Chapter 9

Business Intelligence and Information Systems for

Decision Making

Q1: How big is an exabyte, and why does it matter?

Q2: How do business intelligence (BI) systems provide competitive advantages?

Q3: What problems do operational data pose for BI systems?

Q4: What are the purpose and components of a data warehouse?

Q5: What is a data mart, and how does it differ from a data warehouse?

Q6: What are the characteristics of data-mining systems?

Q7: What are OLAP reports?

Study Questions

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Q1: How Big Is an Exabyte?

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• Businesses collect massive amounts of data– Storage capacity is increasing as cost is decreasing– Storage capacity is becoming almost unlimited, so

businesses collect more at little extra cost

• Buried in that data are important patterns of relationships that can yield valuable information to help businesses make better decisions

Q1: Why Does an Exabyte Matter?

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• Primary BI systems:

1. Reporting systems • Integrate data from multiple systems• Sorting, grouping, summing, averaging, comparing data

2. Data-mining systems • Use sophisticated statistical techniques, regression

analysis, and decision tree analysis• Used to discover hidden patterns and relationships• Market-basket analysis –purchasing patterns

Q2: How Do Business Intelligence (BI) Systems Provide Competitive Advantages?

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3. Knowledge management systems (KMs)• Create value by collecting and sharing human

knowledge about products, products uses, best practices, other critical knowledge

• Used by employees, managers, customers, suppliers, others who need access to company knowledge

4. Expert systems (ES)• Encapsulates knowledge in form of “If/Then” rules

– If Patient_Temp > 103, Then start High_Fever_Procedure

• ES can improve diagnostic and decision quality of non-experts

Q2: How Do Business Intelligence (BI) Systems Provide Competitive Advantages? (cont’d)

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• Dirty data. – Values may be missing– Inconsistent data– Non-integrated data– Wrong granularity (Coarse vs. Fine)

• Too much data causes:1.Curse of dimensionality

• Problem caused by the exponential increase in volume associated with adding extra dimensions to a (mathematical) space.

2.Too many rows or data points

Q3: What Problems Do Operational Data Pose for BI Systems?

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• Purpose:– To extract and clean data from various

operational systems and other sources– To store and catalog data for BI processing

Q4: What are the Purpose and Components of a Data Warehouse?

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Data warehouse architecture consists of the following layers:

• Operational database layer - The source data for the data warehouse - An organization's Enterprise Resource Planning systems fall into this layer.

• Data access layer - The interface between the operational and informational access layer - Tools to extract, transform, load (ETL) data into the warehouse fall into this layer.

• Metadata layer - The data directory - This is usually more detailed than an operational system data directory.

• Informational access layer - The reporting and analyzing tools. Business intelligence tools fall into this layer.

Q4: What are the Purpose and Components of a Data Warehouse?(cont.)

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Components of a Data Warehouse

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• Created to address particular needs– Business function– Problem– Opportunity

• Smaller than data warehouse• Data extracted from data warehouse for a functional

area

Q5: What Is a Data Mart, and How Does It Differ from a Data Warehouse?

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Components of a Data Mart

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• Data mining—application of statistical techniques to find patterns and relationships in body of data for purpose of classifying and predicting

Q6: What Are the Characteristics of Data-Mining Systems?

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• Analysts do not create model before running analysis

• Apply data-mining technique and observe results

• Hypotheses created after analysis as explanation for results

• Common statistical technique used:– Cluster analysis to identify groups with similar

characteristics

Unsupervised Data Mining

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• Model developed before analysis• Statistical techniques used to estimate

parameters• Examples:

– Regression analysis—measures impact of set of variables on one another

– Used for making predictions

Supervised Data Mining

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Neural networks• Used for predicting values and making

classifications• Complicated set of nonlinear equations

Supervised Data Mining (cont’d)

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• OnLine Analytical Processing– is an approach to quickly answer multi-

dimensional analytical queries– Dynamic online view based on

• Measures– Data item to be manipulated – total sales, average cost

• Dimensions– Characteristic of measure – purchase date, customer type,

location, sales region

Q7: What are OLAP reports?

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• 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

Q7: What are OLAP reports? (cont.)

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• Figure CE17-12

Role of OLAP Server and OLAP Database

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Q1: How big is an exabyte, and why does it matter?

Q2: How do business intelligence (BI) systems provide competitive advantages?

Q3: What problems do operational data pose for BI systems?

Q4: What are the purpose and components of a data warehouse?

Q5: What is a data mart, and how does it differ from a data warehouse?

Q6: What are the characteristics of data-mining systems?

Q7: What are OLAP reports?

Active Review

Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall 9-20

Chapter Extension 16

Database Marketing

Q1: What is a database marketing opportunity?

Q2: How does RFM analysis classify customers?

Q3: How does market-basket analysis identify cross-selling opportunities?

Study Questions

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• Database marketing– Application of business intelligence systems for

planning and executing marketing programs– Databases are a key component– Data-mining techniques also important

• Process of sorting through large amounts of data and picking out relevant information

Q1: What Is a Database Marketing Opportunity?

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RFM – RFM program analyzes and ranks customers according to their

purchase patterns– How recently (R) a customer has ordered?– How frequently (F) a customer has ordered?– How much money (M) a customer has spent per order?

Divides customers into five groups and assigns a score• R score 1 = top 20% in most recent orders• R score 5 = bottom 20% (longest since last order)

• F score 1 = top 20% in most frequent orders• F score 5 = bottom 20% least frequent orders

• M score 1 = top 20% in most money spent• M score 5 = bottom 20% in amount of money spent

Q2: How Does RFM Analysis Classify Customers?

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• Figure CE16-1

Example of RFM Score Data

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• Market-basket analysis is a 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– Helps identify cross-selling opportunities

• "Customers who bought book X also bought book Y”

Q3: How Does Market-Basket Analysis Identify Cross-Selling Opportunities?

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• Figure CE16-2

Market-Basket Example

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• SupportProbability that two items will be bought

together

– Fins and masks purchases together 150 times, thus support for fins and a mask is 150/1,000, or 15%

– Support for fins and weights is 60/1,000, or 6%

– Support for fins along with a second pair of fins is 10/1,000, or 1%

Market-Basket Terminology

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• ConfidenceWhat proportion of the customers who bought a

mask also bought fins? – Conditional probability estimate

– Example: » Probability of buying fins = 28% (280/1000)» Probability of buying swim mask = 27% (270/1000)

– After buying Mask, » Probability of buying Fins = 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

Market-Basket Terminology (cont’d)

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• LiftRatio 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

Market-Basket Terminology (cont’d)

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• Common business application– Classify loan applications by likelihood of default– Rules identify loans for bank approval– Identify market segment– Structure marketing campaign– Predict problems

• Too bad the Banks didn’t use decision trees!!

Decision Tree for Loan Evaluation

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Q1: What is a database marketing opportunity?

Q2: How does RFM analysis classify customers?

Q3: How does market-basket analysis identify cross-selling opportunities?

Active Review

CE16-32Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall

Chapter 9Ch Ext.16

Business Intelligence and Information Systems for

Decision Making