mgs4020 final exam review.ppt/jul 23, 2009/page 1 georgia state university - confidential mgs 4020...

MGS4020 Final Exam Review.ppt/Jul 23, 2009/Georgia State University - Confidential

MGS 4020

Business Intelligence

Final Exam Review

Jul 23, 2009


Executive Summary

Part 1 – Multiple Choices 60%• 30 Multiple Choices (2 points each)

Part 2 – Short Questions 40%• 8 Questions (5 points each)

• Executive Information Systems 10%• Data Warehouse 5%• Market Basket Analysis 10%• Direct Marketing 5%• Decision Tree 10%


Introduction - Why Business Intelligence


Business Intelligence

“Encompassing all aspects of collecting, deriving, analyzing, presenting and disseminating relevant business information to enable better business decisions and/or drive business processes"


Why Business Intelligence

1. Improve consistency and accuracy of reporting2. Reduce stress on operational systems for reporting and analysis3. Faster access to information4. BI tools provide increased analytical capabilities5. Empowering the Business User

6. Companies are realizing that data is a company’s most underutilized asset


Business Intelligence can include (but is not limited to):

Business IntelligenceData WarehousingKPIsReporting & AnalysisForecasting & BudgetingDashboardsInformation DeliveryModelingAnalytical ApplicationsPortals

Extract, Transformation & LoadCompetitor AnalysisIntegrationCustomer Intelligence / CRMCorporate Performance ManagementWorkforce AnalyticsOLAPBalanced Scorecard and more . . .


Ch 1 – Introduction to DSS


Obstacles to success in Business Intelligence

1. Data Source – Data Quality

2. Technology

3. Requirements Gathering

4. Justifying Cost; defining measurable ROI

5. Politics – Information Gatekeepers

6. Understanding the Decision Making Process

7. Knowledge Management


Data and Model Management

An increasing focus on the value of data to an organization pointed out that the quality and structure of the database largely determines the success of a DSS

A database organizes data into a logical hierarchy based on granularity of the data

The hierarchy contains four elements:

1. Database

2. Files or Tables

3. Records or Rows

4. Data elements or Columns


General Functions of the DBMS

– Data manipulation

– Data integrity

– Access control

– Concurrency control

– Transaction recovery


General Functions of the MBMS

• Modeling language – allows for creation of decision models, provides a mechanism for linking multiple models

• Model library – stores and manages all models, provides a catalog and description

• Model manipulation – allows management and manipulation of the model base with functions (run, store, query, etc.) similar to those in a DBMS


DSS Knowledge Base

• Any true decision requires reasoning, which requires information

• The knowledge base is where all of this information is stored by the DSS

• Knowledge can just be raw information, or rules, heuristics, constraints or previous outcomes

• This knowledge is different from information in either the database or model base in that it is problem-specific


Ch 2 – Decision & Decision MakersCh 4 – Modeling Decision Processes


Decision Tree

Buy Stock

Do Not Buy Stock

Price goes up

Price goes down

Gain

Loss

Loss/gain nothing


Decision Tree

Buy Stock

Leave money in savings

Return > 4 %

Return < 4 %

Reach Objective - 40%

Miss Objective - 60%

Return > 4 %

Return < 4 %

Reach Objective - 70%

Miss Objective - 30%


Decision Tree – Activation Test

SkyMiles Enrollment

Message A

Returned within xx days

Message B


Did not return within xx days

Message C


If Vc xx, send

Message D

Graduate to “SOW”


If Vc < xx, no more

messages


If Vc xx, send

Message D

If Vc < xx, no more

messages


Decision Tree - Activation Test

ChannelsEnrollmentMessage E/F

flied within xx days

Message G

flied within xx days

Did not fly within xx days

Message H


If Vc xx, send

Message J



If Vc < xx, no more

messages


If Vc xx, send

Message J

If Vc < xx, no more

messages


Decision Tree - Retention / SOW Test

HURDLE

SkyMiles w/ x flies last year, fly x+y

this yearMessages R*


Non-SkyMiles w/ x lx days

since last tripMessage P**


If Vc xx, send

Message Q

Next promotion

(responsive)


