James Nowotarski 24 April 2008 IS 425 Enterprise Information Spring 2008

James Nowotarski

24 April 2008

IS 425Enterprise Information

Spring 2008

2

Topic Duration

Recap of 4/17 20 minutes

IS Competency Analysis 20 minutes

Data warehouse 40 minutes

*** Break 15 minutes

Data mining 40 minutes

Analytics 30 minutes

Current events 20 minutes

Wrap-up

Today’s Agenda

3

Gartner 2008 CIO survey

2008 Business Expectations

To what extent will each of the following be a top priority for you in 2008?

* New question for 2008 ** New question for 2007

Improving business processes 1 1 1

Attracting and retaining new customers 2 3 3

Creating new products or services (innovation) 3 10 9

Expanding into new markets or geographies 4 9 **

Reducing enterprise costs 5 2 2

Improving enterprise workforce effectiveness. 6 4 **

Expanding current customer relationships 7 * *

Increasing the use of information/analytics 8 7 6

Targeting customers and markets more effectively 9 * *

Acquiring new companies and capabilities (M&A, etc) 10 * *

2008 2007 2006

2008 Business Expectations

To what extent will each of the following be a top priority for you in 2008?


Improving business processes 1 1 1

Attracting and retaining new customers 2 3 3

Creating new products or services (innovation) 3 10 9

Expanding into new markets or geographies 4 9 **

Reducing enterprise costs 5 2 2

Improving enterprise workforce effectiveness. 6 4 **

Expanding current customer relationships 7 * *

Increasing the use of information/analytics 8 7 6

Targeting customers and markets more effectively 9 * *

Acquiring new companies and capabilities (M&A, etc) 10 * *

2008 2007 2006

4

Gartner 2008 CIO survey2008 CIO Technology Priorities

To what extent will each of the following technologies be a top five priority for you in 2008?


2008 2007 2006

Business intelligence 1 1 1 11.20%

Enterprise applications (ERP, SCM, CRM, etc) 2 2 ** 8.02%

Servers & storage technologies 3 5 9 8.45%

Legacy modernization, upgrade or replacement 4 3 10 5.79%

Security Technologies 5 6 2 8.53%

Technical Infrastructure 6 8 12 4.67%

Networking, Voice and Data 7 4 8 6.83%

Collaboration technologies 8 10 4 7.75%

Document management 9 9 ** 7.91%

Service oriented (SOA, SOBA) 10 7 6 6.71%

2008Unweighted Average

Budget Change

2008 CIO Technology Priorities

To what extent will each of the following technologies be a top five priority for you in 2008?


2008 2007 2006

Business intelligence 1 1 1 11.20%

Enterprise applications (ERP, SCM, CRM, etc) 2 2 ** 8.02%

Servers & storage technologies 3 5 9 8.45%

Legacy modernization, upgrade or replacement 4 3 10 5.79%

Security Technologies 5 6 2 8.53%

Technical Infrastructure 6 8 12 4.67%

Networking, Voice and Data 7 4 8 6.83%

Collaboration technologies 8 10 4 7.75%

Document management 9 9 ** 7.91%

Service oriented (SOA, SOBA) 10 7 6 6.71%

2008Unweighted Average

Budget Change

5

Porter’s Value Chain Model

Figure 3.6: Porter's value chain model for a manufacturing firm. (Source: Reprinted with permission of the Free Press, a Division of Simon & Schuster Inc. from Competitive Advantage: Creating and Sustaining Superior Performance. Copyright © 1985 by Michael Porter.)

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e-Business Application Architecture

Supply Chain Mgmt

Selling Chain Mgmt

Sta

keh

old

ers

Business Partners,Suppliers, Resellers

Distributors,

Customers, Resellers

Em

plo

yees

HR

MS/

E-P

rocu

rem

en

t

Fin

ance

Auditin

gM

gm

t Contro

l

BI EAI

CRM

ERP

Logistics

Pro

ductio

n

Distrib

utio

n

Mark

etin

g

Sale

s

Cust S

vce

ERP

Information Systems IS 425

DePaul University

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What is ERP?

Anatomy of an Enterprise System

Source: Davenport, T. (1998). Putting the enterprise into the enterprise system. Harvard Business Review, (July/August), 131


DePaul University

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ENTERPRISE SYSTEMS

Enterprise System Architecture

Anatomy of an Enterprise System

Source: Adam & Sammon


DePaul University

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ERP Supported Functions

Financial Hum Res Ops & Log Sales & Mktg

Accts receivable Time accounting Inventory Orders

Asset account Payroll MRP Pricing

Cash forecast Personnel plan Plant Mtce Sales Mgt

Cost accounting Travel expense Prod planning Sales plan

Exec Info Sys Project Mgmt

Financial consol Purchasing

General ledger Quality Mgmt

Profit analysis Shipping

Standard costing Vendor eval

Classification of IT portfolio

Operations Decisions Strategies

Finance

Accounting

Marketing

Human resources

Etc.

