Type
Solution Manual
Book Title
Business Driven Information Systems 5th Edition
ISBN 13
978-0073402987

978-0073402987 Chapter 6 Section 6.2 Business Intelligence

April 4, 2019
SECTION 6.2
BUSINESS INTELLIGENCE
This section takes a step beyond databases and introduces students to data
warehousing, data warehousing tools, and data mining. These technologies
allow organizations to gain vast amounts of business intelligence.
LEARNING OUTCOMES
Learning Outcome 6.5: Identify the advantages of using business
intelligence to support managerial decision making.
Many organizations today find it next to impossible to understand their own
strengths and weaknesses, let alone their biggest competitors, due to
enormous volumes of organizational data being inaccessible to all but the
MIS department. Organization data include far more than simple structured
data elements in a database; the set of data also includes unstructured data
such as voice mail, customer phone calls, text messages, video clips, along
with numerous new forms of data, such as tweets from Twitter. Managers
today find themselves in the position of being data rich and information poor,
and they need to implement business intelligence systems to solve this
challenge.
Learning Outcome 6.6: De+ne data warehousing and data marts and
explain how they support business decisions.
A data warehouse is a logical collection of information, gathered from many
di$erent operational databases, that supports business analysis and decision
making. The primary value of a data warehouse is to combine information,
more specifically, strategic information, throughout an organization into a
single repository in such a way that the people who need that information
can make decisions and undertake business analysis. Extraction,
transformation, and loading (ETL) is a process that extracts information from
internal and external databases, transforms it using a common set of
enterprise definitions, and loads it into a data warehouse. The data
warehouse then sends portions (or subsets) of the information to data marts.
A data mart contains a subset of data warehouse information. To distinguish
between data warehouses and data marts, think of data warehouses as
having a more organizational focus and data marts as having a functional
focus.
Data mining is the process of analyzing data to extract information not
o$ered by the raw data alone. Data mining can also begin at a summary
information level (coarse granularity) and progress through increasing levels
of detail (drilling down), or the reverse (drilling up). Data mining occurs on
structured data that are already in a database or a spreadsheet.
Learning Outcome 6.7: De+ne the three organizational methods for
analyzing big data.
Unstructured data do not exist in a fixed location and can include text
documents, PDFs, voice messages, emails, and so on. Three common forms
for mining structured and unstructured data are cluster analysis, association
detection, and statistical analysis. Data mining, big data analytics, and data
visualization are the three methods organizations are using to dissect,
analyze, and understand organizational data. Data mining is the process of
analyzing data to extract information not offered by the raw data alone. Data
mining can also begin at a summary information level (coarse granularity)
and progress through increasing levels of detail (drilling down), or the
reverse (drilling up). Big data is a collection of large, complex data sets,
including structured and unstructured data, which cannot be analyzed using
traditional database methods and tools. Data visualization describes
technologies that allow users to see or visualize data to transform
information into a business perspective.
CLASSROOM OPENER
GREAT BUSINESS DECISIONS – Bill Inmon – The Father of
the Data Warehouse
Bill Inmon, is recognized as the "father of the data warehouse" and
co-creator of the "Corporate Information Factory." He has 35 years of
experience in database technology management and data warehouse
design. He is known globally for his seminars on developing data warehouses
and has been a keynote speaker for every major computing association and
many industry conferences, seminars, and tradeshows.
As an author, Bill has written about a variety of topics on the building, usage,
and maintenance of the data warehouse and the Corporate Information
Factory. He has written more than 650 articles, many of them have been
published in major computer journals such as Datamation, ComputerWorld,
DM Review and Byte Magazine. Bill currently publishes a free weekly
newsletter for the Business Intelligence Network, and has been a major
contributor since its inception. http://www.b-eye-network.com/home/
CLASSROOM EXERCISE
Analyzing Multiple Dimensions of Information
Jump! is a company that specializes in making sports equipment, primarily
basketballs, footballs, and soccer balls. The company currently sells to four
primary distributors and buys all of its raw materials and manufacturing
materials from a single vendor. Break your students into groups and ask
them to develop a single cube of information that would give the company
the greatest insight into its business (or business intelligence).
Product A, B, C, and D
Distributor X, Y, and Z
Promotion I, II, and III
Sales
Season
Date/Time
Salesperson Karen and John
Vendor Smithson
CLASSROOM EXERCISE
The Brain Behind The Big Bad Burger
The Brain Behind the Big, Bad Burger and Other Tales of Business
Intelligence
You will enjoy this one! It is an excellent article on the side of BI – but
seriously scary on the side of fast food. Be warned – you might never eat fast
food again!!
http://www.cio.com/article/109454/The_Brain_Behind_the_Big_Bad_Burger_an
d_Other_Tales_of_Business_Intelligence
Read the above article and discuss the following:
1. What does business intelligence really mean to a business?
2. What are the negative impacts of business intelligence?
3. How does a database and data warehouse support business intelligence?
4. Any other thoughts or insights you have into this chapter and this case
CLASSROOM EXERCISE
Mining Physician Data: Ethics and BI
Listen to the NPR story at:
http://www.npr.org/templates/story/story.php?storyId=11382945
Answer the following questions
1. Do you agree that mining physician data should be illegal? Why or why
not?
2. As a patient how do you feel about pharmaceutical companies mining
your doctor's data?
3. As an employee of one of the pharmaceutical companies how do you feel
about mining physician data?
CLASSROOM EXERCISE
Ask A Ninja.COM
If you need some insight into just about anything you can visit AskANija.com
http://askaninja.com/
I like to show this site to my students and ask why this site is so successful?
Why would people assume that just because someone is dressed as a Nija
they are knowledgeable about all subjects? This leads to a great discussion
on how can you validate business intelligence. How do you know the data or
analysis you are receiving is from a credible source? Can you prove the data
is complete, accurate, etc? Makes for an interesting classroom discussion.
CORE MATERIAL
The core chapter material is covered in detail in the PowerPoint slides. Each
slide contains detailed teaching notes including exercises, class activities,
questions, and examples. Please review the PowerPoint slides for detailed
notes on how to teach and enhance the core chapter material.

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