ch09_summer - Gonzaga Student Web Server

Report
Chapter 9
Business Intelligence Systems
Jason C. H. Chen, Ph.D.
Professor of MIS
School of Business Administration
Gonzaga University
Spokane, WA 99258
[email protected]
Dr. Chen, Management Information Systems
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“We’re Sitting On All This Data. I Want to
Make It Pay.”
Anne wants membership data to:
• Combine membership data and publicly
available data
• Enable target marketing
• Increase wedding revenue
Dr. Chen, Management Information Systems
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Study Questions
Q1: How do organizations use business intelligence (BI)
systems?
Q2: What are the three primary activities in the BI process?
Q3: How do organizations use data warehouses and data marts
to acquire data?
Q4: How do organizations use typical reporting applications?
Q5: How do organizations use typical data mining
applications?
Q6: What is the role of knowledge management systems?
Q7: What are the alternatives for publishing business
intelligence?
Q8: 2022?
Dr. Chen, Management Information Systems
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BUSINESS INTELLIGENCE
• Business intelligence – information that
people use to support/improve their
decision-making efforts
• Principle BI enablers include:
– Technology
– People
– Culture
Dr. Chen, Management Information Systems
Working Smarter , Not Just Harder
• Overlapping Human/Organizational (Culture, Process)/
Technological factors in BI/KM:
PEOPLE
Knowledge
ORGANIZATIONAL
PROCESSES
TECHNOLOGY
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N
CRM and BI Example
•
•
•
•
A Grocery store in U.K. with the following “patterns” found:
Every Thursday afternoon
Young Fathers (why?) shopping at store
Two of the followings are always included in their shopping list
– Diapers and
– Beers
• What other decisions should be made as a store manager (in terms
of store layout)?
• Short term vs. Long term
– This is an example of cross-selling
– Other types of promotion: up-sell, bundled-sell
• IT (e.g., BI) helps to find valuable information then decision
makers make a timely/right decision for improving/creating
competitive advantages.
Dr. Chen, Management Information Systems
Q/A
• Can the “patterns” in the grocery store
example be produced from its Database?
• Y/N
• Why?
• It only can be produced from its “Data
Warehouse” using a kind of “data mining”
software.
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Q1: How Do Organizations Use Business
Intelligence (BI) Systems?
• Information systems generate enormous amounts of
operational data that contain patterns, relationships,
clusters, and trends about customers, suppliers, business
partners, and employees that can facilitate management,
especially planning and forecasting.
• Business intelligence (BI) systems produce such
information from operational data.
• Data communications and data storage are essentially free,
enormous amounts of data (Big Data) are created and
stored every day.
 12,000 gigabytes per person of data, worldwide in 2009
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Dr. Chen, Management Information Systems
Why do organizations need business intelligence?
• BI systems are computer programs provide valuable
information for decision making.
• Three primary BI systems:
Reporting tools read data, process them, and format the data
– __________
into structured reports (e.g., sorting, grouping, summing, and
averaging) that are delivered to users. They are used primarily
for assessment. RFM is one of the tool for reporting.
Data-miningtools process data using statistical, regression,
– ___________
decision tree, and market basket techniques to discover hidden
patterns and relationships, and make predictions based on the
results
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.
Dr. Chen, Management Information Systems
[1]
[2]
[3]
Fig 9-1: Structure of a Business Intelligence System
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Q/A
Which of the following is true of source data
for a BI system?
A) It refers to the organization's metadata.
B) It refers to data that the organization purchases from
data vendors.
C) It refers to the level of detail represented by the data.
D) It refers to the hierarchical arrangement of criteria that
predict a classification or a value.
Answer: B
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Tools vs. Applications vs. Systems
• BI tool (e.g., decision-tree analysis) is one or
more computer programs. BI tools implement
the logic of a particular procedure or process.
• BI application is the use of a tool on a particular
type of data for a particular purpose.
• BI system is an information system having all
five components (what are they?) that delivers
results of a BI application to users who need
those results.
