Transcript IS 425 Enterprise Information Spring 2008

IS 425
Enterprise Information
Spring 2008
James Nowotarski
24 April 2008
Today’s Agenda
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
2
Gartner 2008 CIO survey
2008 Business Expectations
To what extent will each of the following be a top priority for
you in 2008?
2008
2007
2006
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
*
*
10
*
*
Acquiring new companies and capabilities (M&A, etc)
* New question for 2008 ** New question for 2007
3
Gartner 2008 CIO survey
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
2008
Unweighted Average
Budget Change
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%
10
7
6
6.71%
Service oriented (SOA, SOBA)
* New question for 2008 ** New question for 2007
4
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.)
5
e-Business Application Architecture
Business Partners,
Suppliers, Resellers
Distributors,
Supply Chain Mgmt
BI
EAI
Cust Svce
Sales
Marketing
CRM
Finance
Auditing
Mgmt Control
ERP
Stakeholders
Distribution
Production
Logistics
HRMS/
E-Procurement
Employees
ERP
Selling Chain Mgmt
Customers,
Resellers
6
Information Systems IS 425
What is ERP?
DePaul University
7
Anatomy of an Enterprise
System
Source: Davenport, T. (1998). Putting the enterprise into the enterprise system.
Harvard Business Review, (July/August), 131
Information Systems IS 425
ENTERPRISE SYSTEMS
Enterprise System Architecture
DePaul University
9
Anatomy of an Enterprise
System
Source: Adam & Sammon
Information Systems IS 425
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
DePaul University
11
Classification of IT portfolio
Operations
Decisions
SAP
Finance
Accounting
Marketing
Human
resources
Etc.
Peoplesoft
Enterprise
Systems
Strategies
Custom
Oracle
IBM (Cognos)
SAS
Microsoft
JD Edwards
BI Platforms
Information Builders
The Enterprise Need Through 2010:
Balance Information, Processes and People
Enabling Process
Agility
Managing the
End-to-End
Process Cycle
Enabling
Information
Workers
Incorporating
Information
Application
Portfolio
Management
CRM Changes, 2007 to 2010
Technology
•
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
Focus 1: Business Model and Process
Agility
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.
The Technology Path Through 2010
From:
Managing the
End-to-End
Process Cycle
SOA
To:
Middleware Collaboration
Personal
Enabling Business and
BPM
Productivity
Person-to-Process
BI/BAM
Innovation/Agility
EIM
EDA
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.
Segregate Your Processes:
Commoditized vs. Differentiating
Adaptable Technology
Adaptable IT Processes
Commoditized Business Processes
Commoditized
Applications
Middleware
Infrastructure
Differentiating Bus.
Processes
Differentiating
Applications
Today’s Agenda
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
19
Competency Modules for MSIS
Level III
IT Project
Management
IT Planning
& Strategies
II
IT
Architecture
Design
Global
Systems
& Strategies
Legal
& Social
Issues
Capstone
IS 577
Level II
Database II
Information
Assurance &
Security
Enterprise
Systems
Integration
IT Project
Management
I
Wireless &
Mobile
Applications
Knowledge
Management
Advanced
Internet
Tech.
Level I
Database I
Application
Development
Data Mining
& Analytics
CSC 451
Database
Design
Network
Design
E-Business
Systems
ECT 425
Technical
Fundamentals
Of Distributed
Info Systems
HCI Methods
SE 430
Object-Oriented
Modeling
Software
Engineering
Internet
Application
Development
Foundation Phase
IS 425
Enterprise
Information
Prerequisite Phase
CSC 211
Programming
In Java I
CSC 212
Programming
in Java II
IT 215
Analysis &
Design Techniques
ECT 310
Internet
Application
Development
20
IT Outsourcing
Best jobs in America
1.
2.
3.
4.
5.
Software engineer
College professor
Financial adviser
Human resources
manager
Physician’s
assistant
6.
7.
8.
9.
10.
Market research
analyst
Computer/IT
analyst
Real estate
appraiser
Pharmacist
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/
21
Today’s Agenda
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
22
Data Warehouse Architecture
Client
Client
Query & Analysis
Metadata
Warehouse
Integration
Source
Source
Source
Basic DW: The Repositories
Metadata
Repository
Sources
Optimize for Reporting/Analysis:
• Data quality/accuracy
• Single version of truth across all
systems
• Rapid retrieval of high volume
Staging Area
Data Marts
Operational
Data Store
Optimize for Production:
• Excellent response time
• Workflow-driven
• Vendor application development/support
Data Warehouse
Analysis
Query
Reports
Data mining
The Schumacher Group, April 2008
Multi-Tiered Architecture
other
sources
Operational
DBs
Monitor
&
Integrator
Metadata
Extract
Transform
Load
Refresh
Data
Warehouse
OLAP Server
Serve
Analysis
Query
Reports
Data mining
Data Marts
Data Sources
Data Storage
OLAP Engine
Front-End Tools
Lightly
summarized
27
Simple cumulative
28
Simple cumulative
29
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
Simple cumulative
Data Model for Data
Warehouse
31
Warehouse design: Multi-dimensional Data
Base (MDDB)
Sales
• Multi-Dimensional Database
A
milk
Product
– 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
B
soda
eggs
soap
J
F M A
Date
Multidimensional Data
• Sales volume as a function of product, month,
and region
Dimensions: Product, Location, Time
And Hierarchical summarization paths
Industry Region
Year
Product
Category Country Quarter
Product
City
Office
Month
Month Week
Day
A Sample Data Cube
TV
PC
VCR
sum
1Qtr
2Qtr
3Qtr
4Qtr
sum
Total annual sales
of TV in U.S.A.
U.S.A
Canada
Mexico
sum
Country
Date
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.
Today’s Agenda
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
37
Information Systems IS 425 Class Four
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
DePaul University
38
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)
Information Systems IS 425 Class Four
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
DePaul University
40
Information Systems IS 425 Class Four
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
DePaul University
41
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 42
Information Systems IS 425 Class Four
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
DePaul University
43
Information Systems IS 425 Class Four
Examples of What People are Doing with Data Mining:
 Fraud/Non-Compliance Anomaly
detection
 Recruiting/Attracting
customers
– Isolate the factors that lead to
fraud, waste and abuse
 Maximizing profitability
(cross selling, identifying
profitable customers)
– Target auditing and
investigative efforts more
effectively
 Service Delivery and
Customer Retention
 Credit/Risk Scoring
 Intrusion detection
 Parts failure prediction
– Build profiles of
customers likely to
use which services
 Web Mining
DePaul University
44
Information Systems IS 425 Class Four
Examples of What People are Doing with Data Mining:
 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
DePaul University
45
Information Systems IS 425 Class Four
Examples of What People are Doing with Data Mining:
 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
DePaul University
46
Information Systems IS 425 Class Four
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
DePaul University
47
Information Systems IS 425 Class Four
Database Processing vs. Data Mining Processing
 Query
– Well defined
– SQL

