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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, you'll learn tools and
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1. Chapter 9Business Intelligence Systems 1
2. 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, you’ll learn tools and techniques that MRV can use to identify the guides that contribute the most (and least) to its competitive strategy.
We’ll wrap up by discussing some of the potential benefits and risks of mining credit card data.
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3. 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?
4. BUSINESS INTELLIGENCE Business intelligence – information that people use to support/improve their decision-making efforts
Principle BI enablers include:
Technology
People
Culture
Technology
Even the smallest company with BI software can do sophisticated analyses today that were unavailable to the largest organizations a generation ago. The largest companies today can create enterprisewide BI systems that compute and monitor metrics on virtually every variable important for managing the company. How is this possible? The answer is technology—the most significant enabler of business intelligence.
People
Understanding the role of people in BI allows organizations to systematically create insight and turn these insights into actions. Organizations can improve their decision making by having the right people making the decisions. This usually means a manager who is in the field and close to the customer rather than an analyst rich in data but poor in experience. In recent years “business intelligence for the masses” has been an important trend, and many organizations have made great strides in providing sophisticated yet simple analytical tools and information to a much larger user population than previously possible.
Culture
A key responsibility of executives is to shape and manage corporate culture. The extent to which the BI attitude flourishes in an organization depends in large part on the organization’s culture. Perhaps the most important step an organization can take to encourage BI is to measure the performance of the organization against a set of key indicators. The actions of publishing what the organization thinks are the most important indicators, measuring these indicators, and analyzing the results to guide improvement display a strong commitment to BI throughout the organization.Technology
Even the smallest company with BI software can do sophisticated analyses today that were unavailable to the largest organizations a generation ago. The largest companies today can create enterprisewide BI systems that compute and monitor metrics on virtually every variable important for managing the company. How is this possible? The answer is technology—the most significant enabler of business intelligence.
People
Understanding the role of people in BI allows organizations to systematically create insight and turn these insights into actions. Organizations can improve their decision making by having the right people making the decisions. This usually means a manager who is in the field and close to the customer rather than an analyst rich in data but poor in experience. In recent years “business intelligence for the masses” has been an important trend, and many organizations have made great strides in providing sophisticated yet simple analytical tools and information to a much larger user population than previously possible.
Culture
A key responsibility of executives is to shape and manage corporate culture. The extent to which the BI attitude flourishes in an organization depends in large part on the organization’s culture. Perhaps the most important step an organization can take to encourage BI is to measure the performance of the organization against a set of key indicators. The actions of publishing what the organization thinks are the most important indicators, measuring these indicators, and analyzing the results to guide improvement display a strong commitment to BI throughout the organization.
5. Working , Not Just Harder Overlapping Human/Organizational (Culture, Process)/ Technological factors in BI/KM: Overlapping Human/Organizational/ Technological factors in KM:
People (workforce)
Organizational Processes
Technology (IT infrastructure)
IS – IT, Organization and Management
Overlapping Human/Organizational/ Technological factors in KM:
People (workforce)
Organizational Processes
Technology (IT infrastructure)
IS – IT, Organization and Management
6. 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.
7. 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
8. How Big Is an Exabyte? (See video) Fig 9-1 How Big is an Exabyte?
9. 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?
10. 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
11. 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
12. 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. Five components: H/SW, data, procedure and peopleFive components: H/SW, data, procedure and people
13. 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?
14. Basic Reporting Operations Reporting tools produce information from data using five basic operations:
Sorting
Grouping
Calculating
Filtering
Formatting
15. List of Sales Data
16. Data Sorted by Customer Name
17. 9-17 The figure on the left shows the raw sales data sorted by customer names.
The figure on the right shows data that’s been sorted and grouped.
18. Sales Data Filtered to ShowRepeat Customers and Formatted for Easier Understanding Fig 9-5 Sales Data Filtered to Show Repeat Customers
19. 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? => leads and opportunities
Frequency: How frequently a customer purchased items? => retention
Monetary Value: How much a customer spends on each purchase? => 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.
20. 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.
21. 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
22. 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
23. 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.
24. 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
25. 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 report’s format, hence the term online.
26. 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 manipulated—total sales, average cost
Dimension
Characteristic of measure—purchase date, customer type, location, sales region
27. 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.
