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Chapter 9 Business Intelligence Systems. Jason C. H. Chen, Ph.D. Professor of MIS School of Business Administration Gonzaga University Spokane, WA 99258 chen@jepson.gonzaga.edu. “We’re Sitting On All This Data. I Want to Make It Pay.”. Anne wants membership data to:

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chapter 9 business intelligence systems

Chapter 9Business Intelligence Systems

Jason C. H. Chen, Ph.D.

Professor of MIS

School of Business Administration

Gonzaga University

Spokane, WA 99258

chen@jepson.gonzaga.edu

we re sitting on all this data i want to make it pay
“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
study questions
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?

business intelligence
BUSINESS INTELLIGENCE
  • Business intelligence – information that people use to support/improve their decision-making efforts
  • Principle BI enablers include:
    • Technology
    • People
    • Culture
working not just harder
Working , Not Just Harder

Smarter

  • Overlapping Human/Organizational (Culture, Process)/ Technological factors in BI/KM:

PEOPLE

ORGANIZATIONAL

PROCESSES

TECHNOLOGY

  • Knowledge

N

crm and bi example
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
    • _______ and
    • ________
  • 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.
slide7
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.
q1 how do organizations use business intelligence bi systems
Q1: How Do Organizations Use Business Intelligence (BI) Systems?
  • Information systems generate enormous amounts of operational data that contain patterns, relationships, clusters, and trendsabout 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
data base data warehouse and data marts
Data Base, Data Warehouse and Data Marts
  • __________: An organized collection of logically related (current) data files.
  • ________________ : 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.
  • ___________ : a subset of data warehouse that holds specific subsets of data for one particular functional area or project.
why do organizations need business intelligence
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 hiddenpatterns and relationships, and make predictionsbased 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.
slide11

[1]

[2]

[3]

Fig 9-1: Structure of a Business Intelligence System

slide12
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:

tools vs applications vs systems
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.
example uses of business intelligence
Example Uses of Business Intelligence

[4]

[3]

[2]

(Decision Support Systems)

[1]

Fig 9-2:Example Uses of Business Intelligence

q2 what are the three primary activities in the business intelligence process
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.
what are the three primary activities in the business intelligence process
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

what are the three primary activities in the business intelligence process1
What Are the Three Primary Activities in the Business Intelligence Process?

I

P

O

[1]

[2]

[3]

Fig 9-3: Three Primary Activities in the BI Process

using bi for problem solving at gearup process and potential problems
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
tables used for bi analysis at gearup
Tables Used for BI Analysis at GearUp

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

gearup analysis item summary and lost sales summary reports
GearUp Analysis: Item Summary andLost Sales Summary Reports

Fig 9-5: Extract of the Item_Siummary_Data

Fig 9-6: Lost Sales Summary Report

short and damaged shipments details report
Short and Damaged Shipments Details Report

Fig 9-7: Lost Sales Detail Report

3 how do organizations use data warehouses and data marts to acquire data
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

T/F: Placing BI applications on operational servers can dramatically increase system performance.

Answer:

_____

Operational data is structured for fast and reliable “transaction processing” (e.g., payroll).

data base data warehouse and data marts1
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.
components of a data warehouse
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.

Fig 9-11 Components of a Data Warehouse

operational data

ETL

ETL: Extract, Transformation, Load

data marts and the data warehouse

Legacy Systems

Sales

Data Mart

Finance

Data Mart

Operational Data Store

Marketing

Data Mart

Accounting

Data Mart

Operational Data Store

Operational Data Store

Operational Data Store

Data Marts and the Data Warehouse

Legacy systems feed data to the warehouse.

The warehouse feeds specialized information to departments (data marts).

ETL

Organizational

Data

Warehouse

ETL

ETL: Extract, Transformation, Load

examples of consumer data that can be purchased
Examples of Consumer Data that Can Be Purchased

Fig 9-12 Examples of Consumer Data for Sale

possible problems with source operational data
Possible Problems with Source (Operational) Data

Fig 9-13 Possible Problems with Source (Operational) Data

information cleansing or scrubbing
Information Cleansing or Scrubbing
  • Standardizing Customer name from Operational Systems
data warehouses vs data marts
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

4 how do organizations use typical reporting applications
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.
what are typical reporting applications
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

interpreting rfm score results
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)

interpreting rfm score results1
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)

slide34
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:

olap and its applications
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
    • _______ with
    • ____ table
online analytical processing olap
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 9-16 Example Grocery Sales OLAP Report

OLAP Product Family by Store Type

example expanded grocery sales olap report
Example Expanded Grocery Sales OLAP Report

Figure 9-17

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

example of drilling down into expanded grocery sales olap report
Example of Drilling Down into Expanded Grocery Sales OLAP Report

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

slide39

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

Fig 9 (Extra): Role of OLAP Server & OLAP Database

Third-party vendors provide software for more extensive graphical displays

on line analytic processing olap
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
slide41

REGION

CUSTOMER

  • Slicing a data cube
data base data warehouse and data marts2
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.
database vs datawarehouse
Database vs. Datawarehouse

Database

DBMS

Datawarehouse

???

database vs datawarehouse1
Database vs. Datawarehouse

Database

DBMS

Datawarehouse

Data Mining

q5 how do organizations use typical data mining applications1
Q5  How Do Organizations Use Typical 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.