Data
– Operational data

Output
– Precise
– Subset of database
 Query
– Poorly defined
– No precise query language

Data
– Not operational data
– Usually summarized

Output
– Fuzzy
– Not a subset of database
DePaul University
48
Information Systems IS 425 Class Four
Query Examples
 Database
– Find all credit applicants with last name of Smith.
– Identify customers who have purchased more than
$10,000 in the last month.
 Data Mining
– Find all customers who have purchased beer
– Find all credit applicants who are poor credit risks.
(classification)
– Identify customers with similar buying habits. (clustering)
– Find all items which are frequently purchased with beer.
(association rules)
– Describe attributes of customers likely to spend the most
(segmentation)
DePaul University
49
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
?
?
?
?
Where should detergents be placed in the
Store to maximize their sales?
Are window cleaning products purchased
when detergents and orange juice are
bought together?
Is soda typically purchased with bananas?
Does the brand of soda make a difference?
How are the demographics of the
neighborhood affecting what customers
are buying?
Example: Segmentation
(Target variable: Spending level)
All Customers
10.6
Age: 0-25
5.8
Age: 25-55
15.6
Male
20.2
Female
12.7
Age: > 55
8.5
North
9.6
South
6.5
Today’s Agenda
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
40 minutes

Current events
20 minutes

Wrap-up
55
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

56
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.

