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62 Part I • Decision Making and Analytics: An Overview

the teradata university network Connection
This book is tightly connected with the free resources provided by Teradata University
Network (TUN; see teradatauniversitynetwork.com). The TUN portal is divided
into two major parts: one for students and one for faculty. This book is connected to
the TUN portal via a special section at the end of each chapter. That section includes
appropriate links for the specific chapter, pointing to relevant resources. In addition,
we provide hands-on exercises, using software and other material (e.g., cases) avail-
able at TUN.

the Book’s Web site
This book’s Web site, pearsonglobaleditions.com/turban, contains supplemental tex-
tual material organized as Web chapters that correspond to the printed book’s chapters.
The topics of these chapters are listed in the online chapter table of contents. Other con-
tent is also available on an independent Web site (dssbibook.com). 2

Chapter Highlights

• The business environment is becoming complex • BI architecture includes a data warehouse, busi-
and is rapidly changing, making decision making ness analytics tools used by end users, and a user
more difficult. interface (such as a dashboard).
• Businesses must respond and adapt to the chang- • Many organizations employ descriptive analytics
ing environment rapidly by making faster and to replace their traditional flat reporting with inter-
better decisions. active reporting that provides insights, trends, and
• The time frame for making decisions is shrinking, patterns in the transactional data.
whereas the global nature of decision making is • Predictive analytics enable organizations to estab-
expanding, necessitating the development and lish predictive rules that drive the business out-
use of computerized DSS. comes through historical data analysis of the
• Computerized support for managers is often existing behavior of the customers.
essential for the survival of an organization. • Prescriptive analytics help in building models that
• An early decision support framework divides involve forecasting and optimization techniques
decision situations into nine categories, depending based on the principles of operations research
on the degree of structuredness and managerial and management science to help organizations to
activities. Each category is supported differently. make better decisions.
• Structured repetitive decisions are supported by • Big Data analytics focuses on unstructured, large
standard quantitative analysis methods, such as MS, data sets that may also include vastly different
MIS, and rule-based automated decision support. types of data for analysis.
• DSS use data, models, and sometimes knowledge • Analytics as a field is also known by industry-
management to find solutions for semistructured specific application names such as sports analytics.
and some unstructured problems. It is also known by other related names such as
• BI methods utilize a central repository called a data science or network science.
data warehouse that enables efficient data mining,
OLAP, BPM, and data visualization.

2 As this book went to press, we verified that all the cited Web sites were active and valid. However, URLs are
dynamic. Web sites to which we refer in the text sometimes change or are discontinued because companies
change names, are bought or sold, merge, or fail. Sometimes Web sites are down for maintenance, repair, or
redesign. Many organizations have dropped the initial “www” designation for their sites, but some still use it. If
you have a problem connecting to a Web site that we mention, please be patient and simply run a Web search
to try to identify the possible new site. Most times, you can quickly find the new site through one of the popular
search engines. We apologize in advance for this inconvenience.

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