Chapter 9 Business Intelligence Systems

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Chapter 9Business Intelligence Systems
Jason C. H. Chen, Ph.D. Professor of MIS School
of Business Administration Gonzaga
University Spokane, WA 99258 chen_at_jepson.gonzaga.e
du
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Chapter Preview

• This chapter surveys the most common business
  intelligence and knowledge-management
  applications, discusses the need and purpose for
  data warehouses, and explains how business
  intelligence applications are delivered to users
  as business intelligence systems.
• Along the way, youll learn tools and techniques
  that MRV can use to identify the guides that
  contribute the most (and least) to its
  competitive strategy.
• Well wrap up by discussing some of the potential
  benefits and risks of mining credit card data.

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Study Questions

• 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?
• Q8 2020?

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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

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Working , Not Just Harder
Smarter

• Overlapping Human/Organizational (Culture,
  Process)/ Technological factors in BI/KM

PEOPLE
ORGANIZATIONAL PROCESSES
TECHNOLOGY

• Knowledge

N
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CRM and BI Example

• A Grocery store in U.K.
• 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
• IT (e.g., BI) helps to find valuable information
  then decision makers make a timely/right decision
  for improving/creating competitive advantages.

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Why Do Organizations Need Business Intelligence?

• Information systems generate enormous amounts of
  operational data that contain patterns,
  relationships, clusters, and other information
  that can facilitate management, especially
  planning and forecasting. Business intelligence
  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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How Big Is an Exabyte? (See video)

• This chart explains the names and amounts of
  computer data measurements.

• Fig 9-1 How Big is an Exabyte?

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Study Questions

• 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?
• Q8 2020?

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Business Intelligence (BI) Tools

• BI systems provide valuable information for
  decision making. (BI video)
• Three primary BI systems
• Reporting Tools
• Integrate data from multiple systems
• Sorting, grouping, summing, averaging, comparing
  data
• RFM is one of the tool for reporting.
• Data-mining Tools
• Use sophisticated statistical techniques,
  regression analysis, and decision tree analysis
• Used to discover hidden patterns and
  relationships
• Market-basket analysis

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Business Intelligence Tools

• Knowledge-management tool
• Create value by collecting and sharing human
  knowledge about products, product uses, best
  practices, other critical knowledge
• Used by employees, managers, customers,
  suppliers, others who need access to company
  knowledge

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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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Study Questions

• 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?
• Q8 2020?

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Basic Reporting Operations

• Reporting tools produce information from data
  using five basic operations
• Sorting
• Grouping
• Calculating
• Filtering
• Formatting

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List of Sales Data
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Data Sorted by Customer Name

• 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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• The figure on the left shows the raw sales data
  sorted by customer names.
• The figure on the right shows data thats been
  sorted and grouped.

Fig 9-3 Sales Data Sorted by Customer Name
Fig 9-4 Sales Data, Sorted by Customer Name
Grouped by Number of Orders Purchase Amount
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Sales Data Filtered to ShowRepeat Customers and
Formatted for Easier Understanding

• This figure shows even better information thats
  been filtered and formatted according to specific
  criteria.

• Fig 9-5 Sales Data Filtered to Show Repeat
  Customers

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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?
  gt leads and opportunities
• Frequency How frequently a customer purchased
  items? gt retention
• Monetary Value How much a customer spends on
  each purchase? gt 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.

lower
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What does RFM analysis Tell?

• 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 the score, the better the
  customer, and, consequently, the more profit the
  company will be.

lower
Fig 9-6 Example of RFM Score Data
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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

80/20 Rule (Pareto Principle)
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RFM Tools Classify Customers?

• Divides customers into five groups and assigns a
  score from 1 to 5
• R score 1 top 20 percent in most recent orders
• R score 5 bottom 20 percent (longest since last
  order)
• F score 1 top 20 percent in most frequent
  orders
• F score 5 bottom 20 percent least frequent
  orders
• M score 1 top 20 percent in most money spent
• M score 5 bottom 20 percent in amount of money
  spent

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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.

