Transcript Using Management Information Systems

Using Management Information Systems
David Kroenke
Business Intelligence and Knowledge Management
Chapter 9
© 2007 Prentice Hall, Inc.
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Learning Objectives
Understand the need for business
intelligence systems.
Know the characteristics of reporting
systems.
Know the purpose and role of data
warehouses and data marts.
Understand fundamental data-mining
techniques.
Know the purpose, features, and functions
of knowledge management systems.
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The Nature of Intelligence
Some of the characteristics of intelligent behavior include the ability to
do the following:
Learn from experience including the ability to learn by trial and error
Apply knowledge acquired from experience to another situation
Handle complex situations
Solve problems when important information is missing is the essence of
decision making when dealing with uncertainty
Determining what is important is a mark of a good decision maker
The ability to reason and think
Reacting quickly and correctly to a new situation
Understand and interpret visual images including processing and
manipulating symbols
Being creative and imaginative
Using heuristics, or rules of thumb, or even guesses in making
decisions
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What is AI?
Artificial Intelligence systems include people,
procedures, hardware, software, data and knowledge
needed to develop computer systems and machines
that demonstrate characteristics of intelligence [Ralph
Stair].
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Components of AI
Expert Systems are computer programs that act or behave like a
human expert in a field or area.
Robotics involves developing mechanical or computer devices
controlled by software to perform tasks that require a high degree of
precision or are tedious or hazardous for humans
Vision Systems include hardware and software that permit computers
to capture, store and manipulate visual images and pictures
Natural Language Processing allows the computer to understand and
react to statements and commands made in a "natural" language, such
as English
Learning Systems include hardware and software that allow the
computer to change how it functions or reacts to situations based on
feedback it receives
Neural Networks are computer systems that act like or simulate the
functioning of the human brain
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Expert Systems
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Expert systems are created by interviewing experts in a given business
domain and codifying the rules stated by those experts.
Many expert systems were created in the late 1980s and 1990s, and
some of them have been successful.
Expert systems suffer from three major disadvantages.
 They are difficult and expensive to develop.
 They are difficult to maintain.
 They were unable to live up to the high expectations set by their
name.
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The Need for Business Intelligence Systems
According to a study done at the University of
California at Berkeley, a total of 403 petabytes of
new data were created in 2002.
403 petabytes is roughly the amount of all printed
material ever written.
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The printed collection of the Library of Congress is .01
petabytes.
400 petabytes equals 40,000 copies of the print
collection of the Library of Congress.
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Business Intelligence Systems
The purpose of a business intelligence (BI)
system is to provide the right information, to the
right user, at the right time.
BI systems help users accomplish their goals and
objectives by producing insights that lead to
actions.
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Business Intelligence Tools
Tools for searching business data in an attempt to find patterns is
called business intelligence (BI) tools.
The processing of data is simple:Data are sorted and grouped and
simple totals and averages are calculated.
Reporting tools are used to address questions like:
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What has happened in the past?
What is the current situation?
How does the current situation compare to the past?
Data-mining tools process data using statistical techniques, many of
which are sophisticated and mathematically complex.
Data mining involves searching for patterns and relationships among
data.
In most cases, data-mining tools are used to make predictions.

For example, we can use one form of analysis to compute the
probability that a customer will default on a loan.
Data-mining tools use sophisticated techniques.
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Data Mining
Data mining is the application of statistical techniques to find patterns
and relationships among data and to classify and predict.
Data mining represents a convergence of disciplines.
Data-mining techniques emerged from statistics and mathematics and
from artificial intelligence and machine-learning fields in computer
science.
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Unsupervised Data Mining
With unsupervised data mining, analysts do not
create a model or hypothesis before running the
analysis.
Instead, they apply the data-mining technique to
the data and observe the results.
Analysts create hypotheses after the analysis to
explain the patterns found.
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Supervised Data Mining
With supervised data mining, data miners
develop a model prior to the analysis and apply
statistical techniques to data to estimate
parameters of the model.
One such analysis, which measures the impact of
a set of variables on another variable, is called a
regression analysis.
Neural networks are another popular supervised
data-mining technique used to predict values and
make classifications such as “good prospect” or
“poor prospect” customers.
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Data Warehouses and Data Marts
Basic reports and simple OLAP analyses can be
made directly from operational data.
Many organizations choose to extract operational
data into facilities called data warehouses and
data marts, both of which are facilities that
prepare, store, and manage data specifically for
data mining and other analyses.
Programs read operational data and extract,
clean, and prepare that data for BI processing.
The prepared data are stored in a data-warehouse
database using data-warehouse DBMS, which can
be different from the organization’s operational
DBMS.
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Data Warehouses Versus Data Marts
A data mart is a data collection, smaller than the
data warehouse, that addresses a particular
component or functional area of the business.
The data warehouse is like the distributor in the
supply chain and the data mart is like the retail
store in the supply chain.
Users in the data mart obtain data that pertain to a
particular business function from the data
warehouse.
It is expensive to create, staff, and operate data
warehouses and data marts.
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Problems with Operational Data
Most operational and purchased data have
problems that inhibit their usefulness for business
intelligence.
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Decision Trees
A decision tree is a hierarchical arrangement of
criteria that predict a classification or a value.
Decision tree analyses are an unsupervised datamining technique.
The analyst sets up the computer program and
provides the data to analyze, and the decision tree
program produces the tree.
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A Decision Tree for Loan Evaluation
A common business application of decision trees is to
classify loans by likelihood of default.
Organizations analyze data from past loans to produce a
decision tree that can be converted to loan-decision rules.
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A financial institution could use such a tree to assess the default
risk on a new loan.
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Reporting Systems
The purpose of a reporting system is to create
meaningful information from disparate data
sources and to deliver that information to the
proper user on a timely basis.
