Transcript Business Intelligence Systems

Using MIS 2e
Chapter 9
Business Intelligence Systems
David Kroenke
© Pearson Prentice Hall 2009
9-1
Study Questions
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Q1 – Why do organizations need business intelligence?
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Q2 – What business intelligence systems are available?
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Q3 – What are typical reporting applications?
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Q4 – What are typical data-mining applications?
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Q5 – What is the purpose of data warehouses and data marts?

Q6 – What are typical knowledge-management applications?

Q7 – How are business intelligence applications delivered?
© Pearson Prentice Hall 2009
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Q1 – Why do organizations need business
intelligence?

Q2 – What business intelligence systems are available?

Q3 – What are typical reporting applications?

Q4 – What are typical data-mining applications?

Q5 – What is the purpose of data warehouses and data marts?

Q6 – What are typical knowledge-management applications?

Q7 – How are business intelligence applications delivered?
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Q1 – Why do organizations need business intelligence?
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Computers gather and store enormous amounts of data. 403
petabytes of new data were created in 2002.
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An estimated 2,500 petabytes, or 2.5 exabytes of new data were
generated in 2007.
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Business intelligence is comprised of information that contains
patterns, relationships, and trends about customers, suppliers,
business partners, and employees.
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Business intelligence systems process, store, and provide useful
information to users who need it, when they need it.
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Q1 – Why do organizations need business intelligence?
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This chart explains
the names and
amounts of computer
data measurements.
Fig 9-1 How Big is an Exabyte?
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Q1 – Why do organizations need business intelligence?
Q2 – What business intelligence systems are
available?

Q3 – What are typical reporting applications?

Q4 – What are typical data-mining applications?

Q5 – What is the purpose of data warehouses and data marts?

Q6 – What are typical knowledge-management applications?

Q7 – How are business intelligence applications delivered?
© Pearson Prentice Hall 2009
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Q2 – What business intelligence systems are available?
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A business intelligence (BI) system is an information system that
employs business intelligence tools to produce and deliver
information.
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Business intelligence tools are computer programs that implement a
particular BI technique. The techniques are categorized three ways:
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Reporting tools read data, process them, and format the data into
structured reports that are delivered to users. They are used primarily for
assessment.
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Data-mining tools process data using statistical techniques, search for
patterns and relationships, and make predictions based on the results
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Knowledge-management tools store employee knowledge, make it
available to whomever needs it. These tools are distinguished from the
others because the source of the data is human knowledge.
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Q2 – What business intelligence systems are available?
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It’s important that you understand the difference between these
business intelligence components:
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A BI tool is a computer program that implements the logic of a particular
procedure or process.
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A BI application uses BI tools on a particular type of data for a particular
purpose.
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A BI system is an information system that has all five components
(hardware, software, data, procedures, people) that delivers the results
of a BI application to users.
© Pearson Prentice Hall 2009
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
Q1 – Why do organizations need business intelligence?

Q2 – What business intelligence systems are available?

Q3 – What are typical reporting applications?

Q4 – What are typical data-mining applications?

Q5 – What is the purpose of data warehouses and data marts?

Q6 – What are typical knowledge-management applications?

Q7 – How are business intelligence applications delivered?
© Pearson Prentice Hall 2009
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Q3 – What are typical reporting applications?
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Reporting applications input data
from a source(s) and apply a
reporting tool to the data to
produce information. The
reporting system delivers the
information to users.
Basic reporting operations
include sorting, grouping,
calculating, filtering, and
formatting.
This figure shows raw data
before any reporting operations
are used.
Fig 9-2 Raw Sales Data
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Q3 – What are typical reporting applications?
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The figure on the left shows the raw sales data
sorted by customer names.
The figure on the right shows data that’s been
sorted and grouped.
Fig 9-3 Sales Data Sorted by Customer Name
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Fig 9-4 Sales Data, Sorted by Customer Name & Grouped
by Number of Orders & Purchase Amount
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Q3 – What are typical reporting applications?
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This figure shows even better information that’s been filtered and
formatted according to specific criteria.
Fig 9-5 Sales Data Filtered to Show Repeat Customers
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Q3 – What are typical reporting applications?
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RFM Analysis allows you to
analyze and rank customers
according to purchasing
patterns as this figure
shows.
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R = how recently a
customer purchased your
products
F = how frequently a
customer purchases your
products
M = how much money a
customer typically spends
on your products
The lower the score, the
better the customer.
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Fig 9-6 Example of RFM Score Data
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Q3 – What are typical reporting applications?
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Online Analytical Processing (OLAP) is more generic than RFM and
provides you with the dynamic ability to sum, count, average, and
perform other arithmetic operations on groups of data. Reports, also
called OLAP cubes, use
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Measures which are data items of interest. In the figure below a measure
is Store Sales Net .
Dimensions which are characteristics of a measure. In the figure below a
dimension is Product Family.
Fig 9-7 OLAP Product Family by Store Type
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Q3 – What are typical reporting applications?
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This figure shows how you can alter the format of a report to provide
users with the information they need to do their jobs.
Fig 9-8 OLAP Product Family & Store Location by Store Type
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Q3 – What are typical reporting applications?
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This figure shows how you can divide data into more detail by drilling
down through the data.
Fig 9-9 OLAP Product Family & Store Location by Store Type, Drilled Down to Show
Stores in California
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Q3 – What are typical reporting applications?
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OLAP servers are special products that read data from an
operational database, perform some preliminary calculations,
and then store the results in an OLAP database
Fig 9-10 Role of OLAP Server & OLAP Database
© Pearson Prentice Hall 2009
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Q1 – Why do organizations need business intelligence?