If Vc < xx, no more messages (non-responsive)

Next promotion

(responsive)

If Vc xx, send

Message S




Decision Tree - Reactivation Test

RATE OF trip

SkyMiles w/ xx days since last tripMessages L,M,N,O*


Non- SkyMiles w/ xx days

since last tripMessage P**


If Vc xx, send

Message Q

Next promotion

(responsive)



Next promotion

(responsive)

If Vc xx, send

Message Q




Probability

The Three Requirements of Probabilities:

1. All Probabilities must lie with the range of 0 to 1.

2. The sum of the individual probabilities equal to the probability of their union

3. The total probability of a complete set of outcomes must be equal to 1.


Decomposing Complex Probabilities

Severe Winter70%

Sales > 25,000 units

Sales <= 25,000 units

80%

20%

Sales > 25,000 units

Sales <= 25,000 units

50%

50%

Moderate Winter30%

Probability [ Sales > 25,000 units ] = ( .70 X .80 ) + ( .30 X .50 )= .56 + .15 .= .71 .


Data Mining / Market Basket Analysis


What is Data Mining?

• A set of activities used to find new, hidden, or unexpected patterns in data

• Verification versus Discovery

• Accuracy in predicting consumer behavior


OLAP – Online Analytical Processing

• MOLAP – Multidimensional OLAP

Data Warehouse/ Data Mart

RDBMS

• ROLAP – Relational OLAP


Techniques and Technologies

• Techniques Used to Mine the Data• Classification• Association• Sequence• Cluster

• Data Mining Technologies• Statistical Analysis• Neural Networks, Genetic Algorithms and Fuzzy Logic• Decision Trees


Market Basket Analysis

• Market Basket Analysis• Most common and useful in Marketing• What products customers purchase together

Diapers and Beer sell well on Thursday nights

• Benefits• Better target marketing• Product positioning with stores (virtual stores)• Inventory management

• Limitations• Large volume of real transactions needed• Difficult to correlate frequently purchased items with infrequently

purchased items• Results of previous transactions could have been affected by other

marketing promotions



Association Rules for Market Basket Analysis

• All associations are unidirectional and take on the following form: Left-hand side rule IMPLIES Right-hand side rule Left and Right hand side can both contain multiple items (Multi-

dimensional Market Analysis) Examples:

Steak IMPLIES Red Wine

Hunting Magazines IMPLIES Smokeless Tobacco



3 Measures of Market Basket Analysis

• Support – the percentage of baskets in the analysis where the rule is true• Of 100 baskets 11 contained both steaks and red wine.• 11% support

• Confidence – the percentage of Left-hand side items that also have right-side items• Of the 17 baskets that contained steak, 11 contained red wine.• 65% confidence

• Lift – compares the likelihood of finding the right-hand item in any random basket• Also referred to as Improvement• Lift of less than 1 means it is less predictive than random choice• If Confidence is 35%, but the right-hand side items is in 40% of the

baskets, the rule offers no Improvement of random selection.



Market Basket Analysis results can be:

• Trivial • Hot Dogs IMPLIES Hot Dog Buns• TV IMPLIES TV Warranty

• Inexplicable

Virtual Items – Associating non-items or other attributes into the correlation study

“New Customer”


Limitations of Data Mining

• All relevant data items / attributes may not be collected by the operational systems

• Data noise or missing values (data quality)

• Large database requirements and multi-dimensionality


Why use Analytics?

Some Benefits Are Quantifiable

• 15% to 51%+ increase in net sales

• ROI of over 2500%

• Annual increm revenue of > $178mm

• For one product over a 3 yr period, $650mm in cost savings & over $350mm in increm contribution

• >50% more accurate targeting of likely residential movers

• 24% reduction in churn rate from modeling/targeting likely churners

Other Benefits Not So Easily Quantified

• Decisions based on exhibited behaviors

• Makes data actionable

• Easier to measure results

• Validate instincts and opinions

• Enhanced what-if analysis & planning

• Less guesswork, more facts

• Built-in process improvement


Advanced analytics can help to answer the following questions …

• How do I determine which offers to make to my customers?