IBM (Cognos)

Information Builders

BI Platforms

SAS

JD Edwards

Peoplesoft

SAP

Oracle

Microsoft

EnterpriseSystems

Custom

Enabling Process Agility

Managing the End-to-End

Process Cycle

Enabling Information

Workers

The Enterprise Need Through 2010: Balance Information, Processes and People

Incorporating Information

Application Portfolio

Management

CRM Changes, 2007 to 2010Technology

• SaaS to move to become 25% of all CRM, hottest in sales force automation, Web analytics, e-commerce and small call centers

• CRM BPM and CRM BPP rise in takeup, displacing custom-build and CRM suites

• Customer data integration and multichannel integration, increasing focus for investment as CRM moves outside individual business units

Market

• Growing at 11%, strongest market position since 2000, skills shortages

• SAP and Oracle = 50%, Salesforce.com, Microsoft growing fastest

• High levels of M&A, but also high levels of startups

Functional

• Build-your-own drops from 70% of all CRM implementations to <50%

• SFA, call centers, campaign management remain 65% of package projects

• Hot markets in community marketing, sales pricing management, analytics for Web, sales and call center, collaborative intelligence

ERP Changes, 2007 to 2010

Technology

• SOA has an impact on ERP implementations

Market

• Vendor consolidation — bifurcation to big vendors and little vendors

• Slowing of user upgrades with SOA benefit uncertainty

• Instance consolidation remains big (retiring of legacy products)

Functional

• Return of shared services

• Focus on reconnecting end-to-end processes with integration technologies and acquisitions of big suites

• Functionality delivered through components rather than big changes to core of applications

The ability to adapt business application portfolios to share information and create and source new business processes quickly and at a low cost will be a critical source of competitive advantage.

Enabling business users to augment current business application environments through composition of new business processes and/or alternate sourcing models will enable users to quickly and efficiently adapt to changes in business models.

Focus 1: Business Model and Process Agility

The Technology Path Through 2010

To:

Enabling Business and

Person-to-Process

Innovation/Agility

From: Managing the End-to-End

Process Cycle

Disruptive Impact: The Process of "Me"

Process of Me — Process needs to be redefined: Business + People Processes

Instant Messaging, Alerts, Threaded Discussion (Collaboration and Personal Productivity)

Processes embrace the chaos of business and

people within the business.

SOABPM

Middleware

BI/BAM

EDA

Collaboration

Personal Productivity

EIM

Differentiating Applications

Infrastructure

CommoditizedApplications

Middleware

Commoditized Business Processes Differentiating Bus. Processes

Adaptable TechnologyAdaptable IT Processes

Segregate Your Processes: Commoditized vs. Differentiating

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Topic Duration








Wrap-up

Today’s Agenda

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CSC 212Programming

in Java II

ECT 425Technical

Fundamentals Of Distributed Info Systems

CSC 451Database

Design

SE 430Object-Oriented

Modeling

IS 425Enterprise Information

IT 215Analysis &

Design Techniques

ECT 310Internet

Application Development

CSC 211Programming

In Java I

ApplicationDevelopmentDatabase I

E-BusinessSystems

Data Mining& Analytics

Network Design

CapstoneIS 577

Level I

Level III

Level II

Foundation Phase

Prerequisite Phase

HCI MethodsInternet

ApplicationDevelopment

Database IIInformation

Assurance &Security

EnterpriseSystems

Integration

IT Project Management

I

Wireless &Mobile

Applications

Knowledge Management

IT Planning& Strategies

GlobalSystems

& Strategies

Competency Modules for MSIS

Legal & Social

Issues

Advanced Internet Tech.