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Dr. Chen, Management Information Systems
Example Uses of Business Intelligence
[4]
[3]
[2]
(Decision Support
Systems)
[1]
Fig 9-2:Example Uses of Business Intelligence
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Q2: What Are the Three Primary Activities in
the Business Intelligence Process?
• The primary activities in the BI process are:
Data acquisition
– 1. ______________
• The process of obtaining, cleaning, organizing relating,
and cataloging source data.
BI analysis
– 2. __________
• The process of creating BI analysis: reporting, data
mining, and knowledge management.
Publish results
– 3. ____________
• The process of delivering BI to the knowledge workers
who need it.
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What Are the Three Primary Activities in the
Business Intelligence Process?
I
P
O
[1]
[2]
[3]
The principle is the same as the “simple” model we learned before. What is it?
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Fig 9-3: Three Primary Activities in the BI Process
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Using BI for Problem-solving at GearUp:
Process and Potential Problems
1.
2.
3.
4.
5.
6.
Obtain commitment from vendor
Run sales event
Sell as many items as possible
Order amount actually sold
Receive partial order and damaged items
If received less than ordered, ship partial
order to customers
7. Some customers cancel orders
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Tables Used for BI Analysis at GearUp
Fig 9-4: Tables Used for BI Analysis at GearUp
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GearUp Analysis: Item Summary and
Lost Sales Summary Reports
Fig 9-5: Extract of the Item_Siummary_Data
Fig 9-6: Lost Sales Summary Report
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Short and Damaged Shipments Details Report
Fig 9-7: Lost Sales Detail Report
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Publish Results
Options
• Print and distribute via email or
collaboration tool
• Publish on Web server or SharePoint
• Publish on a BI server
• Automate results via Web service
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3: How Do Organizations Use Data Warehouses
and Data Marts to Acquire Data?
• Why extract operational data for BI
processing?
 Security and control
 Operational not structured for BI analysis
 BI analysis degrades operational server performance
T/F: Placing BI applications on operational servers can
dramatically increase system performance.
Answer: FALSE
Operational data is structured for fast and reliable “transaction
processing” (e.g., payroll).
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Data Base, Data Warehouse and Data Marts
• Data base: An organized collection of logically
related (current) data files.
• Data Warehouse: A data warehouse stores data
from current and previous years (historical
data) that have been extracted from the various
operational and management database of an
organization.
• Data mart: a subset of data warehouse that
holds specific subsets of data for one particular
functional area or project.
Dr. Chen, Management Information Systems
Components of a Data Warehouse

operational
data

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.
ETL
ETL: Extract, Transformation, Load
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Fig 9-11 Components of a Data Warehouse
Data Marts and the Data Warehouse
Legacy
systems feed
data to the
warehouse.
The
warehouse
feeds
specialized
information
to
departments
(data marts).
ETL: Extract,
Transformation,
Load
Dr. Chen, Management Information Systems
Legacy Systems
Finance
Data Mart
Sales
Data Mart
Operational Data
Store
Marketing
Data Mart
ETL
Operational Data
Store
ETL
Operational Data
Store
Operational Data
Store
Organizational
Data
Warehouse
Accounting
Data Mart
Examples of Consumer Data that Can Be Purchased
Fig 9-12 Examples of Consumer Data for Sale
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Possible Problems with Source (Operational) Data
Fig 9-13 Possible Problems with Source (Operational) Data
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Information Cleansing or Scrubbing
• Standardizing Customer name from Operational Systems
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Data Warehouses vs. Data Marts

Here’s the difference between a data warehouse and a data mart:
 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 9-14 Data mart Examples
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4. How Do Organizations Use Typical
Reporting Applications
• Four Basic operations:
1. Sorting
2. Filtering
3. Grouping
4. Calculating
5. Formatting
•
We will use a ‘reporting application’ to
analyze and rank customers based on their
purchasing patterns to help company make
better decision for increasing company’s
revenue.
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What are typical reporting applications?
• RFM Analysis allows you to analyze and rank customers
according to purchasing patterns as this figure shows.
– Recency: How recently a customer purchased items? => leads and
opportunities
– Frequency: How frequently a customer purchased items? =>
retention
– Monetary Value: How much a customer spends on each purchase?