• What do my best customers look like, and where can I find more of them?

• What is the return on my marketing investment? How might my marketing plans be tweaked to optimize investment?

• Who are my most valuable customers? What are my key value drivers?

• Which of my customers have the greatest potential for growth – and which have little or no potential?

• Which of my customers are most vulnerable? What are the triggers causing them to leave or churn?

• Where should I employ my assets to meet customer demand?


Direct Marketing


Marketing Analytics Landscape

Where can I find new customers?

Where can I find more revenue & profit from my

current customers?

Which of my customers are at risk and how

can I keep them?

Which customers do I

want to win back?

Strategy & Tactics: Guiding the business & helping to make numbersBusiness Planning, Forecasting, Corp Strategy, Financial Metrics, Profitability Analysis

Customer Knowledge – Who are my customers?Segmentation & Profiles, External Data, Mkt Share/Wallet Share, Channel Preference Modeling

• Customer Acquisition

• Prospect profiling

• Event driven marketing

• Propensity to buy & response modeling

• Marketing Optimization

• Market Basket Analysis

• Online and Retail Channels

• Customer and product churn modeling

• Retentive stickiness of key products

• Prediction of key events (eg, residential movers)

• Customer reacquisition

• Customer profitability analysis

Acquisition Growth ReacquisitionRetention


Direct Marketing Campaign Platform

ACQUIRE

RETAIN

REACTIVATE

“FIRE”

STORE DIFFERENT CHANNELS

A C T I V A T I O N P R O M O T I O NA C T I V A T I O N P R O M O T I O N

E-mail Address

Vehicles:

• Statements

• Newsletters

• Inserts

• Direct mail

• Personalized kits

• E-mail

• Telephone

Vc Cost to reactivateIf:

Vc < Cost to reactivateIf:

Ugly Postcard???

TestArea

• POS

• Partners

• Advertising

Vehicles:

• Direct Mail

• E-mail

• Statements

Triggered Promotions

highest value

customers

lowest value

customersdowngrade

trigger *

(for example)Days since last purchase = X

X = 30 days for PTNM

X = 60 days for GOLD

X = 120 days for CLUB

Direct Marketing Campaign Platform

PURCHASED

NO PURCHASE

PURCHASE

* < 1 purchase in last 12 mo

If : Time since inactive = X, and

Point balance > X


General Data Mining Methods

• Predicting which customers will purchase, based on demographics, psychographics, firmographics, service history, transactions, credit history, etc. Statistical algorithms and decision trees are used for these problems with much success.

• Market Basket Analysis: which customers who purchase an additional telephone line are also likely to purchase dialup internet service? Pattern matching works well: associative rules, fuzzy logic, neural networks.

• Which types of activities precede each other; eg, do customer hospitality and gaming activities show patterns or sequences? We use a combination of statistical modeling and simulations to identify these trigger points for action, and to estimate the marginal value of each.

• Clustering is useful for determining similar groups based on how closely they resemble each other. Multitude of clustering techniques exist, with the primary difference being in how they define what is “close”. Clustering can be very useful for marketing messaging and advertising, strategy development and implementation, and channel development.

Classification:

Association:

Sequencing:

Clustering:


Analytics Process

DISCOVERY DATA PREPARATION

KNOWLEDGE DEVELOPMENT

LEVERAGING ANALYTICS

POST ANALYSIS

OPPORTUNITIES

IDENTIFYING

SCOPING

OBJECTIVE SETTING

DATA WAREHOUSE

EXTERNAL DATA APPEND

DATA EXTRACTION

DATA VALIDATION

STATISTICAL MODELING

SEGMENTATION

OFFER OPTIMIZATION

CUSTOMER BEHAVIOR SCORING

DIRECT MAIL

TELEMARKETING

EMAIL

LOYALTY CAMPAIGN

RESULTS DECOMPOSITION

REFININGANALYTICS

FEEDBACK

HYPOTHESISTESTING

DEVELOPINGHYPOTHESES

EFFORT

FEEDBACK FOR


Summary

• Analytics allow quantifiable, intelligent decision making

• Analytics can be leveraged across all areas of a business

• Different analytical methods apply to different situations

• Modeling enables you to combine potential hundreds of factors into a single decision metric (or a few key scores/clusters)