Q2 – What business intelligence systems are available?

Q3 – What are typical reporting applications?

Q4 – What are typical data-mining applications?

Q5 – What is the purpose of data warehouses and data marts?

Q6 – What are typical knowledge-management applications?
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Q7 – How are business intelligence applications delivered?
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Q4 – What are typical data-mining applications?
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Businesses use statistical techniques to find patterns and
relationships among data and use it for classification and prediction.
Data mining techniques are a blend of statistics and mathematics,
and artificial intelligence and machine-learning.
Fig 9-11 Convergence Disciplines for Data Mining
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Q4 – What are typical data-mining applications?
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There are two types of data-mining techniques:
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Unsupervised data-mining characteristics:
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No model or hypothesis exists before running the analysis
Analysts apply data-mining techniques and then observe the results
Analysts create a hypotheses after analysis is completed
Cluster analysis, a common technique in this category groups entities
together that have similar characteristics
Supervised data-mining characteristics:
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Analysts develop a model prior to their analysis
Apply statistical techniques to estimate parameters of a model
Regression analysis is a technique in this category that measures the impact
of a set of variables on another variable
Neural networks predict values and make classifications
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Q4 – What are typical data-mining applications?
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Market-Basket Analysis is a data-mining tool for determining sales
patterns. It helps businesses create cross-selling opportunities. Two
terms used with this type of analysis, and shown in the figure, are:
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Support—the probability that two items will be purchased together
Confidence—a conditional probability estimate
Fig 9-12 Market-Basket Example
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Q4 – What are typical data-mining applications?
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A decision tree is a hierarchical arrangement of criteria that predicts
a classification or value. It’s an unsupervised data-mining technique
that selects the most useful attributes for classifying entities on
some criterion. It uses if…then rules in the decision process. Here
are two examples.
Fig 9-13 Grades of Students from Past
MIS Class (Hypothetical Data)
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Fig 9-14 Credit Score Decision Tree
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Q1 – Why do organizations need business intelligence?

Q2 – What business intelligence systems are available?

Q3 – What are typical reporting applications?

Q4 – What are typical data-mining applications?

Q5 – What is the purpose of data warehouses
and data marts?

Q6 – What are typical knowledge-management applications?

Q7 – How are business intelligence applications delivered?
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Q5 – What is the purpose of data warehouses and data marts?
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Data warehouses and data marts address the problems companies have
with missing data values and inconsistent data. They also help standardize
data formats between operational data and data purchased from third-party
vendors.
These facilities prepare, store, and manage data specifically for data mining
and analyses.
Fig 9-15 Components of a Data Warehouse
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Q5 – What is the purpose of data warehouses and data marts?
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Granularity refers to whether
data are too fine or too coarse.
Clickstream data refers to the
clicking behavior of customers
on Web sites.
The phenomenon called the
curse of dimensionality—just
because you have more
attributes doesn’t mean you
have a more worthwhile
predictor.
© Pearson Prentice Hall 2009
Figure 9-16, left, lists some of the
data that’s readily available for
purchase from data vendors
Some of the problems companies
experience with operational data are
shown in figure 9-17 below.
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Q5 – What is the purpose of data warehouses and data marts?
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Here’s the difference between a data warehouse and a data mart:
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A data warehouse stores operational data and purchased data. It cleans
and processes data as necessary. It serves the entire organization.
A data mart is smaller than a data warehouse and addresses a particular
component or functional area of an organization.
Fig 9-18 Data Mart Examples
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Q1 – Why do organizations need business intelligence?

