and data mining as applicable to
organizational functions, such as supply chain management (SCM), marketing, and customer relation-
ship management (CRM) in the context of EIS.
C hapter 17
Historical Data Analysis through Data Mining from an Outsourcing Perspective:
The Three-Phases Model 236
Arjen Vleugel, Utrecht University, The Netherlands
Marco Spruit, Utrecht University, The Netherlands
Anton van Daal, In Summa, The Netherlands
The process of historical data analysis through data mining has proven valuable for the industrial envi-
ronment. There are many models available that describe the in-house process of data mining. However,
many companies either do not have in-house skills or do not wish to invest in performing in-house data
mining. This paper investigates the applicability of two well-established data mining process models in
an outsourcing context. The authors observe that both models cannot properly accommodate several key
aspects in this context; therefore, this paper proposes the Three-phases method, which consists of data
retrieval, data mining and results implementation within an organization. Each element is presented as
a visual method fragment, and the model is validated through expert interviews and an extensive case
study at a large Dutch staffing company. Both validation techniques substantiate the authors' claim that
the Three-phases model accurately describes the data mining process from an outsourcing perspective.
C hapter 18
Data Warehousing Requirements Collection and Definition: Analysis of a Failure 261
Nenad Jukic, Loyola University Chicago, USA
Miguel Velasco, University of Minnesota, USA
Defining data warehouse requirements is widely recognized as one of the most important steps in the
larger data warehouse system development process. This paper examines the potential risks and pitfalls
within the data warehouse requirement collection and definition process. A real scenario of a large-scale
data warehouse implementation is given, and details of this project, which ultimately failed due to inad-
equate requirement collection and definition process, are described. The presented case underscores and
illustrates the impact of the requirement collection and definition process on the data warehouse imple-
mentation, while the case is analyzed within the context of the existing approaches, methodologies, and
best practices for prevention and avoidance of typical data warehouse requirement errors and oversights.
C hapter 19
Business Intelligence 2.0: The extensible Markup Language as Strategic Enabler 272
Ruben A. Mendoza, Saint Joseph's University, USA
Business Intelligence 2.0 is an umbrella term used to refer to a collection of tools that help organizations
extend their BI capabilities using Internet platforms. BI 2.0 tools can enable the automatic discovery
of distributed software services and data stores, greatly increasing the range of market options for an
organization. The development cycle for these tools is still in its early stage, and much work remains.
However, some technologies and standards are already well understood in order to make a significant
impact. This paper provides an overview of the extensible Markup Language (XML) and related tech-
nologies supporting the deployment of web services and service-oriented architectures (SOA). The
author summarizes the critical importance of these technologies to the emergence of BI 2.0 tools. This
paper also explores the current state of Internet-enabled BI activities and strategic considerations for
firms considering BI 2.0 options.
C ompilation of References 286
About the C ontributors 309
I ndex 316
doc_234329986.pdf