1. Chapter 9 – Business Intelligence

2. Announcement
Thursday Night we will begin at 5:30

3. Why do organizations need BI?

4. Why do organizations need BI?
Tons of data out there!
In 2002, 2 exabytes were created
In 2008, 70 exabytes
14x words spoken by human beings ever
Business Intelligence – information containing patterns, relationships, and trends
How do you get it out???? BI Systems

5. What BI Systems are available?

6. What BI Systems are available?
BI System – Information system that employs BI tools to produce and deliver information
Type of systems depend on tools in use
Categories of tools
Reporting - Simple
read, process, format, deliver
Used to assess results – What happened?
Data mining - Sophisticated
Searching for patterns or relationships
Used to make predictions – What will happen?
Knowledge management
Used to store employee knowledge and make it available to others
Source of data – humans
How do you handle what is happening?

7. Tools vs. Applications vs. Systems
Tool – one or more computer programs that implement the logic of a particular procedure
Example: Decision tree analysis
Application – use of a tool on a particular type of data for a particular purpose
Example: Assess risk for a loan to default
System – has all 5 components (hardware, software, data, people, procedures) delivering results of a BI application
Example: delivers results to loan officer who makes final decision

8. Reporting Applications
Reporting application inputs data from one or more sources and applies a reporting tool to that data to produce information. This is then delivered to users by reporting system.
Operations commonly used:
Sorting
Grouping
Calculating
Filtering
Formatting

9. Some Dashboards to see http://dashboard.virginiadot.org/

10. Analytical Tools
RFM Analysis – ranks information according to purchasing behavior – gives customers an RFM Score (1 – 5, 1 being the top 20%)
How Recently?
How Frequently?
How much Money?

11. In Class Exercise Review the data. Sort the data
Split into 20% increments for R, F, and M
1 for Most Recent, 5 for Least Recent
1 for Most Frequent, 5 for Least Frequent
1 for Most Money, 5 for Least Money
Assign scores to each customer

12. What would you do with each?

13. OLAP – Online Analytical Processing
More generic than RFM
Dynamic – viewer can change the format
Measures and Dimensions
Measures – data item of interest
Total sales, average sales, average cost, etc.
Dimension – characteristic of a measure
Purchase date, customer location, etc.

14. Example – An OLAP Cube or report
Users can alter the format
Possible to drill down into the data
Requirements
Computing power
Tools may be costly
Measure
Dimension

15. A Demo of a Tool

16. Data Mining
Statistical techniques to find patterns and relationships among data and use it for classification and prediction
Data mining techniques are a blend of statistics and mathematics, and artificial intelligence and machine-learning

17. What’s the difference between supervised and unsupervised data mining?

18. Supervised vs. Unsupervised data mining
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 hypothesis 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

19. Market-Basket Analysis
Data mining tool for determining sales patterns
Helps businesses create cross-selling opportunities
Terms used with this type of analysis
Support—the probability that two items will be purchased together
Confidence—a conditional probability estimate
Lift – ratio of confidence to support
Complex, requires analytical tools

20. Market-Basket Example: Transactions = 400

21. Decision Trees
Hierarchical arrangement of criteria that predicts a classification or value
Unsupervised data-mining technique that selects the most useful attributes for classifying entities on some criterion
If…then rules

22. Example Select attributes that are most useful for classifying
Predicting Grades for Students in COB 204
What are some attributes/characteristics we should consider?
How do businesses use decision trees?

23. College Admissions Decision Tree Group Assignment – Ethics p.303

24. Data Warehouses and Data Marts
Address the problems companies have with missing data values and inconsistent data
Help standardize data formats between operational data and data purchased from third-party vendors
Prepare, store, and manage data specifically for data mining and analyses.

25. Problems with Operational data

26. The Curse of Dimensionality
The more attributes there are, the easier it is to build a model that is worthless

27. Data Marts vs. Data Warehouses
Data mart is smaller than a warehouse
Data mart addresses a particular component or function

28. Knowledge Management Applications
KM – process of creating value from intellectual capital and sharing with others who need it
Data mining and reporting create new information
KM shares known information

29. What are the benefits of KM?

30. Benefits of KM Fosters innovation – free flow of ideas
Improves customer service – faster response time
Boosts revenues – get product to market faster
Enhances retention – recognize/reward knowledge
Streamlines operations – eliminates/reduces redundant or unnecessary operations
Preserves organizational memory

31. Sharing Document Content
Indexing
Need to be able to easily access information
Need keyword searchability
Need quick response
RSS – Real simple syndication
Think of it as an system for content
Subscribe to magazines, blogs, websites, and other sources – RSS Feeds

32. Example

33. Expert Systems Rule based systems using if…then logic
Created by interviewing experts and codifying their decisioning (vs. decision trees that review past data and performance)
Can have hundreds of thousands of rules (vs. <12 in decision trees)

34. Expert System Problems
Difficult and expensive to manage
Difficult to maintain
Implications of rule changes
Difficult to perform at same level as real experts
Example - medicine

35. How are BI applications delivered?

36. Delivery of Business Intelligence Applications

37. Mgt Functions of BI Servers
Maintains metadata about the authorized allocation of BI results to users
Tracks what results are available, who is authorized to view them, and when the results are provided to users
Options for managing 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
Portal servers – allow for customization of the interface
A report server, a special server dedicated to reports, can supply users with information.

38. Delivery Functions
Characteristics of the delivery function of a BI server:
Tracks authorized users.
Tracks the schedule for providing results to users.
Uses 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.