1. Chapter 9 Business Intelligence Systems
Jason C. H. Chen, Ph.D.
Professor of MIS
School of Business Administration
Gonzaga University
Spokane, WA 99258

2. “We’re Sitting On All This Data. I Want to Make It Pay.”
Anne wants membership data to:
Combine membership data and publicly available data
Enable target marketing
Increase wedding revenue

3. 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 typical reporting applications?
Q5: How do organizations use typical data mining applications?
Q6: What is the role of knowledge management systems?
Q7: What are the alternatives for publishing business intelligence?
Q8: 2022?

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.

5. Working , Not Just Harder
Smarter
Overlapping Human/Organizational (Culture, Process)/ Technological factors in BI/KM:
PEOPLE
ORGANIZATIONAL
PROCESSES
Overlapping Human/Organizational/ Technological factors in KM:
People (workforce)
Organizational Processes
Technology (IT infrastructure)
IS – IT, Organization and Management
TECHNOLOGY
Knowledge N

6. CRM and BI Example
A Grocery store in U.K. with the following “patterns” found:
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
This is an example of cross-selling
Other types of promotion: up-sell, bundled-sell
IT (e.g., BI) helps to find valuable information then decision makers make a timely/right decision for improving/creating competitive advantages.

7. Q/A
Can the “patterns” in the grocery store example be produced from its Database?
Y/N
Why?
It only can be produced from its “Data Warehouse” using a kind of “data mining” software.

8. Q1: How Do Organizations Use Business Intelligence (BI) Systems?
Information systems generate enormous amounts of operational data that contain patterns, relationships, clusters, and trends about customers, suppliers, business partners, and employees that can facilitate management, especially planning and forecasting.
Business intelligence (BI) systems produce such information from operational data.
Data communications and data storage are essentially free, enormous amounts of data (Big Data) are created and stored every day.
12,000 gigabytes per person of data, worldwide in 2009

9. Why do organizations need business intelligence?
BI systems are computer programs provide valuable information for decision making.
Three primary BI systems:
__________ tools read data, process them, and format the data into structured reports (e.g., sorting, grouping, summing, and averaging) that are delivered to users. They are used primarily for assessment. RFM is one of the tool for reporting.
___________ tools process data using statistical, regression, decision tree, and market basket techniques to discover hidden patterns and relationships, and make predictions based on the results
_______________________ tools store employee knowledge, make it available to whomever needs it. These tools are distinguished from the others because the source of the data is human knowledge.
Reporting
Data-mining
Reporting – routine decision
Data mining – analysis
Knowledge – expertise to be retained and reused right away
Knowledge management

10. [1]
[2]
[3] te
Fig 9-1: Structure of a Business Intelligence System

11. Q/A Which of the following is true of source data for a BI system?
A) It refers to the organization's metadata.
B) It refers to data that the organization purchases from data vendors.
C) It refers to the level of detail represented by the data.
D) It refers to the hierarchical arrangement of criteria that predict a classification or a value.
Answer: B

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 people

13. Example Uses of Business Intelligence
[4]
[3]
[2]
(Decision Support Systems)
[1]
Fig 9-2:Example Uses of Business Intelligence

14. The primary activities in the BI process are:
Q2: What Are the Three Primary Activities in the Business Intelligence Process?
The primary activities in the BI process are:
1. ______________
The process of obtaining, cleaning, organizing relating, and cataloging source data.
2. __________
The process of creating BI analysis: reporting, data mining, and knowledge management.
3. ____________
The process of delivering BI to the knowledge workers who need it.
Data acquisition
BI analysis
Publish results

15. What Are the Three Primary Activities in the Business Intelligence Process?
[1]
[2]
[3]
The principle is the same as the “simple” model we learned before. What is it?
Fig 9-3: Three Primary Activities in the BI Process

16. Using BI for Problem-solving at GearUp: Process and Potential Problems
Obtain commitment from vendor
Run sales event
Sell as many items as possible
Order amount actually sold
Receive partial order and damaged items
If received less than ordered, ship partial order to customers
Some customers cancel orders

