Transcript IT Infrastructure - Oregon State University

Evolution of
Information Technology
Infrastructure and Architecture
BA 572 - Week 1 – Part 1
Sources:
HBR 397 – 118, “Intranets and Middleware”
Dr. James Coakley (Oregon State University)
MIS Textbook by Turban, Rainer & Potter (Chapter 5)
Mr. Sakthi Angappamudali (The Standard)
Mr. Lee Martin (Hitachi Consulting)
Dr. V.T. Raja (Oregon State University)
BA572 Week 1 (Part 1)
Outline
IT Infrastructure vs. IT Architecture
 Evolution of IT Infrastructure and Architecture



Major eras of the computer industry

Terminology/Acronyms

Centralized/Decentralized/Distributed Computing

TPS, MIS, DSS, ES, Middleware, OOP, DW, OLAP, Data Mining etc.
Comment on Performance Metrics
Definitions
 Information
Technology (IT) Infrastructure:
physical facilities, services and management
that support computing resources
 Information
Technology
 Hardware
 Software
 Database
 Telecommunications
 IT
personnel
& Networks
Definitions
 Information
Systems (IS) Architecture: the
“plan” that aligns IT infrastructure with
business needs
 Help
people effectively fulfill their information
needs
 Note that the term “Information Architecture” is
also being used to describe process of designing
web sites
Adapted from "Intranets and Middleware", HBR 397-118.
Evolution of Information Technology Infrastructure
Ability to fill information needs
Web Services
Distributed
db
Client/Server
db
db
db
db
db
db
db
PC/LAN
Mainframe
1960
1980
1990
2000
S1
Mainframe
Data Processing Era
 IT
Infrastructure (host-centric processing)
 Hardware:
Mainframe with text-based terminals
 Software: Independent functional applications
 Served
 Data
one purpose
Storage: independent “files” for each
functional application
 Telecommunications: Limited support of
distributed operations
 IT Personnel: technically oriented
Mainframe
IS Architecture:
Transaction Processing System (TPS)
 Emerged
 Collect,
in the early days of IS
store, and process transactions
 Source
 Perform
documents are basis for input
routine, repetitive tasks
 Found in all functions of an organization
 If they fail, the whole organization may suffer
 Automate “highly structured” decision processes
 Payroll
Mainframe
IS Architecture:
Management Information System (MIS)
 Convert/use
TPS data to support monitoring
 Alert
managers to problems or opportunities
 Provide periodic and routine reports
 e.g.,
summary reports, exception reports, comparison
reports
 Provide
structured information to support decision
making
 Resulted
in “Information overload”
Mainframe
IS Architecture:
Centralized Corporate Structure
Functional Transaction
Processing System
Management
Information System
Executive
Managerial
Purchasing
Sales
Inbound
Raw Production FinishedOutbound
Logistics Materials
Goods Logistics
Operational
PC/LAN
Micro-Computing Era
 IT
Infrastructure (PC environment)
 Hardware:
PCs (low cost compared to mainframe)
 Software: Individual PC applications
 Data storage: Individual files linked to apps
 Telecommunications: low-speed LANs
 IT Personnel: technically oriented & mainframe
biased
PC/LAN
db
db
db
db
IS Architecture:
Decision Support Systems
 Proliferation
of desktop applications
 Why?
 TPS/MIS
were not providing information needed to
support decisions
 “End-user”
development
 Undocumented
 Proliferation
spreadsheet models
of localized data storage
PC/LAN
IS Architecture
Functional Transaction
Processing System
Management
Information System
Executive
Desktop Decision
Support System
Managerial
Purchasing
Sales
Inbound
Raw Production FinishedOutbound
Logistics Materials
Goods Logistics
Operational
Client/Server
db
Client/Server Era
 IT
Infrastructure (distributed computing
environment)
Hardware: PCs and Specialized Servers
Software: Facilitating
Data storage: Distributed Relational database and
centralized warehouse
 Telecommunications: high-speed LANs
 Network: Client/Server
 IT Personnel: technically skilled, business oriented



 Information

Systems architecture?
Share applications and data within and across functional
areas
Client/Server
db