58
Example: Marriott - Factor Analysis
Identifies What Is Important
Please Rate the
Importance of the Following
Aspects of Your Stay:
Low
Room Cleanliness
Comfort of Bed
High
Months Since Deep Clean
Age of Bed
Speed of Room Service
Use of Fitness Center
Fitness Center
Spending in Restaurant
Room Prices
Check-In Experience
TV Channels
2: Framework
1: Monitoring
Importance of Attributes in Predicting
Propensity for Guest Return:
Room Service
Restaurant
3: Predictive
Room Price
Speed of Check-In
Premium Movie Channel
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 revenuemanagement analytics have taken
root throughout the enterprise

60
7 common targets for
analytical activity
61
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.
62
The Schumacher Group,
April 2008
63
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.”
64
For May 1

Readings on Systems Development

DL: Discussion on data mining/analytics
65
Extra slides
66
Underlying Technology: Hip and Hype
visibility
XQuery
Content Integration
Comprehensive Data Integration Tool Suites
Master Data Management
Data Profiling
OSS DBMS for Mission-Critical Applications
Enterprise Information
Management
SaaS Data Integration
and Data Quality
Metadata Ontology
Management
Entity Resolution
and Analysis
Data Service
Architectures
Open-Source
Data Integration
Tools
XML-Enabled Database
Management Systems
Data Federation/EII
Linux as a Mission-Critical DBMS Platform
Data Quality Tools
OSS DBMS for Non-Mission-Critical Applications
Real-Time Data Integration
Data Warehouse Appliances
Data Quality
Dashboards
Information-Centric Infrastructure
As of June 2007
Technology
Trigger
Peak of
Inflated
Expectations
Trough of
Disillusionment
Slope of Enlightenment
Plateau of
Productivity
time
Years to mainstream adoption:
less than 2 years
2 to 5 years
5 to 10 years
more than 10 years
From "Hype Cycle for Data Management, 2007," 2 July 2007
obsolete
before plateau
Hierarchy of Impact of Information Technology Investments
Business Value Measures
•Revenue growth
•Return on assets
•Revenue per employee
Impact
Sought
Business-Unit Financial
Business Value
C
•Time to bring a new
product to market
•Sales from new products
•Product or service quality
Dilution
of Impact
Business-Unit Operational
Business Value
B
Dilution
of Impact
•Time to implement a new
Business-Unit IT
application
•Cost to implement a new
Applications Business Value
application
Information
Dilution
Technology $
A of Impact
•Infrastructure availability
Firmwide IT
•Cost per transaction
Infrastructure Business Value
•Cost per workstation
Information
Technology $
Source: “Leveraging the New Infrastructure”, Peter Weill & Marianne Broadbent, ©1998
Time
68
Information Technology Portfolio and Business Value
Increased sales
• Shorter time
to market
• Premium
pricing
• Superior
quality
Increased control
Competitive advantage
Better information
Competitive necessity
Better integration
Market positioning
Improved quality
Innovative services
Informational
Cut costs
• 50% fail
• Some spectacular
successes
• 2-to-3 year lead
• Premium pricing
• Higher revenue
per employee
Strategic
Transactional
Increased
throughput
Business integration
Infrastructure
• 25-40%
return
• Higher ROA
• Low risk
Business flexibility
Reduced marginal
cost of business
unit’s IT
More
Less
Higher growth
Higher ROA
Reduced IT costs
Standardization
69
Source: “Leveraging the New Infrastructure”, Peter Weill & Marianne Broadbent, ©1998
IT Portfolio
70
Composition Will Be a New Source for
Business Application Delivery
Build
Buy
User Interface
BPM
Data Integration
ESB/EAI
EAS
Compose
User Experience
Business Process
Information Services
Business Application
Holistic view
Process
People
Technology
73