=> profitability
• RFM Analysis
– Sort the data by date (for recency), times (for frequency), and
purchase amount (for money), respectively
– Divide the sorted data into five groups
– Assign 1 to top 20%, 2 to next 20%, 3 to the third 20%, 4 to the fourth
20% and 5 to the bottom 20%.
– The lower the score, the better the customer.
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What does RFM analysis Tell?
Example RFM Scores
• RFM Analysis allows you to
analyze and rank customers
according to purchasing
patterns as this figure shows.
– 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, and,
consequently, the more profit
the company will be.
Dr. Chen, Management Information Systems
Fig 9-15 Example of RFM Score Data
Interpreting RFM Score Results
• Ajax has ordered recently and orders
frequently. M score of 3 indicates it does not
order most expensive goods.
 A good and regular customer but need to
attempt to up-sell more expensive goods to
Ajax
• Bloominghams has not ordered in some time,
but when it did, ordered frequently, and orders
were of highest monetary value.
 May have taken its business to another vendor.
Sales team should contact this customer
immediately.
• Caruthers has not ordered for some time; did
not order frequently; did not spend much.
 Sales team should not waste any time on this
customer.
• Davidson in middle
 Set up on automated contact system or use the
Davidson account as a training exercise
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80/20 Rule (Pareto Principle)
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Q/A
U.S. Grocery Corp. is a large grocery chain store.
FOODFARM, one of the customers of U.S. Grocery Corp.
holds an RFM score of 111. Which of the following
characteristics relates FOODFARM with its RFM score?
A) FOODFARM has ordered recently and orders frequently, but it orders the
least expensive goods.
B) FOODFARM has not ordered in some time, but when it did order in the past
it ordered frequently, and its orders were of the highest monetary value.
C) FOODFARM has not ordered for some time, it did not order frequently, and,
when it
did order, it bought the least-expensive items.
D) FOODFARM has ordered recently and orders frequently, and it orders the
most expensive goods.
Answer: D
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OLAP and its Applications
• Online Analytical Processing (OLAP), a second
type of reporting tool, is more generic than RFM.
• OLAP provides you with the dynamic ability to
sum, count, average, and perform other arithmetic
operations on groups of data. Reports, also called
OLAP cubes.
• What software and function that enable you to
create OLAP and its applications?
• ANSWER
– EXCEL with
– Pivot table
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Online Analytical Processing (OLAP)
• Online Analytical Processing (OLAP) cubes, use
– 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 9-16 Example Grocery Sales OLAP Report
OLAP Product Family by Store Type
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Example Expanded Grocery Sales OLAP Report
Figure 9-17
Fig 9-17: Example of Expanded Grocery Sales OLAP Report
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Example of Drilling Down into Expanded Grocery Sales OLAP Report
Fig 9-18: Example of Drilling Down into Expanded Grocery Sales OLAP Report
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Fig 9 (Extra): Role of OLAP Server & OLAP Database

OLAP servers are special products that 1) read data from an
operational database, 2) perform some preliminary
calculations, and then3) store the results in an OLAP database
Third-party vendors provide software for more extensive graphical displays
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On-Line Analytic Processing (OLAP)
• Enables mangers and analysts to interactively examine
and manipulate large amounts of detailed and
consolidated data from different dimensions.
• Analytical Processing:
– Drill-up (Consolidation) – ability to move from detailed data
to aggregated data
• Profit by Product >>> Product Line >>> Division
– Drill-down – ability to move from summary/general to
lower/specific levels of detail
• Revenue by Year >>> Quarter >>>>Week >>>Day
– Slice and Dice – ability to look across dimensions
• Sales by Region Sales
• Profit and Revelers by Product Line
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Slicing a data cube
REGION
CUSTOMER
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Data Base, Data Warehouse and Data Marts
• Data base: An organized collection of
logically related (current) data files.
• Data Warehouse: A data warehouse stores
data from current and previous years
(historical data) that have been extracted
from the various operational and
management database of an organization.
• Data mart: a subset of data warehouse that
holds specific subsets of data for one
particular functional area or project.
