1. Business Intelligence Systems
Chapter 9
Business Intelligence
Systems
This chapter considers BI systems: information systems that identify patterns, relationships, and other information from organizational structured and unstructured social data, external and purchased data.

2. “We Can Produce Any Report You Want, But You’ve Got to Pay for It.”
Different expectations about what is a report
Great use for exception reporting
Feature PRIDE prototype and supporting data are stored in profile, profileworkout, and equipment tables
Need legal advice on system
GOALS: Use the PRIDE system to:
Illustrate a practical application for business intelligence systems, specifically reporting.
Show the use of animation for reporting on a mobile device.
Provide a setting to teach standard reporting terminology.
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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: 2023?
Chapter begins by summarizing reasons organizations use business intelligence. Then, describes three basic activities in business intelligence process and illustrates those activities using GearUp.
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4. Q1: How Do Organizations Use Business Intelligence (BI) Systems?
Components of a Business Intelligence System
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5. Example Uses of Business Intelligence
Note hierarchical nature of tasks. Business intelligence used for all four of collaborative tasks.
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6. Q2: What Are the Three Primary Activities in the BI Process?
> Publish results:
Push publishing delivers BI according to a schedule, or due to an event or particular condition without any request from users.
Pull publishing requires users to request BI results.
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7. Using BI for Problem-solving at GearUp: Process and Potential Problems
Obtain commitment from vendor
Run sales event
Sell as many items as it can
Order amount actually sold
Receive partial order and damaged items
If receive less than ordered, ship partial order to customers
Some customers cancel orders
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8. Tables Used for BI Analysis at GearUp
Top section shows three of tables in GearUp’s operational database used to produce the data extract.
Lucas uses these data to create Item_Shipped, Item_Not_ Shipped, and Quantity_Received tables.
Addison summed quantities from tables to create Item_Summary_Data table.
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9. Extract of ITEM_SUMMARY_DATA Table
To determine orders lost to damage and those lost to cancellations, GearUp indirectly computes TotalCancelled.
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10. Lost Sales Summary Report
To determine extent of sales lost due to short shipments or damage, Addison created an Access report to sum data from Item_Summary_Data table
From this report, vendors 5000 and 2000 have never had a shortage or quality problem. Vendor 4000 has a modest problem, vendors 1000 and 3000 have caused numerous lost sales, due to shortages or damaged goods. 55.5% of sales of vendor 3000’s items have been lost (19,450/35,000).
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11. Lost Sales Details Report
Items shown by EventItemNumber, not by item name, event date, and event date
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12. Event Data Spreadsheet
With a worksheet in tabular format, it would be easy to import this data from Excel to Access. Someone must put it into tabular format or extract the data and enter it manually.
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13. Short and Damaged Shipments Summary
All vendor 1000 problems are caused by damage. Vendor 1000 always shipped appropriate number.
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14. Short and Damaged Shipments Details Report
Report shows vendor 1000 has persistent damage problems and vendor 3000's shipment are short.
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Publish Results
Options
Print and distribute via or collaboration tool
Publish on web server or SharePoint
Publish on a BI server
Automate results via web service
These options discussed in detail in Q5. GearUp would choose among these alternatives according to its needs. Most likely, they will print results and or share via a collaboration tool.
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Q3: 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
IS professionals do not want business analysts processing operational data, because if they make an error it could severely disrupt operations.
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17. Functions of a Data Warehouse
Obtain or extract data
Cleanse data
Organize and relate data
Create and maintain catalog
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18. Components of a Data Warehouse
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19. Examples of Consumer Data that Can Be Purchased
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20. Possible Problems with Source Data
Most operational and purchased data have problems that inhibit usefulness for business intelligence.
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Data Marts Examples
A data mart is a subset of a data warehouse. It addresses a particular component or functional area.
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22. Q4: How Do Organizations Use Reporting Applications?
Create meaningful information from disparate data sources
Deliver information to user on time
Basic operations:
Sorting  
Filtering
Grouping  
Calculating
Formatting
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23. How Does RFM Analysis Classify Customers?
Recently
Frequently
Money
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24. RFM Analysis Classifies Customers
To produce an RFM score, sort customer purchase records by date from most recent (R) purchase, divide into quintiles, and assign a score from 1 to 5 for each customer by quintile. Repeat for Frequently and Money.
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25. OLAP Product Family by Store Type
Typical OLAP Report
Dimensions Product Family and Store Type
Report shows how net store sales vary by product family and store type.
OLAP Product Family by Store Type
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26. OLAP Product Family and Store Location by Store Type
User added dimensions Store (Country) and State. Product-family sales broken out by location of stores.
Sample data include only stores in US western states of California, Oregon, and Washington.
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OLAP Product Family and Store Location by Store Type, Showing Sales Data for Four Cities
User drilled down into stores located in California. Report shows sales data for four cities in California that have stores.
User also changed the order of the dimensions.
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28. Q5: How Do Organizations Use Data Mining Applications?
Source disciplines of data mining
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29. Unsupervised Data Mining
Analyst does not create a priori hypothesis or model
Hypotheses created afterward to explain patterns found
Example: Cluster analysis
Cluster analysis: Statistical technique to identify groups of entities with similar characteristics; used to find groups of similar customers from customer order and demographic data
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30. Supervised Data Mining
Develop a priori model to compute estimated parameters of model
Used for prediction, such as regression analysis
Ex: CellPhoneWeekendMinutes =
(12 + (17.5 X CustomerAge) +
(23.7 X NumberMonthsOfAccount)
= * *6 = 521.7
Predict number of minutes of weekend cell phone use.