Fig 9-19 Data Mining Origins

Data Warehouse

unsupervised vs supervised data mining
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
unsupervised vs supervised data mining1
Unsupervised vs. Supervised Data Mining

Unsupervised

Supervised

Model created before analysis

Hypotheses created before analysis

Regression analysis: make predictions

  • No model before running analysis
  • Hypotheses created after analysis
  • Cluster analysis to find groups
neural networks
Neural Networks
  • Used for predicting values and making classifications
  • Complicated set of nonlinear equations
  • Go to http://kdnuggets.com and search for “neural network”
probability for bi market basket analysis upselling and cross selling
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)

market basket analysis at a dive shop total of transactions tot 400

#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

(c1) Proportion of customers who bought a mask also bought fins (buying fins given s/he bought mask)

P(A | B) = P(A&B)/P(B)

P(Fins | Mask)= P(Fins&Mask)/P(Mask)=250/270 = .926

P(A | B) /P(A)

the lift of fins and mask

P(fins | mask)/P(fins)= confidence/base probability=.926/.7=1.32

Fig 9-20 Market-Basket Analysis at a Dive Shop

slide53

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 enteringinto 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-

slide54

(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 computeralso 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

slide55
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:

decision tree example for mis classes hypothetical data
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 3.0

>

If student is a senior and is a nonbusiness 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 prediction

no

Fig 9-21 Decision Tree Examples for MIS Class (Hypothetical Data)

summary of decision tree analysis
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.

Fig 9-21 Decision Tree Examples for MIS Class (Hypothetical Data)

Fig 9-22 Credit Score Decision Tree

a decision tree for a loan evaluation
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
a decision tree for a loan evaluation1
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 CreditScoreis greater than 572.6 and
      • If CurrentLTVis 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.
fig 9 22 credit score decision tree
Fig 9-22: Credit Score Decision Tree

more than half paid (Accepted)

less than half paid

or

Accepted

and

Figure CE14-4

otherwise

reject the loan.

q6 what is the role of knowledge management systems
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.

sharing document content
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
knowledge management
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”
tacit vs explicit knowledge
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 and explicit knowledge
Tacit and Explicit KNOWLEDGE

Oral Communication

“Tacit” Knowledge

50-95%

Explicit Knowledge Base

5 -50 %

Information Request

“Explicit” Knowledge

Information Feedback

explicit and tacit knowledge
Explicit and Tacit Knowledge
  • Reasons why organizations launch knowledge management programs
the f our m odes of k nowledge c onversion
The Four Modes of Knowledge Conversion

TO

Explicit Knowledge

Tacit Knowledge

A. ___________

(Sympathized Knowledge)

B. ___________

(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

D. ___________

(Systematic Knowledge)

C. ___________

(Operational 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

Explicit Knowledge

Which mode is the one for classroom processes? _____

Source: IkujiroNonaka and Hirotaka Takeuchi, The Knowledge-Creating Company, 1995

expert systems
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
what are characteristics of expert systems
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.
pharmacy alert expert systems for pharmacies
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

q7 what are the alternatives for publishing business intelligence
Q7  What Are the Alternatives for Publishing Business Intelligence?

Fig 9-26 BI Publishing Alternatives

components of a generic business intelligence system
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

what are the management functions of a bi server
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.

Which option is for the “Grocery Store (UK)” case?

data mining
DATA MINING
  • Data-mining software includes many forms of AI such as neural networks and expert systems
other data mining examples
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.
data mining examples cont
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.
  • 82% of customers who bought a new TV 27" or larger are 90% likely to buy an entertainment center within the next 4 weeks.
essential value propositions for a successful company
Essential Value Propositions for a Successful Company
  • Business ________
  • _______ Competency
    • Outsourcing
    • Crowdsourcing
    • Offshoring
  • ________
    • Set corporate goals and get executive sponsorship for the initiative
any sustainable knowledge
Any Sustainable Knowledge?
  • Most sustainable Knowledge is
  • “Learning to Learn and Learning to Change.”

CAPACITY TO LEARN and how to adapt to change

q8 2022
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?