Dr. Chen, Management Information Systems
Database vs. Datawarehouse
DBMS
???
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Database
Datawarehouse
Database vs. Datawarehouse
DBMS
Database
Data Mining
Datawarehouse
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How do BI Tools Obtain Data?
Store A
Operational/
external DB
Data reconciliation
process
Data warehouse
Data marts
Decision support
ETL
Extraction
Basic Reporting
Metadata
Data
mart
Transformation
RFM Analysis
Store B
OLAP
Cleansing
Loading
Summarization
Database
in Data
Warehouse
Data mining
Data
mart
Web clickstream
Online
Data
Web analytics
Data
mart
ETL: Extract,
Transformation, Load
Dr. Chen, Management Information Systems
Data-mining Applications

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 (AI) and machine-learning.
Data Warehouse
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Fig 9-19 Data Mining Origins
Unsupervised vs. Supervised Data Mining
• Data mining is an automated process of discovery and extraction of hidden
and/or unexpected patterns of collected data in order to create models for
decision making that predict future behavior based on analyses of past
activity.
• There are two types of data-mining techniques:
– Unsupervised data-mining characteristics:
•
•
•
•
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 (and decision tree), a common technique in this category
groups entities together that have similar characteristics
– Supervised data-mining characteristics:
• Analysts develop a model prior to their analysis
• Apply statistical techniques such as Market Basket Analysis 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.
• Used for making predictions
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Unsupervised vs. Supervised Data Mining
Unsupervised
Supervised
•No model before running
analysis
•Hypotheses created after
analysis
•Cluster analysis to find
groups
•Model created before
analysis
•Hypotheses created
before analysis
•Regression analysis:
make predictions
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Neural Networks
• Used for predicting values and making
classifications
• Complicated set of nonlinear equations
• Go to http://kdnuggets.com and search for
“neural network”
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Probability for BI –
Market Basket Analysis
(Upselling and Cross-selling)
Support - The probability of two items
(A&B) will be purchased together.
P(A&B) = P(A&B)/Total # of transactions
Confidence - Conditional probability is the
probability that an event (A) will occur, when
another event (B) is known to occur or to have
occurred. If the events are A and B respectively,
this is said to be "the probability of A given B.
P(A | B) = P(A&B)/P(B)
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Market Basket
Analysis at a
Dive Shop
(Total # of
Transactions
(TOT)= 400)
#times an item will be purchased when a customer entering the store
(s1) Purchase Mask and Fins together,
A: Fins
B: Mask
P(A&B)/TOT
P(Fins & Mask)
= 250/400=0.625
P(A | B) =
P(A&B)/P(B)
(c1) Proportion of customers who bought a mask also
bought fins (buying fins given s/he bought mask)
P(Fins | Mask)=
P(Fins&Mask)/P(
Mask)=250/270 =
.926
P(A | B) /P(A)
P(fins |
mask)/P(fins)=
confidence/base
probability=.92
6/.7=1.32
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the lift of fins and mask
Fig 9-20 Market-Basket Analysis at a Dive Shop
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
Market-Basket Analysis is a supervised data-mining tool for determining sales patterns. It
helps businesses create cross-selling opportunities (i.e., buying relevant products together).
Three terms used with this type of analysis are:



Support: the probability that two items will be purchased together (e.g., Fins and Mask will be
purchased together)
Confidence: a conditional probability estimate (e.g., proportion of the customers who bought a
mask also bought fins)
Lift: ratio of confidence to the base probability (e.g., ratio between customers of buying fins after
buying mask and those buying fins of walking into the store). It shows that how much the based
probability increases or decreases when other products are purchased.
Total # of transactions (TOT) = 400 times
Probability of an item that customer will purchase: P(A)/TOT, e.g.,
e.g., (e1) probability of customers entering into the store and buying mask is P(Mask)=270/400=0.675
(e2) probability of customers entering into the store and buying fins is P(Fins)=280/400=0.7
A: Fins; B: Mask
Support : P (A&B)/TOT
e.g., (s1) Purchase Mask and Fins together, P(Fins & Mask) = 250/400=0.625
(s2) Purchase Tank and Dive computer together: P(Tank & Dive computer)=30/400=0.075
Confidence: P(A | B) = P(A&B)/P(B)
e.g., (c1) Proportion of customers who bought a mask also bought fins (buying fins given s/he bought
mask)
P(Fins | Mask)= P(Fins&Mask)/P(Mask)=250/270=0.926
We then compare (e2) and (c1) if someone buys a mask, the likelihood that he or she will also buy fins
increases substantially from .7 to .926.
