Evolution of Information Technology Infrastructure

Evolution of Information Technology Infrastructure BA 572 - Week 1 Definitions  Information Technology (IT) Infrastructure: physical facilities, services and management that support computing resources  Information  Hardware  Software  Database  Telecommunications Technology & Networks  IT personnel Definitions  Information Systems (IS) Architecture: the “plan” that aligns IT infrastructure with business needs people effectively fulfill their information needs  Note that the term “Information Architecture” is now being used to describe process of designing web sites  Help Performance Metrics “ROI” does IT add value?  What is purpose of IT applications?  Automate  How  Facilitate/Informate  Enable Adapted from "Intranets and Middleware", HBR 397-118. Evolution of Information Technology Infrastructure Web Services Ability to fill information needs Distributed db db db db db Client/Server db db db PC/LAN Mainframe 1960 1980 1990 2000 S1 Mainframe Data Processing Era  IT Infrastructure (host-centric processing)  Served Mainframe with text-based terminals  Software: Independent functional applications one purpose  Data  Hardware: 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,  Perform in the early days of IS store, and process transactions documents are basis for input  Source 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  Alert TPS data to support monitoring managers to problems or opportunities  Provide periodic and routine reports  e.g., summary reports, exception reports, comparison reports  Provide structured information to support decision in “Information overload” making  Resulted 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) 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  Hardware: PC/LAN db db db db IS Architecture: Decision Support Systems of desktop applications  Proliferation  Why?  TPS/MIS were not providing information needed to support decisions  “End-user” development spreadsheet models  Undocumented  Proliferation of localized data storage PC/LAN IS Architecture Functional Transaction Processing System Management Information System Desktop Decision Support System Executive 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 IT for “office” employees   Office automation  Document tracking, communication, scheduling, etc. Client/Server db Facilitating Software Systems (cont’d) Support Systems focus  Provide  Decision information to support “semi-structured” decision making  Effectiveness  Expert Systems  Knowledge-base integrated with DSS  Most are “rule-based” systems that process facts, not numbers evaluation  Cisco/DELL tech support  Credit Client/Server db Database Approaches  Centralized  All data in one location maintenance and security  Subject to single point of failure  Promotes Distributed db db db db db Database Approaches data management  Distributed  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  For used to interact with a relational “client-server” database 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 db db db db Purchasing 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) 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  Hardware: 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 db Object Request Broker (ORB)  ORB involves synchronous communication and location/platform transparency.  ORB uses object-oriented programming methods. Distributed Computing Middleware ORB (cont’d) db db db db  ORB architecture: ORB locate service establish connection Remote Service communicate activate service Client Distributed Computing Middleware File Sharing db db db db  Napster: ORB locate service establish connection Stored Files communicate activate service Request Distributed Computing Middleware Peer-to-Peer File Sharing Member Member Member Member Member db db db db  Kazaa: Request Member Member Member Member Member Member Member Distributed Computing Middleware Advantages of ORB Middleware interaction among applications db db db db  Anonymous  Integrate new client/server applications with existing legacy, mission-critical applications  Easier development environment cost  Improve time-to-market of applications  Reduce distributed data environment  Enables dynamic web applications  Enables Distributed Computing Middleware Disadvantages of ORB Middleware db db db db  Switching  costs are high Upgrade from previous “Middleware” solutions  Requires high technical expertise  Tend to outsource  Lengthy deployment time Distributed Computing Middleware Unresolved Issues with ORB db db db db  Security  Scalability  Related to network capacity  Rapidly changing technologies Distributed Computing Middleware db db db db DBMS Applications  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: data collection (Data Warehouse/Data Marts)  Multiprocessor computing  On-line Analytical Processing (OLAP)/Data mining  Massive Distributed Computing Middleware Data Warehouse 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  Distributed Computing Middleware Multidimensional Database db db db db OLTP  OLAP not good when doing analysis of data – poor performance – on-line analytical processing “Slice and Dice” an OLAP Cube Distributed Computing Middleware Advantages of OLAP db db db db  All hierarchical or aggregated values can be pre-calculated in the cube rather than accessing the Warehouse  Major reduction in query time  Each  Not cube makes “business sense” normalized data structures Distributed Computing Middleware Multidimensional Database (cont’d) db db db db  Data marts a department, a business process  Scaled-down  e.g., version of a data warehouse that focuses on a specific area Distributed Computing Middleware Massive Data Analysis db db db db  Data 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 buyer suggestions  Two capabilities Shopping cart analysis prediction of trends and behaviors  Automated discovery of previously unknown patterns  Example:  Automated Distributed Computing Middleware Network Enabling Software db db db db Supply Chain Management Customer Relationship Management Enterprise Wide Systems Enterprise Wide Systems Enterprise Wide Systems Supplier Customer Internet Era  IT Infrastructure (Web-enabled) Low-end PC with Browser, high-end  Hardware: 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: of e-commerce  Transactions between business partners Supplier/ Customer  Subset Enterprise Extranet Intranet Web-based Solutions attempts to incorporate WWW into inter-organizational systems  Static, state-less web pages navigation  Not “connected” to underlying data  Page  Early  Complicated not dynamically updated when data changes Web Services db db db Hurdles for web services are evolving, not set  Standards  Security  Web services do not 'solve' interoperability between applications  Hence – need ERP