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Interpreting RFM Score Results

• 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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Online Analytical Processing (OLAP)

• OLAP, a second type of reporting tool, is more
  generic than RFM.
• OLAP provides the ability to sum, count, average,
  and perform other simple arithmetic operations on
  groups of data.
• Remarkable characteristic of OLAP reports is that
  they are dynamic. The viewer of the report can
  change reports format, hence the term online.

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How Are OLAP Reports Dynamic?

• OLAP reports
• Simple arithmetic operations on data
• Sum, average, count, and so on
• Dynamic
• User can change report structure
• View online
• Measure
• Data item to be manipulatedtotal sales, average
  cost
• Dimension
• Characteristic of measurepurchase date, customer
  type, location, sales region

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OLAP Summary

• 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
• 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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OLAP Reports

• OLAP cube
• Presentation of measure with associated
  dimensions
• a.k.a. OLAP report
• Users can alter format.
• Users can drill down into data.
• Divide data into more detail
• May require substantial computing power

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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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• 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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• 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

• Fig 9-10 Role of OLAP Server OLAP Database

Third-party vendors provide software for more
extensive graphical displays. Data Warehousing
Review OLAP services
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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 gtgtgt Product Line gtgtgt Division
• Drill-down ability to move from summary/general
  to lower/specific levels of detail
• Revenue by Year gtgtgt Quarter gtgtgtgtWeek gtgtgtDay
• 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
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Study Questions

• 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?
• Q8 2020?

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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.

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Database vs. Datawarehouse
Database
DBMS
Datawarehouse
???
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Database vs. Datawarehouse
Database
DBMS
Datawarehouse
Data Mining
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How do BI Tools Obtain Data?
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What are typical 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.

• Fig 9-11 Convergence Disciplines for Data Mining

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What are typical data-mining applications?

• 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, 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 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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Decision Tree Analysis of MIS Class Grades

• Students characteristics
• Class (junior or senior), major, employment, age,
  club affiliations, and other characteristics
• Values used to create groups that were as
  different as possible on the classification GPA
  above or below 3.0
• Results
• Best criterionClass
• Next subdivide Seniors and Juniors into more pure
  groups
• Seniorsbusiness and non-business majors
• Juniorsrestaurant employees and non-restaurant
  employees
• Best classifier is whether the junior worked in a
  restaurant

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Create Set of If/Then Decision Rules

• If student is a junior and works in a restaurant,
  then predict grade gt 3.0.
• If student is a senior and is a non-business
  major, then predict grade lt 3.0.
• If student is a junior and does not work in a
  restaurant, then predict grade lt 3.0.
• If student is a senior and is a business major,
  then make no prediction.

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Decision Trees

• Decision tree
• Hierarchical arrangement of criteria that 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 that create disparate
  groups
• More different or pure the groups, the better the
  classification

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Summary of Decision Tree Analysis

• A decision tree is a hierarchical arrangement of
  criteria that predicts a classification or value.
  Its an unsupervised data-mining technique that
  selects the most useful attributes for
  classifying entities on some criterion. It uses
  ifthen rules in the decision process. Here are
  two examples.

If student is a junior and works in a restaurant,
then predict grade 3.0
gt
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 prediction
no
Fig 9-13 Grades of Students from Past MIS Class
(Hypothetical Data)
Fig 9-14 Credit Score Decision Tree
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Decision Tree

• Figure CE16-3

If Senior Yes
If Junior Yes
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Decision Tree for Loan Evaluation

• Common business application
• Classify loan applications by likelihood of
  default
• Rules identify loans for bank approval
• Identify market segment
• Structure marketing campaign
• Predict problems

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A Decision Tree for a Loan Evaluation

• Classifying likelihood of default
• Examined 3,485 loans
• 28 percent of those defaulted
• Evaluation criteria
• Percentage of loan past due less than 50 percent
  .94, no default
• 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 9-14 Credit Score Decision Tree
Figure CE14-4
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Market-Basket Analysis

• Market-basket analysis is a supervised
  data-mining technique for determining sales
  patterns.
• Uses statistical methods to identify sales
  patterns in large volumes of data
• Shows which products customers tend to buy
  together
• Used to estimate probability of customer purchase
• Helps identify cross-selling opportunities
• "Customers who bought book X also bought book Y