Q: If you are a store manager, how will you train your sales personnel?
A: Train them to try to sell fins to anyone buying a mask. (________
Cross- selling).
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Dr. Chen, Management Information Systems

(continue) Market-Basket Analysis is a supervised data-mining tool for determining sales
patterns. It helps businesses create cross-selling opportunities (i.e., buying relevant
products together). Three terms used with this type of analysis are:



Support: the probability that two items will be purchased together (e.g., Fins and Mask will be
purchased together)
Confidence: a conditional probability estimate (e.g., proportion of the customers who bought a
mask also bought fins)
Lift: ratio of confidence to the base probability (e.g., ratio between customers of buying fins after
buying mask and those buying fins of walking into the store). It shows that how much the based
probability increases or decreases when other products are purchased
e.g., (e2) probability of customers entering into the store and buying fins is P(Fins)=280/400=0.7
Confidence (cont.) P(A | B) = P(A&B)/P(B)
e.g., (c2) Proportion of customers who bought a dive computer also bought fins (i.e., buying fins, given
she or he bought a dive computer)
P(Fins | Dive computer)= P(Fins&Dive computer)/P(Dive computer)=20/120=0.167
Thus, someone buys a dive computer, the likelihood that she will also buy fins falls from 0.7 to 0.167
Q: If you are a store manager, how will you take any action on these types of customers?
NO
A: ______
Lift : P(A | B) /P(A)
e.g., the lift of fins and mask, P(fins | mask)/P(fins)=confidence/base probability=.926/.7=1.32.
Thus, the likelihood that people buy fins when they buy a mask increases by 32 percent.
Surprisingly, it turns out the lift of fins and a mask is the same as the lift of a mask and fins. Both are 1.32
Please note that this analysis only shows shopping carts with two items. We cannot say from this data
what the likelihood is that customer, given that they bought a mask, will buy both weights and fins 52
Dr. Chen, Management Information Systems
Q/A
In marketing transactions, the fact that customers
who buy product X also buy product Y creates a(n)
________ opportunity. That is, "If they're buying
X, sell them Y," or "If they're buying Y, sell them
X."
A) cross-selling
B) value added selling
C) break-even
D) portfolio
Answer: A
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Decision Tree Example for MIS Classes (hypothetical data)
• 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.
If student is a junior and works in a
restaurant, then predict grade > 3.0
If student is a senior and is a nonbusiness
major, then predict grade <
--- 3.0
If student is a junior and does not work in
a restaurant, then predict grade < 3.0
--If student is a senior and is a business
major, then make no prediction
Fig 9-21 Decision Tree Examples for
MIS Class (Hypothetical Data)
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Summary of Decision Tree Analysis
• 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 9-21 Decision Tree Examples for
MIS Class (Hypothetical Data)
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Fig 9-22 Credit Score Decision Tree
A Decision Tree for a Loan Evaluation
•
•
•
•
Classifying likelihood of default
Examined 3,485 loans
28 percent of those defaulted
Evaluation criteria
A. Percentage of loan past due less than 50 percent =
.94, no default
B. Percentage of loan past due greater than 50 percent
= .89, default
• Subdivide groups A and B each into three
classifications: CreditScore, MonthsPastDue, and
CurrentLTV
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A Decision Tree for a Loan Evaluation
Resulting rules
• If the loan is more than half paid, then accept the loan.
• If the loan is less than half paid and
 If CreditScore is greater than 572.6 and
• If CurrentLTV is less than .94, then accept the loan.
• Otherwise, reject the loan.
• Use this analysis to structure a marketing campaign to
appeal to a particular market segment
• Decision trees are easy to understand and easy to
implement using decision rules.