IT Architecture

Design

SoftwareEngineering

IT Project Management

II

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IT OutsourcingBest jobs in America

1. Software engineer

2. College professor

3. Financial adviser

4. Human resources manager

5. Physician’s assistant

6. Market research analyst

7. Computer/IT analyst

8. Real estate appraiser

9. Pharmacist

10. Psychologist

Source:Kalwarski, T., Mosher, D., Paskin, J. & Rosato, D. (2006, May) 50 best jobs in America. Money. Retrieved September 8, 2006, from http://money.cnn.com/magazines/moneymag/bestjobs/

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Topic Duration








Wrap-up

Today’s Agenda

Data Warehouse Architecture

Client Client

Warehouse

Source Source Source

Query & Analysis

Integration

Metadata

Staging Area

MetadataRepository

Data Marts

Data Warehouse

Optimize for Production:• Excellent response time• Workflow-driven• Vendor application development/support

Optimize for Reporting/Analysis:• Data quality/accuracy• Single version of truth across all

systems• Rapid retrieval of high volume

Sources

Basic DW: The Repositories

OperationalData Store

AnalysisQueryReportsData mining

The Schumacher Group, April 2008The Schumacher Group, April 2008

Multi-Tiered ArchitectureMulti-Tiered Architecture

DataWarehouse

ExtractTransformLoadRefresh

OLAP Engine

AnalysisQueryReportsData mining

Monitor&

IntegratorMetadata

Data Sources Front-End Tools

Serve

Data Marts

Operational DBs

othersources

Data Storage

OLAP Server

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Lightly summarized

28

Simple cumulative

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Simple cumulative

Data Model For OLTP

• Data stored by operational systems, such as point-of-sales, are in types of databases called OLTPs.

• OLTP, Online Transaction Process, databases do not have any difference from a structural perspective from any other databases.

• The main difference, and only difference is the way in which data is stored.

Data Model for OLTP

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Simple cumulative

Data Model for Data Warehouse

Warehouse design: Multi-dimensional Data Base (MDDB)

• Multi-Dimensional Database– Dimensions used to index array.

Here Date, Product, and Store are the dimensions of the MDDB

– “Facts” stored in array cells. Here the Sales for each store of each product and for each month will be computed and stored in each cell of the MDDB

Pro

du

ct

Store

Date

J F M A

milk

soda

eggs

soap

AB

Sales

Multidimensional Data

• Sales volume as a function of product, month, and region

Month

Industry Region Year

Category Country Quarter

Product City Month Week

Office Day

Prod

uct

Region

Dimensions: Product, Location, TimeAnd Hierarchical summarization paths

A Sample Data Cube

Date

Product

Coun

trysum

sum TV

VCRPC

1Qtr 2Qtr 3Qtr 4Qtr

U.S.A

Canada

Mexico

sum

Total annual salesof TV in U.S.A.

Online Analytical Processing (OLAP)

• Slice and Dice ... – Select dimensions– Choose measures– Filter by dimensions

• Drill Down ...– Drill down hierarchies– Drill through to details

• Present the Results– Present as spreadsheet– Display graphically

STAR Schema for an OLAP

• OLAPs have a different mandate from OLTPs.

– OLAPs are designed to give an overview analysis of what happened. Hence the data storage (i.e. data modeling) has to be set up differently.

– The most common method used for OLAP design is called the star design.

• It is not always necessary to create a data warehouse for OLAP analysis.

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Topic Duration








Wrap-up

Today’s Agenda

Information Systems IS 425 Class Four

DePaul University

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Terminology - A Working Definition

Data Mining is a “decision support” process in which we search for patterns of information in data.

A pattern is a conservative statement about a probability distribution. – Webster: A pattern is (a) a natural or chance configuration,

(b) a reliable sample of traits, acts, tendencies, or other observable characteristics of a person, group, or institution

What is data mining• Data mining is the process by which analysts apply

technology to historical data (mining) to determine statistically reliable relationships between variables.

• Generally, it is the procedure by which analysts utilize the tools of mathematics and statistical testing applied to business-relevant, historical data in order to identify relationships, patterns, or affiliations among variables or sections of variables in that data to gain greater insight into the underpinnings of the business process (Kudyba & Hoptrof)


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Why Do We Need Data Mining ?

Leverage organization’s data assets– Only a small portion (typically - 5%-10%) of the collected

data is ever analyzed

– Data that may never be analyzed continues to be collected, at a great expense, out of fear that something which may prove important in the future is missing.

– Growth rates of data precludes traditional “manually intensive” approach


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Why Do We Need Data Mining?

As databases grow, the ability to support the decision support process using traditional query languages becomes infeasible

– Many queries of interest are difficult to state in a query language (Query formulation problem)

– “find all cases of fraud”

– “find all individuals likely to buy a FORD expedition”

– “find all documents that are similar to this customers problem”

QUERY

RESULT

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The Law of Accelerating Returns is driving economic growth

• The portion of a product or service’s value comprised of information is asymptoting to 100%

• The cost of information at every level incurs deflation at ~ 50% per year

• This is a powerful deflationary force– Completely different from the deflation in the 1929

Depression (collapse of consumer confidence & money supply)

Source: Ray Kurzweil, futurist & inventor


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Why Data Mining

Credit ratings/targeted marketing:

– Given a database of 100,000 names, which persons are the least likely to default on their credit cards?

