D. What do we need for an effective decision?  E

53 
What Is Business Intelligence? 
1. Business intelligence (BI) is the delivery of accurate, useful in-
formation to the appropriate decision makers within the necessary time 
frame to support effective decision making. By this definition all the 
work we have done with Excel would qualify as business intelligence 
since our deliverables contained accurate and useful information to 
support effective decision making. However, business intelligence is 
commonly under stood to include distilling and analyzing large data 
sets such as those found in corporate databases. Extracting and analyz-
ing information stored in databases is the subject BI. It is very likely 
that at multiple points in your work career you will be asked to engage 
in just this type of analysis. 
2. Business intelligence is part of the big picture information sys-
tems architecture. Most systems in existence can be classified either as 
enterprise systems, collaboration systems, or business intelligence sys-
tems. The enterprise systems—taking orders for example—feed their 
data to the data warehouse, which in turn is queried to support business 
intelligence. 
3. From a managerial standpoint, there are three factors necessary 
to make an effective decision: 
- Construct a set of goals to work toward. 
- Determine a way to measure whether a chosen path is moving 
closer or farther from those goals. 
- Present information on those measures to decision makers in a 
timely fashion 
4. For example, let’s say our goals are to develop a clothing busi-
ness that produces high quality products while lowering costs. We fur-
ther determine that we will measure product quality by the percentage 
of products rejected by inspectors at each station. (Think about those 
quality inspector tags that you find in pockets of your new clothes. The 
clothes you are wearing are the ones accepted by the inspector.) A rela-
tively high rejection rate is a red flag to management requiring further 

54 
analysis. Is this an overzealous inspector? Is there any pattern to the 
rejected products? Does one station in the factory tend to produce more 
rejects than the others? 
5. We also need to see performance over time. Is product quality 
improving or getting progressively worse? 
Let’s say that our analysis determines that the high rejection rate 
comes from just one factory in Southeast 
Asia. We report the problem to management. They dispatch a team 
to review the plant. The review discovers child labor, abusive condi-
tions, and very low morale at the plant. The horrible conditions are 
quickly re- versed and the rejection rate returns to average. 
6. We will look at three types of business intelligence—static re-
ports, dynamic reports, and data mining. Static reports are by far the 
most common form of business intelligence. Most businesses have 
summarized standard reports already laid out and printed to assist in 
managerial decision making. For example, universities use enrollment 
reports to gauge which departments might need to hire more faculty. 
Credit card companies will request reports of persons with high credit 
scores to target credit card promotions. Similarly, the companies might 
target college students with good future earning potential. Marketers 
might look at sales figures for different stores and regions to determine 
where there are opportunities to run a sales promotion. 
7. Dynamic reports look similar to static reports but online and in-
teractive. A manager curious as to where a certain summary number on 
his dashboard comes from can drill down to expose the detail that con-
tributed to that number. In essence it is a fact-finding tour where infor-
mation discovered in each step gives clues on where to search next for 
information. For example, if sales in North America are down, then 
drill down to discover a problem in the Midwest region. Then drill 
down farther to discover a problem in the Cleveland, Ohio plant. 
8. Data mining uses computer programs and statistical analyses to 
search for unexpected patterns, correlations, trends, and clustering in 
the data. In essence, it is fishing through the data to see if there are pat-

55 
terns of interest. One often cited example of data mining was the dis-
covery that beer and diapers are frequently purchased on the same trip 
to the grocery store. Upon further inquiry marketers discovered that 
Dad picks up some beer on his trip to the grocery store to buy diapers. 
Marketers can use this information to place the two items in close prox-
imity in the store. 

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