1. Business Intelligence and Knowledge Management
Chapter 9
Business Intelligence and Knowledge Management

2. Agenda Business Intelligence System Reporting system Data Warehouse
Data Mart
Knowledge Management Systems
Discussion and Case Study

3. Business Intelligence System
Need
Inexpensive storage
Drowning in data (terabyte - 12, petabyte - 15, exabyte - 18)
Starving for useful information
Purpose
Provide the right information, to the right user, at the right time for actions
Business intelligence tool
Searching business data for finding patterns
Types: reporting tool and data-mining tool

4. Reporting Tool Programs
Read data from sources
Sort and group data
Calculate simple totals and averages
Produce reports
Deliver reports to the users
For business assessment: a customer canceling an important order

5. Data-mining Tool Programs For business prediction using probability
Use sophisticated statistical techniques and complex mathematics
Search for patterns and relationships among data
For business prediction using probability
Calculating the probability of a customer defaulting on a loan
Assessing new loan applications

6. Reporting System - I Purpose Operation Component
Create meaningful information from disparate data sources and to deliver that information to the proper user on a timely basis
Operation
Filtering data
Sorting data
Grouping data
Making simple calculations
Component
A database of reporting metadata with description of reports, users, groups, roles, events, and other entities in the reporting activity

7. Reporting System - II Report type Report media Static Dynamic Query
Online analytical process (dynamic grouping structure)
Report media
Paper
Voice
Digital: screen, digital dashboard, Web service, alert

8. Reporting System - III Report mode Function Example
Push: preset schedule
Pull: user request
Function
Authoring: connecting to data sources, creating report structure, and formatting report
Management: who, what, when, by what mean, user account, and user group
Delivery: push or pull, method, time
Example
RFM analysis
Online analytical processing (OLAP)

9. RFM Analysis
Analyzing and ranking customers according to their purchasing patterns
How recently (R) a customer has ordered
How frequently (F) a customer orders
How much money (M) the customer spends per order

10. RFM Score
The program first sorts customer purchase records by the date of their most recent (R) purchase
The program then divides the customers into five groups and gives customers in each group a score of 1 to 5.
The top 20% of the customers having the most recent orders are given an R score 1 (highest).
The program then re-sorts the customers on the basis of how frequently they order.
The top 20% of the customers who order most frequently are given a F score of 1 (highest).
Finally the program sorts the customers again according to the amount spent on their orders.
The 20% who have ordered the most expensive items are given an M score of 1 (highest).

11. OLAP Characteristics Component
Provide the ability to sum, count, average, and other simple arithmetic operations on groups of data
Display the current state of the business
The viewer can dynamically the report’s format
Drill down (detail data)
Component
Measure: the data item of interest (total, average)
Dimension: a characteristic of a measure (customer type, sales region)
OLAP server & OLAP database: store results from operational databases

12. Role of OLAP Server and OLAP Database

13. Problems with Operational Data
Problematic data (dirty data)
Missing elements
Inconsistent data
Nonintegrated data
Too fine or too coarse (clickstream data)
Wrong granularity (format)
Curse of dimensionality: the more attributes, the easier to build a model to fit the sample data but worthless as a predictor

14. Data Warehouse
Programs read operational data and extract, clean, and prepare data for business intelligence processing
Data-warehouse DBMS
Extract and provide data to business intelligence tools such as data-mining programs
Internal data and purchased from outside sources
Metadata: source, format, assumption, constraint, and other facts about the data

15. Components of a Data Warehouse

16. Data Mart
A data collection, smaller than the data warehouse, to address a particular component or functional area of the business
Expensive to create, staff, and operate data warehouse and data mart

17. Data Mart Examples

18. Data Mining
The application of statistical and mathematic techniques to find patterns and relationships among data for classifying and predicting
From artificial intelligence and machine-learning
Type
Unsupervised data mining
Supervised data mining

19. Convergence Disciplines for Data Mining

20. Unsupervised Data Mining
No model or hypothesis before running the analysis
Apply the data-mining technique to the data and observe the results
Create hypotheses after the analysis to explain the patterns found
Cluster analysis
Find groups of similar customers from customer order and demographic data
Decision Tree
A hierarchical arrangement of criteria to predict a classification or a value
Loan-decision rules

21. Supervised Data Mining
Develop a model prior to the analysis and apply statistical techniques to data to estimate parameters of the model
Regression analysis
Measure the impact of a set of variables on another variable
Neural network
Predict values and make classifications such as “good prospect” or “poor prospect” customers.

22. Market-Basket Analysis
A data-mining technique for determining sales patterns
Show the products that customers tend to buy together
Support: the probability that two items will be purchased together
A standard CRM analysis

23. Knowledge Management (KM)
The process of creating value from intellectual capital and sharing that knowledge with employees, managers, suppliers, customers, and others
Emphasis is on people, their knowledge, and effective means for sharing that knowledge with others
Preserve organizational memory by capturing and storing the lessons learned and best practices of key employees
Enable employees and others to leverage organizational knowledge to work smarter

24. Benefits of KM Free flow of ideas (innovation)
Storing lesson learned and best practice
Better customer service
Boosting profit by getting product to the market faster
Increasing employee retention
Reducing cost by eliminating redundant and unnecessary process

25. KM Content Management - I
Track organizational documents, Web pages, graphics, and related materials
Concern with the creation, management, and delivery of documents for a specific KM purpose

26. KM Content Management -II
Problems
Complicated and huge
Dependency relationship between documents
Perishable document contents
Multinational languages
Delivering methods
Pull using index and search engine
Web browsers

27. Knowledge Sharing Portals, discussion groups, and email
Idea publishing
Bulletin board
Frequent ask question
Collaborations system
Web broadcast
Video conference
Net meeting
Expert system
Decision tree with narrow domain and complex rules
Expensive and difficult to create and maintain

28. Issues of Knowledge Sharing
Problems
Competition
Shy
Strategy
Reward
Incentive

29. Discussion Security (275a-b) Problem Solving (283a-b) Ethics (289a-b)
State some methods for an organization to prevent the semantic security problems.
Problem Solving (283a-b)
State two statistic usages and its associated risks in a business decision making process.
Ethics (289a-b)
State some disadvantages of using decision tree as the admission rules.
Reflections (295a-b)
Is it a common practice of lower management to manipulate the data and generate the information to accommodate the needs of upper management in the real business world? How do you avoid this situation as the upper management?

30. Case Study
Case 9-1 Laguna Tools ( ) every question

31. Points to Remember Business Intelligence System Reporting system
Data Warehouse
Data Mart
Knowledge Management Systems
Discussion and Case Study