28. 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
29. Fig 9-8 OLAP Product Family & Store Location by Store Type
30. Fig 9-9 OLAP Product Family & Store Location by Store Type, Drilled Down to Show Stores in California
31. Fig 9-10 Role of OLAP Server & OLAP Database
32. 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 >>> Product Line >>> Division
Drill-down – ability to move from summary/general to lower/specific levels of detail
Revenue by Year >>> Quarter >>>>Week >>>Day
Slice and Dice – ability to look across dimensions
Sales by Region Sales
Profit and Revelers by Product Line
33. Hoffer’s text (chapter 11)Hoffer’s text (chapter 11)
34. 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?
35. 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.
36. Database vs. Datawarehouse
37. Database vs. Datawarehouse
38. How do BI Tools Obtain Data?
39. Fig 9-11 Convergence Disciplines for Data Mining What are typical data-mining applications?
40. 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
Data mining can begin at a summary information level (coarse granularity) and progress through increasing levels of detail (drilling down), or the reverse (drilling up)
Data-mining tools include query tools, reporting tools, multidimensional analysis tools, statistical tools, and intelligent agents
Ask your students to provide an example of what an accountant might discover through the use of data-mining tools
Ans: An accountant could drill down into the details of all of the expense and revenue finding great business intelligence including which employees are spending the most amount of money on long-distance phone calls to which customers are returning the most products
Could the data warehousing team at Enron have discovered the accounting inaccuracies that caused the company to go bankrupt?
If the did spot them, what should the team have done?
Data mining can begin at a summary information level (coarse granularity) and progress through increasing levels of detail (drilling down), or the reverse (drilling up)
Data-mining tools include query tools, reporting tools, multidimensional analysis tools, statistical tools, and intelligent agents
Ask your students to provide an example of what an accountant might discover through the use of data-mining tools
Ans: An accountant could drill down into the details of all of the expense and revenue finding great business intelligence including which employees are spending the most amount of money on long-distance phone calls to which customers are returning the most products
Could the data warehousing team at Enron have discovered the accounting inaccuracies that caused the company to go bankrupt?
If the did spot them, what should the team have done?
41. Decision Tree Analysis of MIS Class Grades Student’s 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 criterion—Class
Next subdivide Seniors and Juniors into more pure groups
Seniors—business and non-business majors
Juniors—restaurant employees and non-restaurant employees
Best classifier is whether the junior worked in a restaurant
42. Create Set of If/Then Decision Rules If student is a junior and works in a restaurant, then predict grade > 3.0.
If student is a senior and is a non-business 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 no prediction.
43. 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
44. Summary of Decision Tree Analysis A decision tree is a hierarchical arrangement of criteria that predicts a classification or value. It’s an unsupervised data-mining technique that selects the most useful attributes for classifying entities on some criterion. It uses if…then rules in the decision process. Here are two examples. Since major is not significant to JUNIORS in the study.
Make no prediction since they are 50/50
The more different groups, the better the classification will be.
In this example, we are classifying students depending on whether their grade was greater than 3.0 or less or equal to 3.0.
For example, if every student who lived off campus earned a grade higher than 3.0, and every student who lived on campus earned a grade lower than 3.0then the program would use the variable live-off-campus or live-on-campus to classify students.
Since major is not significant to JUNIORS in the study.
Make no prediction since they are 50/50
The more different groups, the better the classification will be.
In this example, we are classifying students depending on whether their grade was greater than 3.0 or less or equal to 3.0.
For example, if every student who lived off campus earned a grade higher than 3.0, and every student who lived on campus earned a grade lower than 3.0then the program would use the variable live-off-campus or live-on-campus to classify students.
45. Decision Tree Figure CE16-3
46. 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
47. 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
48. 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.
49. Fig 9-14: Credit Score Decision Tree
50. 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”
51. Market-Basket Analysis is a data-mining tool for determining sales patterns. It helps businesses create cross-selling opportunities (i.e., buying relevant products together). Or even promote bundle-selling (for buying “fins”) with discount for those customers bought “mask” since they are likely to buy “fins” after purchasing “mask”.
P(Fins)=280/1000=.28
P(Mask)=270/1000=.27
Market-Basket Analysis is a data-mining tool for determining sales patterns. It helps businesses create cross-selling opportunities (i.e., buying relevant products together). Or even promote bundle-selling (for buying “fins”) with discount for those customers bought “mask” since they are likely to buy “fins” after purchasing “mask”.
P(Fins)=280/1000=.28
P(Mask)=270/1000=.27
52. 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
53. 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
54. 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.
55. 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 customer’s age, plus 23.7 times the number of months of the account.