• Analytics are more powerful when tied to bottom line profitability


Ch 6 - Executive Information System


What is an Executive Information System

• A special type of DSS that support Senior Management

• Provides a “Big Picture” view of the business

• Analysis of overall operations

• Covers a broad range of business areas

• Supports strategic decision-making

• Current picture of operations and performance

• Internal & External Views

• Highlights exceptions and allows for further analysis


Executive Information System Interface

• Must be easy and intuitive to use

• Likely to include KPIs – Key Performance Indicators

• Graphs and Trends

• Single screen summary

• Exception Highlighting (arrows, colors, etc.)

• Drill-Down capabilities“. . .allows for further structured investigation.”


Sources of Executive Information System

• Cost Accounting Systems (Revenue and Expenses)

• External Information (markets, customers, suppliers, competitors)

• Spread across organizations and systems

• Objective and Subjective assessments

• Current results and short-term performance levels

• Highly volatile information


Common Features of an Executive Information System

• Status access, drill down, exception reporting, trend analysis and ad hoc queries/reports

• Widespread access to external databases and information repositories

• Multidimensional data mining and visualization

• Multilevel access control security

• Usage monitoring


Sample Executive Information System


Ch 10 – The Data Warehouse


Data Flow

OperationalData Store

DataWarehouse

DataMart

Metadata

LegacySystems

PersonalData

Warehouse


The Data Warehouse

The Data Warehouse

• is physically separated from all other operational systems

• holds aggregated data and transactional data for management separate from that data used for online transaction processing


Data Warehouse Vendors

• Business Objects

• Cognos

• Hyperion

• IBM

• Microsoft

• NCR / Teradata

• Oracle

• SAS


Relational Database

A relational database is a collection of data items organized as a set of formally-described tables from which data can be accessed or reassembled in many different ways without having to reorganize the database tables. The relational database was invented by E. F. Codd at IBM in 1970.

The standard user and application program interface to a relational database is the structured query language (SQL). SQL statements are used both for interactive queries for information from a relational database and for gathering data for reports.

A relational database is a set of tables containing data fitted into predefined categories. Each table (which is sometimes called a relation) contains one or more data categories in columns. Each row contains a unique instance of data for the categories defined by the columns. For example, a typical business order entry database would include a table that described a customer with columns for name, address, phone number, and so forth. Another table would describe an order: product, customer, date, sales price, and so forth. A user of the database could obtain a view of the database that fitted the user's needs. For example, a branch office manager might like a view or report on all customers that had bought products after a certain date. A financial services manager in the same company could, from the same tables, obtain a report on accounts that needed to be paid.


Relational Database

When creating a relational database, you can define the domain of possible values in a data column and further constraints that may apply to that data value. For example, a domain of possible customers could allow up to ten possible customer names but be constrained in one table to allowing only three of these customer names to be specifiable.

The definition of a relational database results in a table of metadata or formal descriptions of the tables, columns, domains, and constraints. Meta is a prefix that in most information technology usages means "an underlying definition or description." Thus, metadata is a definition or description of data and metalanguage is a definition or description of language.

A database is a collection of data that is organized so that its contents can easily be accessed, managed, and updated. The most prevalent type of database is the relational database, a tabular database in which data is defined so that it can be reorganized and accessed in a number of different ways. A distributed database is one that can be dispersed or replicated among different points in a network. An object-oriented programming database is one that is congruent with the data defined in object classes and subclasses.

SQL (Structured Query Language) is a standard interactive and programming language for getting information from and updating a database. Although SQL is both an ANSI and an ISO standard, many database products support SQL with proprietary extensions to the standard language. Queries take the form of a command language that lets you select, insert, update, find out the location of data, and so forth.