– Identify likely responders to sales promotions

Fraud detection

– Which types of transactions are likely to be fraudulent, given the demographics and transactional history of a particular customer?

Customer relationship management:

– Which of my customers are likely to be the most loyal, and which are most likely to leave for a competitor? :

Data Mining helps extract such information


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Fraud/Non-Compliance Anomaly detection

– Isolate the factors that lead to fraud, waste and abuse

– Target auditing and investigative efforts more effectively

Credit/Risk Scoring Intrusion detection Parts failure prediction

Recruiting/Attracting customers

Maximizing profitability (cross selling, identifying profitable customers)

Service Delivery and Customer Retention

– Build profiles of customers likely to use which services

Web Mining

Examples of What People are Doing with Data Mining:


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Where does the data come from?

– Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies

Target marketing

– Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.

– Determine customer purchasing patterns over time Cross-market analysis

– Associations/co-relations between product sales, & prediction based on such association

Customer profiling

– What types of customers buy what products (clustering or classification)

Customer requirement analysis

– Identifying the best products for different customers

– Predict what factors will attract new customers



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Finance planning and asset evaluation

– cash flow analysis and prediction

– contingent claim analysis to evaluate assets

– cross-sectional and time series analysis (financial-ratio, trend analysis, etc.)

Resource planning

– summarize and compare the resources and spending Competition

– monitor competitors and market directions

– group customers into classes and a class-based pricing procedure

– set pricing strategy in a highly competitive market



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Why Now?

• Data is being produced

• Data is being warehoused

• The computing power is available

• The computing power is affordable

• The competitive pressures are strong

• Commercial products are available


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Database Processing vs. Data Mining Processing

Query– Well defined– SQL

Query

– Poorly defined

– No precise query language

DataData

– Operational dataOperational data

OutputOutput

– PrecisePrecise

– Subset of databaseSubset of database

DataData

– Not operational dataNot operational data

– Usually summarizedUsually summarized

OutputOutput

– FuzzyFuzzy

– Not a subset of databaseNot a subset of database


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Query Examples

Database

Data Mining– Find all customers who have purchased beerFind all customers who have purchased beer

– Find all items which are frequently purchased with beer. Find all items which are frequently purchased with beer. (association rules)(association rules)– Describe attributes of customers likely to spend the most Describe attributes of customers likely to spend the most (segmentation)(segmentation)

– Find all credit applicants with last name of Smith.Find all credit applicants with last name of Smith.– Identify customers who have purchased more than Identify customers who have purchased more than $10,000 in the last month$10,000 in the last month..

– Find all credit applicants who are poor credit risks. Find all credit applicants who are poor credit risks. (classification)(classification)

– Identify customers with similar buying habits. (clustering)Identify customers with similar buying habits. (clustering)

Data Mining Models and Tasks

Association Rules• There has been a considerable amount of research in the area of Market

Basket Analysis. Its appeal comes from the clarity and utility of its results, which are expressed in the form association rules.

• Given– A database of transactions– Each transaction contains a set of items

• Find all rules X->Y that correlate the presence of one set of items X with another set of items Y– Example: When a customer buys bread and butter, they buy milk 85% of

the time

+

Example: Association analysis

…. . ….…..

all 100 orders

orders with pretzels orders with beer

Market Basket Example

Is soda typically purchased with bananas?Does the brand of soda make a difference?

Where should detergents be placed in theStore to maximize their sales?

Are window cleaning products purchased when detergents and orange juice are bought together?

How are the demographics of the neighborhood affecting what customers are buying?

?

?

?

?

Example: Segmentation (Target variable: Spending level)

All Customers10.6

Male20.2

Female12.7

Age: 0-255.8

Age: 25-5515.6

Age: > 558.5

North9.6

South6.5

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Topic Duration








Wrap-up

Today’s Agenda

What is analytics

The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions (Davenport)

Much of the attention focuses on “advanced” analytics, of which predictive analytics is a subset

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Data Mining Models and Tasks

Examples of analytics applications What products their customers want What prices those customers will pay How many items each will buy in a lifetime What triggers will make people buy more Predict problems with demand and supply

chains, to achieve low rates of inventory and high rates of perfect orders.

