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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]
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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 reporting applications?
Q5: How do organizations use data mining applications?
Q6: How do organizations use BigData applications?
Q7: What is the role of knowledge management systems?
Q8: What are the alternatives for publishing BI?
Q9: 2026?
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Database vs. Datawarehouse
DBMS
???
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Database
Datawarehouse
Q1: How Do Organizations Use Business
Intelligence (BI) Systems?
• Information systems generate enormous amounts of
operational _____ 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 are created and stored
every day.
 12,000 gigabytes per person of data, worldwide in
2009
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Q1: How Do Organizations Use Business
Intelligence (BI) Systems?
Components of Business
Intelligence System
Fig 9-1: Components of a Business Intelligence System
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Tools vs. Applications vs. Systems
• BI ____ (e.g., decision-tree analysis) is one
or more computer programs. BI tools
implement the logic of a particular procedure
or process.
• BI __________ is the use of a tool on a
particular type of data for a particular purpose.
• BI _______ 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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Why do organizations need business intelligence?
• BI systems are computer programs provide valuable
information for decision making.
• Three primary BI systems:
– __________ 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 (Recency, Frequency, and Monetary
Value) is one of the tool for reporting.
– ____________ tools process data using statistical, regression,
decision tree, and market basket techniques to discover
hidden patterns and relationships, and make predictions
based on the results
– ___________ _________ 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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How Do Organizations Use BI?
[4]
[3]
[2]
(Decision
Support
Systems)
[1]
Fig 9-2 Example Uses of Business Intelligence
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What Are Typical BI Applications?
• Identifying changes [or patterns] in purchasing
patterns (data warehouse)
• Important life events cause customers to change what
they buy.
• BI for entertainment
• Netflix has data on watching, listening, and rental
habits, however, determines what people actually
want, not what they say.
• Predictive policing/ ______
• Analyze data on past crimes, including location, date,
time, day of week, type of crime, and related data, to
predict where crimes are likely to occur.
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Just-in-Time Medical Reporting
• Example of real time data mining and
reporting.
• Injection notification services
• Software analyzes patient’s records, if
injections needed, recommends as exam
progresses.
• Blurry edge of medical ethics.
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Q2: What Are the Three Primary
Activities in the BI Process?
[1]
[2]
[3]
Fig 9-3 Three Primary Activities in the BI Process
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Q3: Components and Functions of a Data Warehouse
Fig 9-14 Components of a Data Warehouse
• Functions of a data warehouse
 Obtain data from operational, internal and external
databases.
 Cleanse data.
 Organize and relate data.
 Catalog data using metadata.
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Data Warehouse vs. Data Mart
Data Mart is a _________ of Data Warehouse
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Fig 9-15 Data Mart Examples
Independent data mart data
warehousing architecture
Data marts:
Legacy System:
Operational database
Mini-warehouses, limited in scope
L
T
E
Separate ETL for each
independent data mart
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Data access complexity
due to multiple data
marts
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Q4: How Do Organizations Use
Reporting Applications?
• Create meaningful information from
disparate data sources
• Deliver information to user on time
• Basic operations:
1.
2.
3.
4.
5.
Sorting
Filtering
Grouping
Calculating
Formatting
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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.
– __________: How recently a customer purchased items? => leads
and opportunities
– __________: How frequently a customer purchased items? =>
retention
– __________ 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 _______ the score, the better the customer.
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RFM Analysis: Example RFM Scores
• _ecently
• _requently
• _oney
lower the score,
The ______
the better the customer,
and, consequently, the
more profit the
company will be.
Fig 9-16 Example of RFM Scores
Organizations can find their most valuable customers through “RFM”:
– 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
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Interpreting RFM Score Results – more examples
• 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
80/20 Rule (Pareto Principle)
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RFM Analysis Classification Scheme
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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
– _________ with
– __________
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Example of Grocery Sales OLAP Report
http://dwreview.com/OLAP/
http://www.tableausoftware.com
• 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.
OLAP Product Family by Store Type
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Fig 9-17 Example Grocery Sales OLAP Report
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Example of Expanded Grocery Sales
OLAP Report
Drill
down
Fig 9-18 Example of Expanded Grocery
Sales OLAP Report
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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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PART II
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Q5 How Do Organizations Use Data-mining
Applications?