Considerable skill is required to interpret the quality of such a model
56. 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
57. What are typical data-mining applications?
58. DATA MINING Data-mining software includes many forms of AI such as neural networks and expert systems
Data-mining tools apply algorithms to information sets to uncover inherent trends and patterns in the information
Analysts use this information to develop new business strategies and business solutions
Ask your students to identify an organization that would “not” benefit from investing in data warehousing and data-mining tools
Ans: None
CLASSROOM EXERCISE
Analyzing Multiple Dimensions of Information
Jump! is a company that specializes in making sports equipment, primarily basketballs, footballs, and soccer balls. The company currently sells to four primary distributors and buys all of its raw materials and manufacturing materials from a single vendor. Break your students into groups and ask them to develop a single cube of information that would give the company the greatest insight into its business (or business intelligence).
Product A, B, C, and D
Distributor X, Y, and Z
Promotion I, II, and III
Sales
Season
Date/Time
Salesperson Karen and John
Vendor Smithson
Data-mining tools apply algorithms to information sets to uncover inherent trends and patterns in the information
Analysts use this information to develop new business strategies and business solutions
Ask your students to identify an organization that would “not” benefit from investing in data warehousing and data-mining tools
Ans: None
CLASSROOM EXERCISE
Analyzing Multiple Dimensions of Information
Jump! is a company that specializes in making sports equipment, primarily basketballs, footballs, and soccer balls. The company currently sells to four primary distributors and buys all of its raw materials and manufacturing materials from a single vendor. Break your students into groups and ask them to develop a single cube of information that would give the company the greatest insight into its business (or business intelligence).
Product A, B, C, and D
Distributor X, Y, and Z
Promotion I, II, and III
Sales
Season
Date/Time
Salesperson Karen and John
Vendor Smithson
59. 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 Data mining can begin at a summary information level (coarse granularity) and progress through increasing levels of detail (drilling down), or the reverse (drilling up)
Data-mining tools include query tools, reporting tools, multidimensional analysis tools, statistical tools, and intelligent agents
Ask your students to provide an example of what an accountant might discover through the use of data-mining tools
Ans: An accountant could drill down into the details of all of the expense and revenue finding great business intelligence including which employees are spending the most amount of money on long-distance phone calls to which customers are returning the most products
Could the data warehousing team at Enron have discovered the accounting inaccuracies that caused the company to go bankrupt?
If the did spot them, what should the team have done?
Data mining can begin at a summary information level (coarse granularity) and progress through increasing levels of detail (drilling down), or the reverse (drilling up)
Data-mining tools include query tools, reporting tools, multidimensional analysis tools, statistical tools, and intelligent agents
Ask your students to provide an example of what an accountant might discover through the use of data-mining tools
Ans: An accountant could drill down into the details of all of the expense and revenue finding great business intelligence including which employees are spending the most amount of money on long-distance phone calls to which customers are returning the most products
Could the data warehousing team at Enron have discovered the accounting inaccuracies that caused the company to go bankrupt?
If the did spot them, what should the team have done?
60. Other Data Mining Examples A telephone company used a data mining tool to analyze their customer’s 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
*
61. 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 < 30 and income <= $25,000 and credit rating < 3 and credit amount > $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.
62. 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?
63. 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
64. 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.
65. What is the purpose of data warehouses and data marts? Fig 9-15 Components of a Data Warehouse
67. 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
68. 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.
70. Fig 9-16: Example Typical of Customer Credit Data
71. Problems with Operational Data Dirty data—mistakes in spelling or punctuation, incorrect data associated with a field, incomplete or outdated data or even data that is duplicated in the database.
72. Examples of Dirty Data A value of “B” for customer gender
213 for customer age
Value of 999–999–9999 for a U.S. phone number
Part color of “gren”
mail address of WhyMe@GuessWhoIAm.org.
73. 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? 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. Why is knowledge one of the real competitive advantages?
It is difficult to duplicate knowledge
It can take years to acquire
It is a personal asset
What if an organization could capture all of a persons knowledge using technology?
You would no longer need that person in the organization
Why is knowledge one of the real competitive advantages?
It is difficult to duplicate knowledge
It can take years to acquire
It is a personal asset
What if an organization could capture all of a persons knowledge using technology?