17. Tables Used for BI Analysis at GearUp
Fig 9-4: Tables Used for BI Analysis at GearUp

18. GearUp Analysis: Item Summary and Lost Sales Summary Reports
Fig 9-5: Extract of the Item_Siummary_Data
Fig 9-6: Lost Sales Summary Report

19. Short and Damaged Shipments Details Report
Fig 9-7: Lost Sales Detail Report

20. 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

21. Why extract operational data for BI processing?
3: 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
Operational data is structured for fast and reliable transaction processing.
Placing BI applications on operational servers can dramatically REDUCE system performance
T/F: Placing BI applications on operational servers can dramatically increase system performance.
Answer:
FALSE
Operational data is structured for fast and reliable “transaction processing” (e.g., payroll).

22. 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.

23. Components of a Data Warehouse
Data warehouses and data marts address the problems companies have with missing data values and inconsistent data. They also help standardize data formats between operational data and data purchased from third-party vendors.
These facilities prepare, store, and manage data specifically for data mining and analyses.
operational data
ETL
ETL: Extract, Transformation, Load
Fig 9-11 Components of a Data Warehouse

24. Data Marts and the Data Warehouse
Legacy systems feed data to the warehouse.
The warehouse feeds specialized information to departments (data marts).
Operational Data Store
Legacy Systems
Finance
Data Mart
Accounting
Marketing
Sales
ETL
Organizational
Data
Warehouse
ETL
ETL: Extract, Transformation, Load

25. Examples of Consumer Data that Can Be Purchased
Fig 9-12 Examples of Consumer Data for Sale

26. Possible Problems with Source (Operational) Data
Fig Possible Problems with Source (Operational) Data

27. Information Cleansing or Scrubbing
Standardizing Customer name from Operational Systems
Ask your students if they have ever received more than one piece of identical mail, such as a flyer, catalog, or application
If so, ask them why this might have occurred
Could it have occurred because their name was in many different disparate systems?
What is the cost to the business of sending multiple identical marketing materials to the same customers?
Expense
Risk of alienating customers
Pat (or Patti) Burton information was entered in different ways and saved in different operational systems (i.e., Sales, Customer Service and Billing). They are, therefore, cleansed by a ‘Cleaning’ software and the cleaned/accurate information was saved in the Customer Information.
They should be created and saved in a single repository (DB) and in a single/consistent form

28. Data Warehouses vs. Data Marts
Here’s the difference between a data warehouse and a data mart:
A data warehouse stores operational data and purchased data. It cleans and processes data as necessary. It serves the entire organization.
A data mart is smaller than a data warehouse and addresses a particular component or functional area of an organization.
Fig 9-14 Data mart Examples

29. 4. How Do Organizations Use Typical Reporting Applications
Four Basic operations:
Sorting  
Filtering
Grouping  
Calculating
Formatting
We will use a ‘reporting application’ to analyze and rank customers based on their purchasing patterns to help company make better decision for increasing company’s revenue.

30. 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.
lower

31. What does RFM analysis Tell? Example RFM Scores
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.
lower
Fig 9-15 Example of RFM Score Data

32. 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
80/20 Rule (Pareto Principle)

33. Q/A
U.S. Grocery Corp. is a large grocery chain store. FOODFARM, one of the customers of U.S. Grocery Corp. holds an RFM score of 111. Which of the following characteristics relates FOODFARM with its RFM score?
A) FOODFARM has ordered recently and orders frequently, but it orders the least expensive goods.
B) FOODFARM has not ordered in some time, but when it did order in the past it ordered frequently, and its orders were of the highest monetary value.
C) FOODFARM has not ordered for some time, it did not order frequently, and, when it
did order, it bought the least-expensive items.
D) FOODFARM has ordered recently and orders frequently, and it orders the most expensive goods.
Answer: D

34. OLAP and its Applications
Online Analytical Processing (OLAP), a second type of reporting tool, is more generic than RFM.
OLAP provides you with the dynamic ability to sum, count, average, and perform other arithmetic operations on groups of data. Reports, also called OLAP cubes.
What software and function that enable you to create OLAP and its applications?
ANSWER
EXCEL with
Pivot table