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31. Market-Basket Analysis
Market-basket analysis – a data-mining technique for determining sales patterns
Statistical methods to identify sales patterns in large volumes of data
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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32. Market-Basket Example: Dive Shop Transactions = 400
Hypothetical sales data
First row of numbers under each column is total number of times an item sold.
For example, 270 in third row under Mask means that 270 of the 400 (.67) transactions included masks.
280 under Fins means that 280 of 400 (.700) transactions included fins.
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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”
Basic idea of a decision tree is to select attributes most useful for classifying entities.
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34. Credit Score Decision Tree
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35. Ethics Guide: The Ethics of Classification
Classifying applicants for college admission
Collects demographics and performance data of all its students
Uses decision tree program
Statistically valid measures to obtain statistically valid results
No human judgment involved
GOAL
Explore difficult ethical issues about using decision trees for classifying people.
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36. The Ethics of Classification: Resulting Decision Tree
Classifying people can raise serious ethical issues because:
Statistically valid measures without human judgment involved
Important data might not be included
Results could reinforce social stereotypes
Might not be organizationally or socially feasible, or legal
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37. Q6: How Do Organizations Use BigData Applications?
Huge volume – petabyte and larger
Rapid velocity – generated rapidly
Great variety
Structured data, free-form text, log files, possibly graphics, audio, and video
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38. MapReduce Processing Summary
Technique for harnessing power of thousands of computers working in parallel
Google search logs broken into pieces
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39. Google Trends on the Term Web 2.0
This trend line supports contention that “Web 2.0” is fading from use.
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Hadoop
Open-source program supported by Apache Foundation2
Manages thousands of computers
Implements MapReduce
Written in Java
Amazon.com supports Hadoop as part of EC3 cloud offering
Query language entitled Pig
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41. Using MIS InClass 9: What Wonder Have We Wrought?
Data aggregator is a company that obtains data from public and private sources, and stores, combines, publishes it in sophisticated ways.
See Instructor’s Manual for example answers to questions.
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42. Q7: What Is the Role of Knowledge Management Systems?
Creating value from intellectual capital and sharing that knowledge with those who need that capital
Preserving organizational memory by capturing and storing lessons learned and best practices of key employees
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43. Benefits of Knowledge Management
Improve process quality
Increase team strength
Goal: Enable employees to use organization’s collective knowledge
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44. What Are Expert Systems?
Rule-based IF/THEN
Encode human knowledge
Process IF side of rules
Report values of all variables
Knowledge gathered from human experts
Expert systems shells
Expert systems are rule-based systems that encode human knowledge as If/Then rules.
Expert systems shells – programs that process a set of rules
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45. Example of IF/THEN Rules
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46. Drawbacks of Expert Systems
Difficult and expensive to develop
Labor intensive
Ties up domain experts
Difficult to maintain
Changes cause unpredictable outcomes
Constantly need expensive changes
Don’t live up to expectations
Can’t duplicate diagnostic abilities of humans
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47. What Are Content Management Systems (CMS)?
Support management and delivery of documents, other expressions of employee knowledge
Challenges
Databases are huge
Content dynamic
Documents do not exist in isolation
Contents are perishable
In many languages
Content management system functions are huge and complex.
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48. What are CMS Application Alternatives?
In-house custom
Customer support department develops in-house database applications to track customer problems
Off-the-shelf
Horizontal market products (SharePoint)
Vertical market applications
Public search engine
Google
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49. How Do Hyper-Social Organizations Manage Knowledge?
Hyper- Social KM Media
Social media, and related applications, for management and delivery of organizational knowledge resources
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50. Resistance to Hyper-Social Knowledge-Sharing
Reluctance to exhibit ignorance
Employee competition
Solution
Strong management endorsement
Strong positive feedback and rewards
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51. Q8: What Are the Alternatives for Publishing BI?
Table lists four server alternatives for BI publishing.
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52. What Are the Two Functions of a BI Server?
Components of a Generic Business Intelligence System
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Q9: 2023?
Companies will know more about your purchasing habits and psyche.
Social singularity – Machines will build their own information systems.
Will machines possess and create information for themselves?
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54. Guide: Semantic Security
Unauthorized access to protected data and information
Physical security
Passwords and permissions
Delivery system must be secure
Unintended release of protected information through reports & documents
What, if anything, can be done to prevent what Megan did?
GOALS
Discuss trade-off between information availability and security.
Introduce, explain, and discuss ways to respond to semantic security.
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55. Guide: Data Mining in the Real World
Problems:
Dirty data
Missing values
Lack of knowledge at start of project
Over fitting
Probabilistic
Seasonality
High risk – unknown outcome
GOALS
Teach real-world issues and limitations of data mining.
Investigate ethics of working on projects of doubtful or harmful utility to sponsoring organization.
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Active Review
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: 2023?
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57. Case Study 9: Hadoop the Cookie Cutter
Third-party cookie created by site other than one you visited
Generated in several ways, mostly occurs when a Web page includes content from multiple sources
DoubleClick
IP address where content was delivered
Records data in a log
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58. Case Study 9: Hadoop the Cookie Cutter (cont'd)
Third-party cookie owner has history of what was shown, what ads clicked, and intervals between interactions
Cookie log contains data to show how you respond to ads and your pattern of visiting various web sites where ads placed
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FireFox Collusion
FireFox has an optional feature called Collusion that tracks and graphs all cookies on your computer.
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60. Ghostery in Use (ghostery.com)
Who are these companies that are gathering my browser behavior data? You can find out using ghostery, another useful browser add-in feature (
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