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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). 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
  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)

A Fins B Mask
P(Fins)280/1000.28
Lift is almost double
Fig 9-12 Market-Basket Example
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Market-Basket Terminology

• Support
• Probability that two items will be bought
  together
• Fins and masks purchased together 150 times, thus
  support for fins and a mask is 150/1,000, or 15
  percent
• Support for fins and weights is 60/1,000, or 6
  percent
• Support for fins along with a second pair of fins
  is 10/1,000, or 1 percent

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Market-Basket Terminology

• Lift
• Ratio of confidence to base probability of buying
  item
• Shows how much base probability increases or
  decreases when other products are purchased
• Example
• Lift of fins and a mask is confidence of fins
  given a mask, divided by the base probability of
  fins.
• Lift of fins and a mask is .5556/.28 1.98

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Market-Basket Terminology

• Confidence
• What proportion of the customers who bought a
  mask also bought fins?
• Conditional probability estimate
• Example
• Probability of buying fins 28
• Probability of buying swim mask 27
• After buying fins,
• Probability of buying mask 150/270 or 55.56
• Likelihood that a customer will also buy fins
  almost doubles, from 28 to 55.56. Thus, all
  sales personnel should try to sell fins to anyone
  buying a mask.

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Regression Analysis

• CellphoneWeekendMinutes 12 (17.5
  CustomerAge)
• (23.7 NumberMonthsOfAccount)
• Using this equation, analysts can predict number
  of minutes of weekend cell phone use by summing
  12, plus 17.5 times the customers age, plus 23.7
  times the number of months of the account.
• Considerable skill is required to interpret the
  quality of such a model

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Neural Networks

• Neural networks
• Popular supervised data-mining technique used to
  predict values and make classifications such as
  good prospect or poor prospect customers
• Complicated set of nonlinear equations
• See kdnuggets.com to learn more

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What are typical data-mining applications?
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DATA MINING

• Data-mining software includes many forms of AI
  such as neural networks and expert systems

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Data Mining Analysis

• Data mining the process of analyzing data to
  extract information not offered by the raw data
  alone
• To perform data mining users need data-mining
  tools
• Data-mining tool uses a variety of techniques
  to find patterns and relationships in large
  volumes of information and infers rules that
  predict future behavior and guide decision making
• An example
• Grocery Store in UK

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Other Data Mining Examples

• A telephone company used a data mining tool to
  analyze their customers 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

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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 lt 30 and income lt 25,000 and credit
  rating lt 3 and credit amount gt 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.

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Study Questions

• 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?
• Q8 2020?

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What Is the Purpose of Data Warehouses and Data
Marts?

• Purpose (video)
• To extract and clean data from various
  operational systems and other sources
• To store and catalog data for BI processing
• Extract, clean, prepare data
• Stored in data-warehouse DBMS

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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.

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What is the purpose of data warehouses and data
marts?

• 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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Independent data mart data warehousing
architecture
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Data Warehouse Data Sources

• Internal operations systems
• External data purchased from outside sources
• Data from social networking, user-generated
  content applications
• Metadata concerning data stored in data-warehouse
  meta database
• Clickstream data of customers clicking behavior
  on a Web site

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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.

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• Figure 9-16, left, lists some of the data thats
  readily available for purchase from data vendors
• Some of the problems companies experience with
  operational data are shown in figure 9-17 below.

• 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
  dimensionalityjust because you have more
  attributes doesnt mean you have a more
  worthwhile predictor.

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Fig 9-16 Example Typical of Customer Credit Data
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Problems with Operational Data

• Dirty datamistakes in spelling or punctuation,
  incorrect data associated with a field,
  incomplete or outdated data or even data that is
  duplicated in the database.

Fig 9-17 Problems of using Transaction Data for
Analysis and Data mining
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Examples of Dirty Data

• A value of B for customer gender
• 213 for customer age
• Value of 9999999999 for a U.S. phone number
• Part color of gren
• mail address of WhyMe_at_GuessWhoIAm.org.