35. Online Analytical Processing (OLAP)
Online Analytical Processing (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.
Fig Example Grocery Sales OLAP Report
OLAP Product Family by Store Type

36. Example Expanded Grocery Sales OLAP Report
Figure 9-17
Fig 9-17: Example of Expanded Grocery Sales OLAP Report

37. Example of Drilling Down into Expanded Grocery Sales OLAP Report
Fig 9-18: Example of Drilling Down into Expanded Grocery Sales OLAP Report

38. Fig 9 (Extra): Role of OLAP Server & OLAP Database
OLAP servers are special products that 1) read data from an operational database, 2) perform some preliminary calculations, and then3) store the results in an OLAP database
Third-party vendors provide software for more extensive graphical displays

39. 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

40. Slicing a data cube
REGION
Hoffer’s text (chapter 11)
CUSTOMER

41. 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.

42. Database vs. Datawarehouse
DBMS
Datawarehouse
???

43. Database vs. Datawarehouse
DBMS
Datawarehouse
Data Mining

44. How do BI Tools Obtain Data?
ETL
ETL: Extract, Transformation, Load

45. Data-mining Applications
Businesses use 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 (AI) and machine-learning.
Data Warehouse
Fig 9-19 Data Mining Origins

46. Unsupervised vs. Supervised Data Mining
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 (and decision tree), 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 such as Market Basket Analysis 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?

47. Unsupervised vs. Supervised Data Mining
No model before running analysis
Hypotheses created after analysis
Cluster analysis to find groups
Model created before analysis
Hypotheses created before analysis
Regression analysis: make predictions

48. Used for predicting values and making classifications
Neural Networks
Used for predicting values and making classifications
Complicated set of nonlinear equations
Go to and search for “neural network”

49. Probability for BI – Market Basket Analysis (Upselling and Cross-selling)
Support - The probability of two items (A&B) will be purchased together.
P(A&B) = P(A&B)/Total # of transactions
Confidence - Conditional probability is the probability that an event (A) will occur, when another event (B) is known to occur or to have occurred. If the events are A and B respectively, this is said to be "the probability of A given B.
P(A | B) = P(A&B)/P(B)

50. #times an item will be purchased when a customer entering the store
Market Basket Analysis at a Dive Shop (Total # of Transactions (TOT)= 400)
(s1) Purchase Mask and Fins together,
A: Fins
B: Mask
P(A&B)/TOT
P(Fins & Mask) = 250/400=0.625
P(A | B) = P(A&B)/P(B)
P(Fins | Mask)= P(Fins&Mask)/P(Mask)=250/270 = .926
(c1) Proportion of customers who bought a mask also bought fins (buying fins given s/he bought mask)
P(A | B) /P(A)
P(fins | mask)/P(fins)= confidence/base probability=.926/.7=1.32
the lift of fins and mask
Fig 9-20 Market-Basket Analysis at a Dive Shop

51. Market-Basket Analysis is a supervised data-mining tool for determining sales patterns. It helps businesses create cross-selling opportunities (i.e., buying relevant products together). Three terms used with this type of analysis are:
Support: the probability that two items will be purchased together (e.g., Fins and Mask will be purchased together)
Confidence: a conditional probability estimate (e.g., proportion of the customers who bought a mask also bought fins)
Lift: ratio of confidence to the base probability (e.g., ratio between customers of buying fins after buying mask and those buying fins of walking into the store). It shows that how much the based probability increases or decreases when other products are purchased.
Total # of transactions (TOT) = 400 times
Probability of an item that customer will purchase: P(A)/TOT, e.g.,
e.g., (e1) probability of customers entering into the store and buying mask is P(Mask)=270/400=0.675
(e2) probability of customers entering into the store and buying fins is P(Fins)=280/400=0.7
Support : P (A&B)/TOT
e.g., (s1) Purchase Mask and Fins together, P(Fins & Mask) = 250/400=0.625
(s2) Purchase Tank and Dive computer together: P(Tank & Dive computer)=30/400=0.075
Confidence: P(A | B) = P(A&B)/P(B)
e.g., (c1) Proportion of customers who bought a mask also bought fins (buying fins given s/he bought mask)
P(Fins | Mask)= P(Fins&Mask)/P(Mask)=250/270=0.926
We then compare (e2) and (c1) if someone buys a mask, the likelihood that he or she will also buy fins increases substantially from .7 to .926.
Q: If you are a store manager, how will you train your sales personnel?
A: Train them to try to sell fins to anyone buying a mask. (________ selling).
A: Fins; B: Mask
Cross-