You would no longer need that person in the organization
78. 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. Four modes of K conversion between Tacit K and explicit K
Tacit to Tacit - Socialization (Sympathized Knowledge)
Tacit to explicit - Externalization (Conceptual Knowledge)
Explicit to tacit – Internalization (Operational Knowledge)
Explicit to explicit – Combination (Systematic Knowledge)
Four modes of K conversion between Tacit K and explicit K
Tacit to Tacit - Socialization (Sympathized Knowledge)
Tacit to explicit - Externalization (Conceptual Knowledge)
Explicit to tacit – Internalization (Operational Knowledge)
Explicit to explicit – Combination (Systematic Knowledge)
79. Tacit and Explicit KNOWLEDGE
Intellectual and knowledge-based assets fall into two categories
Explicit knowledge – consists of anything that can be documented, archived, and codified, often with the help of IT
Tacit knowledge - knowledge contained in people’s heads
Four modes of K conversion between Tacit K and explicit K
Tacit to Tacit - Socialization (Sympathized Knowledge)
Tacit to explicit - Externalization (Conceptual Knowledge)
Explicit to tacit – Internalization (Operational Knowledge)
Explicit to explicit – Combination (Systematic Knowledge)Intellectual and knowledge-based assets fall into two categories
Explicit knowledge – consists of anything that can be documented, archived, and codified, often with the help of IT
Tacit knowledge - knowledge contained in people’s heads
Four modes of K conversion between Tacit K and explicit K
Tacit to Tacit - Socialization (Sympathized Knowledge)
Tacit to explicit - Externalization (Conceptual Knowledge)
Explicit to tacit – Internalization (Operational Knowledge)
Explicit to explicit – Combination (Systematic Knowledge)
80. Explicit and Tacit Knowledge Reasons why organizations launch knowledge management programs Intellectual and knowledge-based assets fall into two categories
Explicit knowledge – consists of anything that can be documented, archived, and codified, often with the help of IT
Tacit knowledge - knowledge contained in people’s heads
What types of knowledge management programs could your college pursue to help new students adapt to the college?
Effective study habits
Writing rules
Research database
Course evaluationsIntellectual and knowledge-based assets fall into two categories
Explicit knowledge – consists of anything that can be documented, archived, and codified, often with the help of IT
Tacit knowledge - knowledge contained in people’s heads
What types of knowledge management programs could your college pursue to help new students adapt to the college?
Effective study habits
Writing rules
Research database
Course evaluations
81. The Four Modes of Knowledge Conversion Four modes of K conversion between Tacit K and explicit K
Tacit to Tacit - Socialization (Sympathized Knowledge) [sharing K thru conversation]
Tacit to explicit - Externalization (Conceptual Knowledge) [studying/learning from lectures ? HTML hws]
Explicit to tacit – Internalization (Operational Knowledge) [reading text ? your own knowledge]
Explicit to explicit – Combination (Systematic Knowledge) [Many text books/ Google search ? your paper]
Four modes of K conversion between Tacit K and explicit K
Tacit to Tacit - Socialization (Sympathized Knowledge) [sharing K thru conversation]
Tacit to explicit - Externalization (Conceptual Knowledge) [studying/learning from lectures ? HTML hws]
Explicit to tacit – Internalization (Operational Knowledge) [reading text ? your own knowledge]
Explicit to explicit – Combination (Systematic Knowledge) [Many text books/ Google search ? your paper]
82. 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:
Indexing—most 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
87. Fig 20: Blog Posts of SharePoint Team Member
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 hospital’s 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
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?
94. 9-94 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. Fig 9-23 Sample Portal, Provided by iGoogle BI Servers Vary in Complexity and Functionality and could operate as a portal server.
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 First, you have to have a business model, then, the company needs to set corporate goals and get executive sponsorship for the initiative."
"Start with your business objectives of the product or service the company is selling, figure out where it is in the lifecycle, and determine which phase of CRM to focus on, for example, the company should determine whether it wants to focus on acquiring customers, retaining customers or up-selling and cross selling to customers."
Examples: Dell vs. Gateway and Toyota vs. GM/FORDFirst, you have to have a business model, then, the company needs to set corporate goals and get executive sponsorship for the initiative."
"Start with your business objectives of the product or service the company is selling, figure out where it is in the lifecycle, and determine which phase of CRM to focus on, for example, the company should determine whether it wants to focus on acquiring customers, retaining customers or up-selling and cross selling to customers."
Examples: Dell vs. Gateway and Toyota vs. GM/FORD
102. Any Sustainable Knowledge? Most sustainable Knowledge is
“Learning to Learn and Learning 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 consumer’s purchases than we’ll know ourselves.
105. END of Chapter 9 105