• Some organizations use decision trees to select variables
to be used by other types of data-mining tools.
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Fig 9-22: Credit Score Decision Tree
more than half paid (Accepted)
or
less than half paid
and
Figure CE14-4
otherwise
reject the loan.
Dr. Chen, Management Information Systems
Accepted
What are typical data-mining applications?
DM Capabilities
Description
Discover rules that
Associations/Affinity
correlate one set of
(Supervised):
Association between items events or items with
another set of events
or items.
Relate events in time
Sequence/Temporal
based on a series of
Patterns (Supervised):
Time-based Affinity
preceding events.
(Statistical Analysis)
Create partitions so
Clustering:
Grouping items according that all members of
to statistical similarities
each set are similar
according to some
(Unsupervised)
metric or set of
metrics.
Classification:
Assigns new records to
existing classes
(Unsupervised)
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Example
Market Basket Analysis:
75% of customers who buy Coke also buy
corn chips (good for CRM analysis)
Time-Based Analysis:
60% of customers buy TVs followed by
digital camcorders
Customer Segmentation:
Meals charged on a business-issued gold card
are typically purchased on weekdays and
have a mean value of greater than $250,
whereas meals purchased using a personal
platinum card occur predominately on
weekends, have a mean value of $175 and
include a bottle of wine more than 65% of the
time.
Discover rules that
Decision Tree Analysis (Customer
define whether an item Segmentation):
or event belongs to a
Customers with excellent credit history have
particular subset or
a debt/equity ratio of less than 10%
class of data
Q6. What Is the Role of Knowledge
Management Systems?
1. KM fosters innovation by encourage free flow of
ideas.
2. KM improves customer service by streamlining
response time.
3. BIM boosts revenues by getting products and services
to market faster.
4. KM enhances employee retention rates by
recognizing the value of employees’ knowledge
(sharing) and rewarding them for it.
5. KM streamlines operations and reduce costs by
eliminating redundant or unnecessary processes.
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Sharing Document Content
• Indexing - most important content function in KM
applications
• Only authorized people (employees) are allowed to access
to available “Indexing” systems
• Real Simple Syndication (RSS) - subscribing to
content sources
• e.g., With a program called RSS reader, you can subscribe
to magazines, blogs, Web sites, and other content sources.
• Blogs - place where employees share their knowledge
that may include RSS feeds
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KNOWLEDGE MANAGEMENT
•
The process of creating value from intellectual capital and
sharing that knowledge with employees, managers,
suppliers, customers, and others who need it.
Reporting and data mining are used to create new
information from data, knowledge-management systems
concern the sharing of knowledge that is known to exist.
•
•
•
Knowledge management (KM) – the process of capturing,
classifying, evaluating, retrieving, and sharing
information assets in a way that provides context for
effective decisions and actions.
Knowledge management system (KMS) – an information
system that supports the capturing and use of an
organization’s “know-how”
Dr. Chen, Management Information Systems
Tacit vs. Explicit Knowledge
• Intellectual and knowledge-based assets fall
into two categories
Tacit knowledge is personal, context• _______
specific and hard to formalize and
communicate
Explicit knowledge can be easily collected,
• ________
organized and transferred through digital
means.
Dr. Chen, Management Information Systems
Tacit and Explicit KNOWLEDGE
Oral Communication
“Tacit” Knowledge
50-95%
Information Request
“Explicit” Knowledge
Information Feedback
Dr. Chen, Management Information Systems
Explicit Knowledge Base
5 -50 %
Explicit and Tacit Knowledge
• Reasons why organizations launch knowledge
management programs
Dr. Chen, Management Information Systems
The Four Modes of Knowledge Conversion
TO
Tacit
Knowledge
FROM
Explicit
Knowledge
Tacit Knowledge
A. Socialization
Explicit Knowledge
B. Externalization
(Sympathized Knowledge)
Transferring tacit knowledge
through shared experiences,
apprenticeships, mentoring
relationships, on–the-job training,
“Talking at the water cooler”
(Conceptual Knowledge)
Articulating and thereby capturing
tacit knowledge through use of
metaphors, analogies, and models
C. Internalization
D. Combination
(Operational Knowledge)
Converting explicit knowledge
into tacit knowledge; learning by
doing; studying previously
captured explicit knowledge
(manuals, documentation) to gain
technical know-how
(Systematic Knowledge)
Combining existing explicit
knowledge through exchange and
synthesis into new explicit
knowledge
C
Which mode is the one for classroom processes? _____
Dr. Chen, Management Information Systems
Source: Ikujiro Nonaka and Hirotaka Takeuchi, The Knowledge-Creating Company, 1995
Expert Systems
• Encode human knowledge as Rule-based
systems (IF/THEN)
• Rules created by interviewing experts
(culture issue)
• Major problems with ES:
 Expensive to develop
 Unpredictable maintenance
 Over hyped
Dr. Chen, Management Information Systems
67
What are Characteristics of Expert Systems?