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Problems with Operational Data

• Too much data causes
• Curse of dimensionality
• Problem caused by the exponential increase in
  volume associated with adding extra dimensions to
  a (mathematical) space.
• Too many rows or data points
• With more attributes, the easier it is to build a
  model that fits the sample data but that is
  worthless as a predictor.
• Major activities in data mining concerns
  efficient and effective ways of selecting
  attributes.

74
Data Warehouses and Data Marts?

• Heres 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 9-18 Data Mart Examples

75
Data Warehouses vs. Data Marts

• Data mart is a collection of data (video)
• Created to address particular needs
• Business function
• Problem
• Opportunity
• Smaller than data warehouse
• Users may not have data management expertise
• Need knowledgeable analysts for specific function
• Data extracted from data warehouse for a
  functional area

76
Study Questions

• 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?
• Q8 2020?

77
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 organizations 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.

Tacit
Explicit
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Tacit and Explicit KNOWLEDGE
Oral Communication Tacit Knowledge 50-95
Explicit Knowledge Base 5 -50
Information Request
Explicit Knowledge
Information Feedback
80
Explicit and Tacit Knowledge

• Reasons why organizations launch knowledge
  management programs

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The Four Modes of Knowledge Conversion
TO
Explicit Knowledge
Tacit Knowledge
Socialization (Sympathized Knowledge)
Externalization (Conceptual Knowledge)
Tacit Knowledge
Transferring tacit knowledge through shared
experiences, apprenticeships, mentoring
relationships, onthe-job training, Talking at
the water cooler
Articulating and thereby capturing tacit
knowledge through use of metaphors, analogies,
and models
FROM
Combination (Systematic Knowledge)
Internalization (Operational Knowledge)
Converting explicit knowledge into tacit
knowledge learning by doing studying previously
captured explicit knowledge (manuals,
documentation) to gain technical know-how
Combining existing explicit knowledge through
exchange and synthesis into new explicit knowledge
Explicit Knowledge
Source Ikujiro Nonaka and Hirotaka Takeuchi, The
Knowledge-Creating Company, 1995
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Primary Benefits of KM

• 1. KM fosters innovation by encouraging the free
  flow of ideas.
• 2. KM improves customer service by streamlining
  response time.
• 3. KM boosts revenues by getting products and
  services to market faster.
• 4. KM enhances employee retention rates by
  recognizing the value of employees knowledge and
  rewarding them for it.
• 5. KM streamlines operations and reduces costs by
  eliminating redundant or unnecessary processes.
• KM preserves organizational memory by capturing
  and storing the lessons learned and best
  practices of key employees.

83
Sharing of Document Content and Employee Knowledge

• Sharing Document Content
• Collaboration systems are concerned with document
  creation and change management, KM applications
  are concerned with maximizing content use.

84
Two Typical Knowledge-Management Applications

• Two key technologies for sharing content in KM
  systems
• Indexingmost important content function in KM
  applications that provide easily accessible and
  robust means of determining if content exists and
  a link to obtain the content. Used in conjunction
  with search functions.

85
Two Typical Knowledge-Management Applications

• RSS 2. (Real Simple Syndication)a standard for
  subscribing to content sources on Web sites. An
  RSS Reader program helps users to
• Subscribe to content sources.
• Periodically check sources for new or updated
  content through RSS feeds.
• Place content summaries in an RSS inbox with link
  to the full content.
• Think of RSS as an email system for content
• Data source must provide what is termed an RSS
  feed, which simply means that the site posts
  changes according to one of the RSS standards.

86
Fig 9-19 Interface of a Typical RSS Reader

• This figure shows a typical RSS reader. The left
  pane shows RSS sources. Entries are grouped into
  categories predetermined by the user.

87
Fig 20 Blog Posts of SharePoint Team Member

• Blogs provide an easy way to share knowledge as
  seen in this figure. You can use RSS feeds to
  subscribe to thousands of blogs.