52. (continue) Market-Basket Analysis is a supervised data-mining tool for determining sales patterns. It helps businesses create cross-selling opportunities (i.e., buying relevant products together). Three terms used with this type of analysis are:
Support: the probability that two items will be purchased together (e.g., Fins and Mask will be purchased together)
Confidence: a conditional probability estimate (e.g., proportion of the customers who bought a mask also bought fins)
Lift: ratio of confidence to the base probability (e.g., ratio between customers of buying fins after buying mask and those buying fins of walking into the store). It shows that how much the based probability increases or decreases when other products are purchased
e.g., (e2) probability of customers entering into the store and buying fins is P(Fins)=280/400=0.7
Confidence (cont.) P(A | B) = P(A&B)/P(B)
e.g., (c2) Proportion of customers who bought a dive computer also bought fins (i.e., buying fins, given she or he bought a dive computer)
P(Fins | Dive computer)= P(Fins&Dive computer)/P(Dive computer)=20/120=0.167
Thus, someone buys a dive computer, the likelihood that she will also buy fins falls from 0.7 to 0.167
Q: If you are a store manager, how will you take any action on these types of customers?
A: ______
Lift : P(A | B) /P(A)
e.g., the lift of fins and mask, P(fins | mask)/P(fins)=confidence/base probability=.926/.7=1.32.
Thus, the likelihood that people buy fins when they buy a mask increases by 32 percent.
Surprisingly, it turns out the lift of fins and a mask is the same as the lift of a mask and fins. Both are 1.32
Please note that this analysis only shows shopping carts with two items. We cannot say from this data what the likelihood is that customer, given that they bought a mask, will buy both weights and fins NO

53. Q/A
In marketing transactions, the fact that customers who buy product X also buy product Y creates a(n) ________ opportunity. That is, "If they're buying X, sell them Y," or "If they're buying Y, sell them X."
A) cross-selling
B) value added selling
C) break-even
D) portfolio
Answer: A

54. Decision Tree Example for MIS Classes (hypothetical data)
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.
If student is a junior and works in a restaurant, then predict grade
>
If student is a senior and is a nonbusiness major, then predict grade
<
---
If student is a junior and does not work in a restaurant, then predict grade
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.
<
---
If student is a senior and is a business major, then make prediction no
Fig 9-21 Decision Tree Examples for MIS Class (Hypothetical Data)

55. 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
Fig 9-21 Decision Tree Examples for MIS Class (Hypothetical Data)
Fig 9-22 Credit Score Decision Tree

56. 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

57. 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 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.

58. Fig 9-22: Credit Score Decision Tree
more than half paid (Accepted) or
less than half paid
and
Accepted
Figure CE14-4
otherwise
reject the loan.

59. What are typical data-mining applications?

60. Q6. What Is the Role of Knowledge Management Systems?
1. KM fosters innovation by encourage free flow of ideas. 2. KM improves customer service by streamlining response time. 3. BIM boosts revenues by getting products and services to market faster. 4. KM enhances employee retention rates by recognizing the value of employees’ knowledge (sharing) and rewarding them for it. 5. KM streamlines operations and reduce costs by eliminating redundant or unnecessary processes.