Facilitating Software Systems
Office automation

IT for “office” employees

Document tracking, communication, scheduling, etc.
Client/Server
Facilitating Software Systems
(cont’d)
db
 Decision
Support Systems
 Provide
information to support “semi-structured”
decision making
 Effectiveness
 Expert
focus
Systems
 Knowledge-base
integrated with DSS
 Most are “rule-based” systems that process facts, not
numbers
 Credit
evaluation
 Cisco/DELL tech support
Client/Server
Database Approaches
db
 Centralized
 All
data in one location
 Promotes
maintenance and security
 Subject to single point of failure
Distributed
db
db
db
db
db
Database Approaches
 Distributed
data management
 Get
data closer to applications
 Replicated
 Complete
copies in multiple locations
 Significant overhead
 Partitioned
 Each
location has portion of database
 Data management becomes
 Complex Concurrency Control
an issue
Distributed
db
db
db
db
db
Online Transaction
Processing
 Transactions
used to interact with a relational
“client-server” database
 For
each transaction, OLTP typically deals with
a small number of rows from the tables
 The transactions are typically highly
structured, repetitive and have predetermined
outcomes
 E.g., orders, changing customer address, etc.
Client/Server Systems
Functional Transaction
Processing System
Executive
db
Client/Server
System
Managerial
db
Purchasing
db
db
db
db
Sales
Inbound
Raw Production FinishedOutbound
Logistics Materials
Goods Logistics
Operational
Distributed Computing
Middleware
db
db
db
db
Network Era
(Distributed Computing)
 IT
Infrastructure (distributed computing
environment)
 Hardware:
PCs and high-end Servers
 Software: Enabling, enterprise-wide
 Data storage: Distributed Relational Database
 Telecommunications: high-speed WAN
 Network: Middleware
 IT Personnel: still technical, but business
awareness
Distributed Computing
Middleware
db
db
db
db
Introduction of
Middleware
 Software
that makes it possible for systems on
different platforms to communicate with each
other.
 Allows
applications to talk to each other
 Consistent Application
Program Interface (API)
 Code application to talk to middleware, not underlying
resources
 Upgrade/modify underlying resources without needing
to modify applications
Distributed Computing
Middleware
db
db
db
 ORB
db
Object Request Broker
(ORB)
involves synchronous communication
and location/platform transparency.
 ORB uses object-oriented programming
methods.
Distributed Computing
ORB (cont’d)
Middleware
db
db
db
 ORB
db
architecture:
ORB
activate
service
locate
service
establish connection
Client
Remote Service
communicate
Distributed Computing
File Sharing
Middleware
db
db
db
db
 Napster:
ORB
activate
service
locate
service
establish connection
Request
Stored Files
communicate
Peer-to-Peer
File Sharing
Distributed Computing
Middleware
db
db
db
 Kazaa:
db
Member
Member
Member
Member
Member
Request
Member
Member
Member
Member
Member
Member
Member
Distributed Computing
Middleware
db
db
db
Advantages of ORB
Middleware
db
 Anonymous
interaction among applications
 Integrate
new client/server applications with
existing legacy, mission-critical applications
 Easier
development environment
 Reduce
cost
 Improve time-to-market of applications
 Enables
distributed data environment
 Enables dynamic web applications
Distributed Computing
Middleware
db
db
db
db
 Switching

Disadvantages of ORB
Middleware
costs are high
Upgrade from previous “Middleware” solutions
 Requires
high technical expertise
 Tend to outsource
 Lengthy deployment time
Distributed Computing
Middleware
db
db
db
Unresolved Issues
with ORB
db
 Security
 Scalability
 Related
 Rapidly
to network capacity
changing technologies
Distributed Computing
Middleware
db
db
db
DBMS Applications
db
 With
advent of high-speed, distributed
architectures expanded our use of database
beyond capturing and storing transaction data
 Knowledge
 Process
Discovery
of extracting useful knowledge from volumes
of data
 Supported by:
 Massive
data collection (Data Warehouse/Data Marts)
 Multiprocessor computing
 On-line Analytical Processing (OLAP)/Data mining
Distributed Computing
Data Warehouse
Middleware
db
db
db
db
 Collection
of data in support of decision making
process that is:
Subject-oriented: organized by entity, not application
 Integrated: stored in one place, even though it originated
from a variety of sources


Crosses functional boundaries of an organization
Time-variant: represents a snapshot at one point in time
 Nonvolatile: data is read-only
 Typically very large

Data Warehouse

Large repository of detailed and summary data used to
support the strategic decision making process for the
enterprise

Stores current and historical data (internal and external)

Integrates data from organization’s disparate information
systems used by functional units