Q2 – What business intelligence systems are available?

Q3 – What are typical reporting applications?

Q4 – What are typical data-mining applications?

Q5 – What is the purpose of data warehouses and data marts?

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Q6 – What are typical knowledge-management
applications?
Q7 – How are business intelligence applications delivered?
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Q6 – What are typical knowledge-management applications?
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The characteristics and goals of knowledge management applications and
systems are to
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Create value for an organization from its intellectual capital
Share knowledge among and between employees, managers, suppliers, and
customers
Include knowledge that is known to exist in documents or employees’ brains
Foster innovation by encouraging the free flow of ideas
Improve customer service by streamlining response times
Boost revenues by getting products and services to market faster
Enhance employee retention rates by recognizing the value of employees’
knowledge and rewarding them for it
Streamline operations and reduce costs by eliminating redundant or unnecessary
processes
Preserve organizational memory by capturing and storing lessons learned and the
best practices of key employees.
The three major categories of knowledge assets are data, documents, and
employees.
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Q6 – What are typical knowledge-management applications?
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Two key technologies for sharing content in KM systems are:
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Indexing—the single most important content function in KM
applications. It’s an easily accessible and robust means of
determining if content exists and includes a link to obtain the
content. It’s used in conjunction with search functions.
RSS, Real Simple Syndication—a standard for subscribing to
content sources on Web sites. It uses an RSS Reader program
that helps users
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subscribe to content sources.
periodically check sources for new or updated content through RSS
feeds.
place content summaries in an RSS inbox with a link to the full
content.
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Q6 – What are typical knowledge-management applications?
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This figure shows a typical RSS reader. The left pane shows RSS
sources. Entries are grouped into categories predetermined by the
user.
Fig 9-19 Interface of a Typical RSS Reader
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Q6 – What are typical knowledge-management applications?
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Blogs provide an easy way to share knowledge as seen in this
figure. You can use RSS feeds to subscribe to thousands of blogs.
Fig 9-20 Blog Posts of SharePoint Team Member
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Q6 – What are typical knowledge-management applications?
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Another form of knowledge management are expert systems.
Here are characteristics about them along with some of their
problems:
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They capture human expertise and format it for use by
nonexperts.
They are rule-based systems that use if…then rules.
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They gather data from people rather than using data-mining
techniques.
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They are difficult and expensive to develop.
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They are difficult to maintain because the rules are constantly
changing.
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They have been unable to live up to the high expectations set by
their name.
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Q6 – What are typical knowledge-management applications?
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This is an example of the output from a medical expert system that
is part of a decision support system. Based on the system’s rules,
an alert is issued if the system detects a problem with a patient’s
prescriptions.
Fig 9-21 Alert from Pharmacy Clinical Decision Support System
© Pearson Prentice Hall 2009
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
Q1 – Why do organizations need business intelligence?

Q2 – What business intelligence systems are available?

Q3 – What are typical reporting applications?

Q4 – What are typical data-mining applications?

Q5 – What is the purpose of data warehouses and data marts?

Q6 – What are typical knowledge-management applications?

Q7 – How are business intelligence applications
delivered?
© Pearson Prentice Hall 2009
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Q7 – How are business intelligence applications delivered?
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This figure shows the components of a generic BI system. A BI
application server delivers results in a variety of formats to devices
for consumption by BI users. A BI server provides two functions:
management and delivery.
Fig 9-22 Components of Generic Business Intelligence System
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Q7 – How are business intelligence applications delivered?
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The management function of a BI server maintains metadata
about the authorized allocation of BI results to users. It tracks
what results are available, who is authorized to view them,
and when the results are provided to users. Here are options
for managing BI results:
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Users can pull their results from a Web site using a portal server
with a customizable user interface.
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A server can automatically push information to users through
alerts which are messages announcing events as they occur.
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A report server, a special server dedicated to reports, can supply
users with information.
© Pearson Prentice Hall 2009
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Q7 – How are business intelligence applications delivered?
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This figure shows a portal that provides common data to users. It
can be used to help companies manage their knowledge.
Fig 9-23 Sample Portal, Provided by iGoogle
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Q7 – How are business intelligence applications delivered?
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Here are the characteristics of the delivery function of a BI
server:
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It tracks authorized users.
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It tracks the schedule for providing results to users.
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It uses exception alerts that notify users of an exceptional event.
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The procedures used depends on the nature of the BI system.
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Procedures tend to be more flexible than those in an operational
system because users of a BI system tend to be engaged in work
that is neither structured nor routine.
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Procedures are determined by unique requirements of users.
© Pearson Prentice Hall 2009
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