Reporting systems generate information from data
as a result of four operations:
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Filtering data
Sorting data
Grouping data
Making simple calculations on the data
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Components of Reporting Systems
A reporting system maintains a database of
reporting metadata.
The metadata describes the reports, users,
groups, roles, events, and other entities involved
in the reporting activity.
The reporting system uses the metadata to
prepare and deliver reports to the proper users on
a timely basis.
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Report Type
In terms of a report type, reports can be static or
dynamic.
Static reports are prepared once from the
underlying data, and they do not change.
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Example, a report of past year’s sales
Dynamic reports: the reporting system reads the
most current data and generates the report using
that fresh data.
Examples are: a report on sales today and a report on
current stock prices
Query reports are prepared in response to data entered
by users.
Online analytical processing (OLAP) reports allow the user
to dynamically change the report grouping structures.
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Report Media
Reports are delivered via many different report media or
channels.
Some reports are printed on paper, and others are created
in a format like PDF whereby they can be printed or viewed
electronically.
Other reports are delivered to computer screens.
Companies sometimes place reports on internal corporate
Web sites for employees to access.
Another report medium is a digital dashboard, which is an
electronic display customized for a particular user.
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Vendors like Yahoo! and MSN provide common examples.
Users of these services can define content they want- say, a local
weather forecast, a list of stock prices, or a list of news sources.
The vendor constructs the display customized for each user.
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Digital Dashboard Example
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RFM Analysis
RFM analysis is a way of analyzing and ranking customers according
to their purchasing patterns.
It is a simple technique that considers how recently (R) a customer has
ordered, how frequently (F) a customer orders, and how much money
(M) the customer spends per order.
To produce an RFM score, the program first sorts customer purchase
records by the date of their most recent (R) purchase.
In a common form of this analysis, the program then divides the
customers into five groups and gives customers in each group a score
of 1 to 5.
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The top 20% of the customers having the most recent orders are given an
R score 1 (highest).
The program then re-sorts the customers on the basis of how
frequently they order.
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The top 20% of the customers who order most frequently are given a F
score of 1 (highest).
Finally the program sorts the customers again according to the amount
spent on their orders.
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The 20% who have ordered the most expensive items are given an M
score of 1 (highest).
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Online Analytical Processing
Online analytical processing (OLAP) provides the ability to sum,
count, average, and perform other simple arithmetic operations on
groups of data.
The remarkable characteristics of OLAP reports is that they are
dynamic.
The viewer of the report can change the report’s format, hence, the
term online.
An OLAP report has measures and dimensions.
A measure is the data item of interest.
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It is the item that is to be summed or averaged or otherwise
processed in the OLAP report.
A dimension is a characteristic of a measure.
Purchase data, customer type, customer location, and sales region
are all examples of dimension.
With an OLAP report, it is possible to drill down into the data.
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This term means to further divide the data into more detail.
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Market-Basket Analysis
A market-basket analysis is a data-mining
technique for determining sales patterns.
A market-basket analysis shows the products that
customers tend to buy together.
In market-basket terminology, support is the
probability that two items will be purchased
together.
You can expect market-basket analysis to become
a standard CRM analysis during your career.
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Knowledge Management
Knowledge management systems concern the sharing of knowledge
that is already known to exist, either in libraries of documents, in the
heads of employees, or in other known sources.
Knowledge management (KM) is the process of creating value from
intellectual capital and sharing that knowledge with employees,
managers, suppliers, customers, and others who need that capital.
Knowledge management is a process that is supported by the five
components of an information system.
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Its emphasis is on people, their knowledge, and effective means for
sharing that knowledge with others.
The benefits of KM concern the application of knowledge to enable
employees and others to leverage organizational knowledge to work
smarter.
KM preserves organizational memory by capturing and storing the
lessons learned and best practices of key employees.
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Content Management Systems
Content management systems are information systems that track
organizational documents, Web pages, graphics, and related materials.
KM content management systems are concerned with the creation,
management, and delivery of documents that exist for the purpose of
imparting knowledge.
Typical users of content management systems are companies that sell
complicated products and want to share their knowledge of those
products with employees and customers.
The basic functions of content management systems are the same as
for report management systems: author, manage, and deliver.
The only requirement that content managers place on document
authoring is that the document has been created in a standardized
format.
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Content Delivery
Almost all users of content management systems pull the
contents.
Users cannot pull content if they do not know it exists.
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The content must be arranged and indexed, and a facility for
searching the content devised.
Documents that reside behind a corporate firewall, however,
are not publicly accessible and will not be reachable by
Google or other search engines.
Organizations must index their own proprietary documents and
provide their own search capability for them.
Web browsers and other programs can readily format content expressed
in HTML, PDF, or another standard format.
XML documents often contain their own formatting rules that browsers
can interpret.
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The content management system will have to determine an appropriate
format for content expressed in other ways.
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KM Systems to Facilitate the Sharing of Human
Knowledge
Nothing is more frustrating for a manager to
contemplate than the situation in which one employee
struggles with a problem that another employee knows
how to solve easily.
KM systems are concerned with the sharing not only
of content, but also with the sharing of knowledge
among humans.
How can one person share her knowledge with another?
 How can one person learn of another person’s great idea?
Three forms of technology are used for knowledge- sharing
among humans:
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Portals, discussion groups, and email
Collaborations systems
Expert systems
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Ethics Guide–The Ethics of Classification
Classification is a useful human skill.
Sorting and classifying are necessary, important,
and essential activities.
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But those activities can also be dangerous
Serious ethical issues arise when we classify
people.
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What makes someone a good or bad “prospect”?
If we’re talking about classifying customers in order to
prioritize our sales calls, then the ethical issue may not
be too serious.
What about classifying applicants for college?
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