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Chapter 1 • An Overview of Business Intelligence, Analytics, and Decision Support 49

Third, BI has an executive and strategy orientation, especially in its BPM and dash-
board components. DSS, in contrast, is oriented toward analysts.
Fourth, most BI systems are constructed with commercially available tools and com-
ponents that are fitted to the needs of organizations. In building DSS, the interest may
be in constructing solutions to very unstructured problems. In such situations, more pro-
gramming (e.g., using tools such as Excel) may be needed to customize the solutions.
Fifth, DSS methodologies and even some tools were developed mostly in the aca-
demic world. BI methodologies and tools were developed mostly by software companies.
(See Zaman, 2005, for information on how BI has evolved.)
Sixth, many of the tools that BI uses are also considered DSS tools. For example,
data mining and predictive analysis are core tools in both areas.
Although some people equate DSS with BI, these systems are not, at present, the
same. It is interesting to note that some people believe that DSS is a part of BI—one of its
analytical tools. Others think that BI is a special case of DSS that deals mostly with report-
ing, communication, and collaboration (a form of data-oriented DSS). Another explana-
tion (Watson, 2005) is that BI is a result of a continuous revolution and, as such, DSS is
one of BI’s original elements. In this book, we separate DSS from BI. However, we point
to the DSS–BI connection frequently. Further, as noted in the next section onward, in
many circles BI has been subsumed by the new term analytics or data science.

sectiOn 1.7 revieW QuestiOns
1. Define BI.
2. List and describe the major components of BI.
3. What are the major similarities and differences of DSS and BI?

1.8 Business analytiCs overvieW

The word “analytics” has replaced the previous individual components of computerized
decision support technologies that have been available under various labels in the past.
Indeed, many practitioners and academics now use the word analytics in place of BI.
Although many authors and consultants have defined it slightly differently, one can view
analytics as the process of developing actionable decisions or recommendation for actions
based upon insights generated from historical data. The Institute for Operations Research
and Management Science (INFORMS) has created a major initiative to organize and pro-
mote analytics. According to INFORMS, analytics represents the combination of computer
technology, management science techniques, and statistics to solve real problems. Of
course, many other organizations have proposed their own interpretations and motivation
for analytics. For example, SAS Institute Inc. proposed eight levels of analytics that begin
with standardized reports from a computer system. These reports essentially provide a
sense of what is happening with an organization. Additional technologies have enabled
us to create more customized reports that can be generated on an ad hoc basis. The next
extension of reporting takes us to online analytical processing (OLAP)–type queries that
allow a user to dig deeper and determine the specific source of concern or opportuni-
ties. Technologies available today can also automatically issue alerts for a decision maker
when performance issues warrant such alerts. At a consumer level we see such alerts for
weather or other issues. But similar alerts can also be generated in specific settings when
sales fall above or below a certain level within a certain time period or when the inventory
for a specific product is running low. All of these applications are made possible through
analysis and queries on data being collected by an organization. The next level of analysis
might entail statistical analysis to better understand patterns. These can then be taken a
step further to develop forecasts or models for predicting how customers might respond to

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