Relational Database

• IBM DB2, DB2/400 • Microsoft SQL/Server • Teradata • Oracle • Sybase • Informix / Red Brick

• Microsoft Access• MySQL


SQL

SQL – Structured Query Language

1. DDL – Data Definition Language

• Create• Drop • Alter

2. DML – Data Manipulation Language

• Insert• Update• Delete• Select


Why Business Intelligence

1. Improve consistency and accuracy of reporting

2. Reduce stress on operational systems for reporting and analysis

3. Faster access to information

4. BI tools provide increased analytical capabilities

5. Empowering the Business User

6. Companies are realizing that data is a company’s most underutilized asset


Retail Sales Dimensional Model (Partial)

Sales Fact Table

Time_Key (FK)Product_Key (FK)Store_Key (FK)Customer_Key(FK)UnitsRevenueCost. . .

Product Dimension Table

Product_Key (PK)SKU_NumberDescriptionBrandProduct_CategorySize. . . .Etc.

Customer Dimension Table

Customer_Key (PK)Customer_NamePurchase_ProfileCredit_ProfileDemographic_CategoryAddress. . . .Etc.

Time Dimension Table

Time_Key (PK)DateDay_of_WeekWeek_NumberMonth. . . .Etc.

Store Dimension Table

Store_Key (PK)Store_IDStore_NameAddressDistrictFloor_Plan. . . .Etc.


Fact Table

1. Contains Foreign Keys that relate to Dimension Tables

2. Have a many-to-one relationship to Dimension Tables

3. Contains Metrics to be aggregated

4. Typically does not contain any non-foreign key or non-metric data elements

5. Level of Granularity defines depth and flexibility of analysis

Sales Fact Table

Time_Key (FK)Product_Key (FK)Store_Key (FK)Customer_Key(FK)UnitsRevenueCost. . .


Dimension Table

1. Contains a Primary Key that relates to the Fact Table(s)

2. Has a one-to-many relationship to the Fact Table(s)

3. Contains Descriptive data used to limit and aggregated metrics from the Fact Table(s)

4. Can sometimes contain pre-aggregated data

Product Dimension Table

Product_Key (PK)SKU_NumberDescriptionBrandProduct_CategorySize. . . .Etc.


Business Objects


Business Objects – What Exactly is a Universe?

• BUSINESS OBJECTS universes make it easy to access data, because they contain objects of data in business terms that are familiar to you. What’s more, you need no knowledge of the database structure, or of database technology, to be able to create powerful reports with data that is relevant to your work.

• Universes provide the business-intelligent, semantic layer that isolates you from the technical issues of the database. A universe maps to data in the database, in everyday terms that describe your business situation.

• Universes are made up of classes and objects. For example, the objects in a human resources universe would be Names, Addresses, Salaries, etc. Classes are logical groupings of objects. Each class has a meaningful name, such as Vacation (for objects pertaining to employees’ vacations). Each object maps to data in the database, and enables you to retrieve data for your reports.


Business Objects – Classes & Sub-classes


Business Objects – Dimension objects, measure objects and detail objects

• Dimension objects retrieve the data that will provide the basis for analysis in a report. Dimension objects typically retrieve character-type data (customer names, resort names, etc.), or dates (years, quarters, reservation dates, etc.)

• A detail object is always associated to one dimension object, on which it provides additional information. For example, Address is a detail object that is associated to Customer. Address provides additional information on customers, i.e., their addresses.

• Measure objects retrieve numeric data that is the result of calculations on data in the database. In the demo universe, Revenue is the calculation of number of items sold multiplied by item price. Measure objects are usually located in the Measures class.


Applying a complex condition on a query

Applying a complex condition requires three steps. First, you select the object you want, then the operator (e.g., greater than), then the operand (e.g., values that you type, or another object). The following procedure explains how to do it, and gives information to help you choose the operator and operand you need:

1. In the Query Panel, drag the object you want to use from the Classes and Objects list to the Conditions box. The Classes and Objects list turns into the Operators list:



2. Double-click the operator you want to use. The Operators list turns into the Operands list:



3. Double-click the operand you want. The following table helps you select the operand you need and tells you what to do next:

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