61. Sharing Document Content
Indexing - most important content function in KM applications
Only authorized people (employees) are allowed to access to available “Indexing” systems
Real Simple Syndication (RSS) - subscribing to content sources
e.g., With a program called RSS reader, you can subscribe to magazines, blogs, Web sites, and other content sources.
Blogs - place where employees share their knowledge that may include RSS feeds

62. 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.
Knowledge management (KM) – the process of capturing, classifying, evaluating, retrieving, and sharing information assets in a way that provides context for effective decisions and actions.
Knowledge management system (KMS) – an information system that supports the capturing and use of an organization’s “know-how”
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

63. 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.
Tacit
Explicit
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)

64. Tacit and Explicit KNOWLEDGE
Oral Communication
“Tacit” Knowledge
50-95%
Information Request
Explicit Knowledge Base
5 -50 %
“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)
Information Feedback

65. 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 evaluations

66. The Four Modes of Knowledge ConversionTO
Explicit Knowledge
Tacit Knowledge
A. Socialization
(Sympathized Knowledge)
B. Externalization
(Conceptual Knowledge)
Tacit Knowledge
Transferring tacit knowledge through shared experiences, apprenticeships, mentoring relationships, on–the-job training, “Talking at the water cooler”
Articulating and thereby capturing tacit knowledge through use of metaphors, analogies, and models
FROM
C. Internalization
(Operational Knowledge)
D. Combination
(Systematic Knowledge)
Converting explicit knowledge into tacit knowledge; learning by doing; studying previously captured explicit knowledge (manuals, documentation) to gain technical know-how
Combining existing explicit knowledge through exchange and synthesis into new explicit knowledge
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]
Explicit Knowledge C
Which mode is the one for classroom processes? _____
Source: Ikujiro Nonaka and Hirotaka Takeuchi, The Knowledge-Creating Company, 1995

67. Expert Systems Encode human knowledge as Rule-based systems (IF/THEN)
Rules created by interviewing experts (culture issue)
Major problems with ES:
Expensive to develop
Unpredictable maintenance
Over hyped

68. What are Characteristics of Expert Systems?
They capture human expertise and format it for use by nonexperts.
They are rule-based systems that use if…then rules to store the expert’s knowledge.
They gather data from people rather than using data-mining techniques.
They are difficult and expensive to develop.
They are difficult to maintain because the rules are constantly changing.
They have been unable to live up to the high expectations set by their name.
Examples
Medical Expert Systems and
Legal Expert Systems etc.

69. Pharmacy Alert - Expert Systems for Pharmacies
This is an example of the output from a medical expert system that is part of a decision support system. Based on the system’s rules, an alert (for safety) is issued if the system detects a problem with a patient’s prescriptions.
Fig 9-25 Alert from Pharmacy Clinical Decision Support System

70. Q7 What Are the Alternatives for Publishing Business Intelligence?
Fig BI Publishing Alternatives

71. Components of a Generic Business Intelligence System
This figure shows the components of a generic BI system. A BI application server delivers results in a variety of formats to devices for consumption by BI users. A BI server provides two functions: management and delivery.
Fig 9-27 Components of Generic Business Intelligence System

72. What are the Management Functions of a BI Server?
The management function of a BI server
Maintain metadata about the authorized allocation of BI results to users.
It tracks what results are available,
It tracks who is authorized to view them, and
It tracks when the results are provided to users.
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.
Answer: “PULL” – automatically delivered
Which option is for the “Grocery Store (UK)” case?
Push

73. 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

74. 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 *

75. 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.

76. Essential Value Propositions for a Successful Company
Business ________
_______ Competency
Outsourcing
Crowdsourcing
Offshoring
________
Set corporate goals and get executive sponsorship for the initiative
Model
Core
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/FORD
Execution

77. Any Sustainable Knowledge?
Most sustainable Knowledge is
“Learning to Learn and Learning to Change.”
CAPACITY TO LEARN and how to adapt to change

78. Companies will know more about your purchasing habits and psyche.
Q8: 2022
Companies will know more about your purchasing habits and psyche.
Social singularity — machines can build their own information systems.
Will machines possess and create information for themselves?

79. End of Chapter 9