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Example: Marriott - Factor Analysis Identifies What Is Important

Importance of Attributes in Predicting Propensity for Guest Return:

Months Since Deep Clean

Age of Bed

Use of Fitness Center

Spending in Restaurant

Room Price

Speed of Check-In

Speed of Room Service

Premium Movie Channel

Please Rate the Importance of the Following Aspects of Your Stay:

Low High

Room Cleanliness

Comfort of Bed

Fitness Center

Restaurant

Room Prices

Check-In Experience

Room Service

TV Channels

1: Monitoring

2: Framework

3: Predictive

Example: Marriott’s revenue opportunity model

Computes actual revenues as a percentage of the optimal rates that could have been charged

That figure has grown from 83% to 91% as Marriott’s revenue-management analytics have taken root throughout the enterprise

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7 common targets for analytical activity

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Long, arduous journey

The UK Consumer Cards and Loans business within Barclays Bank, for example, spent five years executing its plan to apply analytics to the marketing of credit cards and other financial products.

The company had to make process changes in virtually every aspect of its consumer business: underwriting risk, setting credit limits, servicing accounts, controlling fraud, cross selling, and so on.

On the technical side, it had to integrate data on 10 million Barclaycard customers, improve the quality of the data, and build systems to step up data collection and analysis.

And it had to hire new people with top-drawer quantitative skills.

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The Schumacher Group, April 2008

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Trends in data mining and advanced analytics projects Need to be driven much more by the business units

The most significant challenges driving changes in data

mining market are scalability and performance Terabyte-class databases have become more common today

The growth of ecommerce has also driven the need for data-mining approaches that work with online Web businesses

More focus on text mining with almost 80% of data nowadays in unstructured textual format.”

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65

Readings on Systems Development DL: Discussion on data mining/analytics

For May 1

66

Extra slides

Underlying Technology: Hip and Hype

Technology Trigger

Peak ofInflated

Expectations

Trough of Disillusionment Slope of Enlightenment Plateau of

Productivity

time

visibility

Years to mainstream adoption:

less than 2 years 2 to 5 years 5 to 10 years more than 10 yearsobsoletebefore plateau

As of June 2007

XML-Enabled Database Management Systems

Linux as a Mission-Critical DBMS Platform

OSS DBMS for Non-Mission-Critical Applications

Real-Time Data Integration

Data Warehouse Appliances

Data Federation/EII

OSS DBMS for Mission-Critical Applications

Data Profiling

Comprehensive Data Integration Tool Suites

XQuery

Master Data Management

Data Service Architectures

Enterprise Information Management

SaaS Data Integration and Data Quality

Information-Centric Infrastructure

Open-Source Data Integration Tools

Entity Resolution and Analysis

Data Quality DashboardsMetadata Ontology

Management

Content Integration

Data Quality Tools

From "Hype Cycle for Data Management, 2007," 2 July 2007

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Firmwide IT

Infrastructure Business Value

Business-Unit IT

Applications Business Value

Business-Unit Operational

Business Value

Business-Unit Financial

Business Value

Time

Impact

Sought

Dilutionof Impact

•Revenue growth•Return on assets•Revenue per employee

•Time to bring a new product to market•Sales from new products•Product or service quality

•Time to implement a new application•Cost to implement a new application

•Infrastructure availability•Cost per transaction•Cost per workstation

Business Value Measures

Dilu

tion

of IT

Impa

cts

Dilutionof Impact

Dilutionof Impact

InformationTechnology $

InformationTechnology $ A

C

B

Source: “Leveraging the New Infrastructure”, Peter Weill & Marianne Broadbent, ©1998

Hierarchy of Impact of Information Technology Investments

69

Increased control

Better information

Better integration

Improved quality

•Shorter time to market•Premium pricing•Superior quality

Increased sales

Competitive advantage

Competitive necessity

Market positioning

Innovative services

•50% fail•Some spectacular successes•2-to-3 year lead•Premium pricing•Higher revenue per employee

Cut costs

Increased

throughput

•25-40% return•Higher ROA•Low risk

Business integration

Business flexibility

Reduced marginal

cost of business

unit’s IT

Reduced IT costs

Standardization

More Higher growth Less Higher ROA

Infrastructure

Transactional

Informational Strategic

Source: “Leveraging the New Infrastructure”, Peter Weill & Marianne Broadbent, ©1998

Information Technology Portfolio and Business Value

70

IT Portfolio

Composition Will Be a New Source for Business Application Delivery

Buy Build ComposeEAS User Interface

BPM

Data IntegrationE

SB

/EA

I

User Experience

Business Process

Information Services

Business Application

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Holistic view

Technology

ProcessPeople

Documents

James Nowotarski 24 April 2008 IS 425 Enterprise Information Spring 2008