Business Intelligence and Knowledge Management

1
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

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Data-mining Tool

• Programs
• 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

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Reporting System - I

• Purpose
• 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
• Static
• Dynamic
• Query
• Online analytical process (dynamic grouping
  structure)
• Report media
• Paper
• Voice
• Digital screen, digital dashboard, Web service,
  email alert

8
Reporting System - III

• Report mode
• 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)

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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

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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).

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OLAP

• Characteristics
• 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 reports 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

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Role of OLAP Server and OLAP Database
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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
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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

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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

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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

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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

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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

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Issues of Knowledge Sharing

• Problems
• Competition
• Shy
• Strategy
• Reward
• Incentive

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Discussion

• Security (275a-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?

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Case Study

• Case 9-1 Laguna Tools (300-301) every question

31
Points to Remember

• Business Intelligence System
• Reporting system
• Data Warehouse
• Data Mart
• Knowledge Management Systems
• Discussion and Case Study