Involve hundreds of gigabytes, and terabytes of data

Run on very powerful computers

Expensive
Data Warehousing Process
OLTP, DW and DM - Data Characteristics
• OLTP - Raw Detail
No/Minimal History
•DW-Integrated •History
• Targeted
•Scrubbed
•Summaries • Specialized (OLAP)
Data Mart
Data
Warehouse
OLTP
Systems
Functional
IS
External
Data
End User
Workstations
Central
Repository
•Extract
•Design
•Scrub
•Mapping
•Transform
•Load
•Index
•Aggregation
•Replication
•Data Set Distribution
Distributed Computing
Middleware
db
db
db
 Data
Multidimensional
Database (cont’d)
db
marts
 Scaled-down
version of a data warehouse that
focuses on a specific area
 e.g.,
a department, a business process
An Incremental Approach
Sales
Distribution
Product
Glossary
Marketing
Customer
Common Business
MetricsAccounts
Common Business Rules
Common Business Dimensions
Operations
and Inventory
Common Logical Subject Area ERD
Finance
Vendors
Individual Architected Data Marts
The Eventual Result
Sales
Distribution
Product
Architected
Enterprise
Foundation
Marketing
Finance
Customer
Operations
and Inventory
Accounts
Vendors
Enterprise Data Warehouse
Distributed Computing
Middleware
db
db
db
Multidimensional
Database
db
OLTP
not good when doing analysis of
data – poor performance
 OLAP
– on-line analytical processing
On-line Transaction Processing (OLTP) and
On-line Analytical Processing (OLAP)
 OLTP:
Immediate processing/analysis and handling
of multiple concurrent transactions from
customers/users

Example:
 OLAP:
Capability for manipulating and analyzing
large volumes of data from multiple perspectives
(multidimensional analysis)

Example:
“Slice and Dice” an OLAP Cube
Distributed Computing
Advantages of OLAP
Middleware
db
db
db
db
 All
hierarchical or aggregated values can be
pre-calculated in the cube rather than
accessing the Warehouse
 Major
 Each
 Not
reduction in query time
cube makes “business sense”
normalized data structures
Distributed Computing
Massive Data Analysis
Middleware
db
db
db
 Data
db
mining
 Provides
a means to extract patterns and
relationships
 Example: Analyze
sales data to identify products that
may be attractive to a customer
 Amazon.com
 Two
buyer suggestions
capabilities
 Automated
prediction of trends and behaviors
 Automated discovery of previously unknown patterns
Data Mining
 Some
Benefits:
Market Segmentation
 Fraud Detection
 Market Basket Analysis
 Trend Analysis

Business Intelligence
 BI/Analytics
 Used
software (suite):
to collect, store, analyze and present
 sufficient
and accurate information in a timely manner
and in a usable form
 Includes
OLAP, data mining, statistical analysis
 Has a positive impact on business strategy, and
operations
 Addresses analysis paralysis caused due to
information overload?
Business Intelligence
Enterprise BI Suites
and Platforms
The Decision Making Roadmap
Business Planning
Actions
Vision
Knowledge
Transaction
Systems
Decision
Support
Systems
Data
RUN
•
•
•
•
Operational
Functional
Current
Detailed
Users
Information
MANAGE
•
Analyze What If
Scenarios
• History
• Detailed
Knowledge Brokers
Executive
Information
Systems?
GROW
•
MultiDimensional
• History
• Summary
Management
Distributed Computing
Network Enabling Software
Middleware
db
db
db
db
Supply Chain
Management
Enterprise
Wide Systems
Supplier
Customer
Relationship
Management
Enterprise
Wide Systems
Enterprise
Wide Systems
Customer
Internet Era
 IT
Infrastructure (Web-enabled)
 Hardware:
Low-end PC with Browser, high-end
Servers
 Software: Web extensions
 Database: Distributed Relational
 Network: Use IP-based standards
 Telecommunications: broadband
 IT Personnel: Business analysts, technical
specialties
Business use of the Internet:
Electronic Commerce
 B2C:
Internet
 B2B: Extranet
 B2E: Intranet
Individual
Internet
 E-business:
 Subset
of e-commerce
 Transactions between
business partners
Enterprise
Intranet
Extranet
Supplier/
Customer
Web-based Solutions
 Early
attempts to incorporate WWW into interorganizational systems
 Static, state-less web pages
Complicated navigation
 Not “connected” to underlying data


Page not dynamically updated when data changes
 Dynamic
and interactive web applications connected
to enterprise database(s)
 Web 2.0
http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/0
9/30/what-is-web-20.html
 http://en.wikipedia.org/wiki/Web_2.0

Web Services
db
db
Web Services
db
 Standards
are evolving
 Security?
 Do
web services 'solve' interoperability
between applications?
 Need
ERP?
Comment on Performance Metrics
 How


does IT add value and how much value?
TCO/ROI
Tangible vs. Intangible Impacts
 What



is(are) purpose(s) of IT applications?
Automate
Facilitate/Informate
Enable business strategy/significant competitive
advantage

Alignment of IT and Business Strategy