Businesses use statistical techniques to find __________ 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
Fig 9-20 Source Disciplines of Data Mining
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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
Apply statistical techniques such as Market Basket Analysis to estimate
parameters of a model
• 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
• 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
•Analyst does not start with
a priori hypothesis or
model
•Hypothesized model
created (after) based on
analytical results to explain
patterns found
•Example: Market Basket
Analysis and Cluster
analysis to find groups
(Decision Tree)
•Model created before
analysis
•Hypotheses created
before analysis
•Regression analysis:
make predictions
•Example: Cellphone
Weekend Minuses (next
slide)
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Supervised Data Mining
• Uses a priori model to compute outcome of model
• Prediction, such as regression analysis
• Analysts predict the number of minutes of weekend cell
phone:
• Ex: A customer who is 21 years old and opens an
account with 6 months. What is the number of weekend
minutes can be predicted?
• Answer: CellPhoneWeekendMinutes
= (12 +
(17.5*CustomerAge)+(23.7*NumberMonthsofAccount)
= 12 + 17.5*___ + 23.7*___ = ___________
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Market-Basket Analysis
• Market-basket analysis
– Identify sales patterns in large volumes
of data
– un-supervised data-mining tool
– Products customers tend to buy together
– Probabilities of customer purchases
– Identify cross-selling opportunities
Customers who bought fins also
bought a mask.
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Interpretation on the Results

Market-Basket Analysis is a un-supervised data-mining
tool for determining sales patterns. It helps businesses
create cross-selling opportunities (i.e., buying relevant
products together). Two 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 fin also bought mask [given by
he/she also bought mask])
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)
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Market
Basket
Analysis at
a Dive
Shop
(Transactions
= 400)
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Fig 9-21 Market-Basket Analysis at a Dive Shop
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Market-Basket Example: Dive Shop
Transactions = 400
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Transactions of both fins and masks are bought
together are 250. Therefore, the probability
that these two items will be purchased together.
i.e., support is 250/400=0.625
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It means that if there are 100 transactions
done today, fins and masks are found in the
shopping basket is about 62.5 (62.5%)
transactions.
Transactions of both fins and masks are bought
together are 250. Therefore, the probability
that these two items will be purchased together.
i.e., support is 250/400=0.625
It means that if there are 100 transactions
done today, fins and masks are found in the
shopping basket is about 62.5 (62.5%)
transactions.
There exists cross-selling opportunities
The _______ the support value the ______
cross-selling opportunities will be
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The conditional probability of the customers who
bought a fin given by he/she also bought mask.
Therefore, confidence is 250/270=0.926.
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It means that 92.6% probability
of the times that when mask is
bought, fin is bought as well.
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Lift: the ratio of confidence (0.926)
to the base probability (0.7) of buying
an item (fins). 0.926/0.7=1.32
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Therefore, the likelihood that customers buy fins
when they buy a mask increases by 32% (1.32-1)
Decision Trees
• Hierarchical arrangement of criteria to
predict a classification or value
• Unsupervised data mining technique
• Basic idea of a decision tree
– Select attributes most useful for
classifying something on some criteria to
create “pure groups”
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Credit Score Decision Tree
Fig 9-22 Credit Score Decision Tree
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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
[A]
[B]
[C]
[D]
Decision Tree Examples for MIS Class (Hypothetical Data)
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What are typical data-mining applications?
DM Capabilities
Description
Discover rules that
Associations/Affinity
correlate one set of
(Unsupervised):
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: How Do Organizations Use BigData Applications?
•
•
•
•
BigData? – Not just big. Three V’s.
Huge _______– petabyte and larger
Rapid _______– generated rapidly
Great _______
– Structured data, free-form text, log files, graphics, audio, and
video
• Because BigData is huge, fast, and varied. It can’t be
processed using traditional techniques.
• MapReduce is a technique for harnessing the power of
thousands of computers working in parallel.
• Push ____________ to the data instead of pushing data to
a computing mode.
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Dr. Chen, Management Information Systems
Q7: What Is the Role of Knowledge
Management Systems?
• Knowledge Management
 The process of creating value from intellectual capital and
sharing knowledge with those who need that capital
 Preserving organizational memory by capturing and storing
lessons learned and best practices of key employees
 Scope of KM same as SM in hyper-social organizations.
 Enhance employee retention rates by recognizing and
rewarding knowledge sharing.
 Streamline operations and reduce costs.
• Knowledge management system (KMS) – an
information system that supports the capturing and use
of an organization’s “know-how”
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Tacit vs. Explicit Knowledge
• Intellectual and knowledge-based assets fall into
two categories
• _______ knowledge is personal, context-specific
and hard to formalize and communicate
• ________ knowledge can be easily collected,
organized and transferred through digital means.
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Tacit and Explicit KNOWLEDGE
Oral Communication
“Tacit” Knowledge
50-95%
Information Request
“Explicit” Knowledge
Information Feedback
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Explicit Knowledge Base
5 -50 %
Q8: What Are the Two Functions of a BI Server?

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.
Which option is for the “Grocery Store (UK)” case (pull or push)?
Fig 9-29 Elements of Generic Business Intelligence System
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Q9: 2026?
• Exponentially more information about customers,
better data mining techniques.
• Companies buy and sell your purchasing habits and
psyche.
• Singularity
Computer systems adapt and create their own
software without human assistance.
Machines will possess and create information for
themselves.
Will we know what the machines will know?
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• END of CHAPTER 9
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