88
Expert Systems

• Another form of knowledge management are expert
  systems with the following characteristics
• Expert systems attempt to capture human expertise
  and put it into a format that can be used by
  nonexperts.
• Expert systems are rule-based systems that use
  If?Then rules similar to those created by
  decision-tree analysis, except they are created
  from human experts instead of data-mining
  systems.
• Expert systems gather data from people rather
  than using data-mining techniques

89
Problems of Expert Systems

• Difficult and expensive to develop. They require
  many labor hours from both experts in the domain
  under study and designers of expert systems. High
  opportunity cost of tying up domain experts.
• Difficult to maintain. Nature of rule-based
  systems creates unexpected consequences when
  adding a new rule in middle of hundreds of
  others. A small change can cause very different
  outcomes.
• No expert system has the same diagnostic ability
  as knowledgeable, skilled, and experienced
  doctors. Rules/actions change frequently.

90
Expert Systems for Pharmacies

• Used as a safety net to screen decisions of
  doctors and other medical professionals. These
  systems help to achieve hospitals goal of
  state-of-the-art, error-free care.
• DoseChecker, verifies appropriate dosages on
  prescriptions issued in the hospital.
• PharmADE, ensures that patients are not
  prescribed drugs that have harmful interactions.
• Pharmacy order-entry system invokes these
  applications as a prescription is entered. If
  either system detects a problem with the
  prescription, it generates an alert.

91
Pharmacy Alert

• This is an example of the output from a medical
  expert system that is part of a decision support
  system. Based on the systems rules, an alert is
  issued if the system detects a problem with a
  patients prescriptions.

Fig 9-21 Alert from Pharmacy Clinical Decision
Support System
92
Study Questions

• 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?
• Q8 2020?

93
How Are Business Intelligence Applications
Delivered?

• 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
94
What Are the Management Functions of a BI Server?

• 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
• 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.

95
What Are the Management Functions of a BI Server?

• Maintains metadata about authorized allocation of
  BI results to users
• Tracks what results are available, what users are
  authorized to view those results, and schedule to
  provide results to authorized users. Adjusts
  allocations as available results change and users
  come and go.

96
BI Servers Vary in Complexity and Functionality

• Some BI servers are simply Web sites from which
  users can download, or pull BI application
  results.
• For example, a BI Web server might post results
  of an RFM analysis for salespeople to query to
  obtain RFM scores for their customers. Management
  function for such a site would simply be to track
  authorized users and restrict access.

97
BI Servers Vary in Complexity and Functionality
and could operate as a portal server.

• 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

98
BI Portals

• Portals might provide common data such as local
  weather, and links to company news, and to BI
  application results such as reports on daily
  sales, operations, new employees, and results of
  data-mining applications.
• Authorized users are allowed to place reports,
  data-mining results, or other BI application
  results on their customized pages.
• BI application server pushes the subscribed
  results to the user.

99
Report Server

• A special case of a BI application server that
  serves only reports
• BI application servers track results, users,
  authorizations, page customizations,
  subscriptions, alerts, and data for any other
  functionality provided.

100
What Are the Delivery Functions of a BI Server?

• Track authorized users
• Track the schedule for providing results to users
• Issue exception alerts that notify users of an
  exceptional event
• Procedures used depends on the nature of the BI
  system
• 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
• Procedures are determined by unique requirements
  of users
• BI results can be delivered to any device, such
  as computers, PDAs, phones, other applications
  such as Microsoft Office, and as a SOA service

101
Essential Value Propositions for a Successful
Company

• Business
• Competency
• Set corporate goals and get executive sponsorship
  for the initiative

Model
Core

• Execution

102
Any Sustainable Knowledge?

• Most sustainable Knowledge is
• Learning to Learn and Learning to Change.

CAPACITY TO LEARN and how to adapt to change
103
Study Questions

• 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?
• Q8 2020?

104
2020?

• Through data mining, companies, known as data
  aggregators, will know more about your
  purchasing psyche than you, your mother, or your
  analyst.
• If you use your card to purchase secondhand
  clothing, retread tires, bail bond services,
  massages, casino gambling or betting you alert
  the credit card company of potential financial
  problems and, as a result, it may cancel your
  card or reduce your credit limit.
• Absent laws to the contrary, by 2020 your credit
  card data will be fully integrated with personal
  and family data maintained by the data
  aggregators (like Acxiom and ChoicePoint).
• By 2020, some online retailers will know a lot
  more about you, data aggregators, and most
  consumers purchases than well know ourselves.

105

• END of Chapter 9