• They capture human expertise and format it for use by
nonexperts.
• They are rule-based systems that use if…then rules to store the
expert’s knowledge.
• They gather data from people rather than using data-mining
techniques.
• They are difficult and expensive to develop.
• They are difficult to maintain because the rules are constantly
changing.
• They have been unable to live up to the high expectations set by
their name.
• Examples
– Medical Expert Systems and
– Legal Expert Systems etc.
Dr. Chen, Management Information Systems
Pharmacy Alert - Expert Systems for Pharmacies

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 (for safety) is
issued if the system detects a problem with a patient’s prescriptions.
Dr. Chen, Management Information Systems
Fig 9-25 Alert from Pharmacy Clinical Decision Support System
Q7 What Are the Alternatives for Publishing
Business Intelligence?
Fig 9-26 BI Publishing Alternatives
Dr. Chen, Management Information Systems
70
Components of a Generic Business Intelligence System

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 9-27 Components of Generic Business Intelligence System
Dr. Chen, Management Information Systems
What are the Management Functions of a BI
Server?
• The management function of a BI server
– Maintain metadata about the authorized allocation of BI results to
users.
– It tracks what results are available,
– It tracks who is authorized to view them, and
– It tracks when the results are provided to users.
• Options for managing BI results:
– Users can pull their results from a Web site using a portal server
with a customizable user interface.
– A server can automatically push information to users through alerts
which are messages announcing events as they occur.
– A report server, a special server dedicated to reports, can supply
users with information.
Which option is for the “Grocery Store (UK)” case? Push
Dr. Chen, Management Information Systems
DATA MINING
• Data-mining software includes many forms of AI
such as neural networks and expert systems
Dr. Chen, Management Information Systems
Other Data Mining Examples
• A telephone company used a data mining tool to
analyze their customer’s data warehouse. The data
mining tool found about 10,000 supposedly
residential customers that were expending over
$1,000 monthly in phone bills.
• After further study, the phone company
discovered that they were really small business
owners trying to avoid paying business rates
*
Dr. Chen, Management Information Systems
Data Mining Examples (cont.)
• 65% of customers who did not use the credit
card in the last six months are 88% likely to
cancel their accounts.
• If age < 30 and income <= $25,000 and credit
rating < 3 and credit amount > $25,000 then
the minimum loan term is 10 years.
• 82% of customers who bought a new TV 27" or
larger are 90% likely to buy an entertainment
center within the next 4 weeks.
Dr. Chen, Management Information Systems
Essential Value Propositions for a
Successful Company
• Business ________
Model
• _______
Competency
Core
– Outsourcing
– Crowdsourcing
– Offshoring
Execution
• ________
– Set corporate goals and get executive sponsorship for
the initiative
Dr. Chen, Management Information Systems
Any Sustainable Knowledge?
• Most sustainable Knowledge is
CAPACITY TO LEARN and how to
adapt to change
• “Learning to Learn and Learning to
Change.”
Dr. Chen, Management Information Systems
Q8: 2022
• Companies will know more about your
purchasing habits and psyche.
• Social singularity — machines can build
their own information systems.
• Will machines possess and create
information for themselves?
Dr. Chen, Management Information Systems
78
• End of Chapter 9
Dr. Chen, Management Information Systems
79

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