Evolution of Information Technology Infrastructure and Architecture

1. 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)

2. 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

3. Definitions • Information Technology (IT) Infrastructure: physical facilities, services and management that support computing resources • Information Technology • Hardware • Software • Database • Telecommunications & Networks • IT personnel

4. 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

5. Adapted from "Intranets and Middleware", HBR 397-118.

6. db db db db Web Services Client/Server db db db Evolution of Information Technology Infrastructure Distributed db PC/LAN Mainframe

7. Mainframe Data Processing Era • IT Infrastructure (host-centric processing) • Hardware: Mainframe with text-based terminals • Software: Independent functional applications • Served one purpose • Data Storage: independent “files” for each functional application • Telecommunications: Limited support of distributed operations • IT Personnel: technically oriented

8. Mainframe IS Architecture:Transaction Processing System (TPS) • Emerged in the early days of IS • Collect, store, and process transactions • Source documents are basis for input • Perform routine, repetitive tasks • Found in all functions of an organization • If they fail, the whole organization may suffer • Automate “highly structured” decision processes • Payroll

9. 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”

10. Mainframe IS Architecture: Centralized Corporate Structure Functional Transaction Processing System Executive Management Information System Managerial Purchasing Sales InboundLogistics RawMaterials Production FinishedGoods OutboundLogistics Operational

11. 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

12. PC/LAN db IS Architecture:Decision Support Systems db db db • Proliferation of desktop applications • Why? • TPS/MIS were not providing information needed to support decisions • “End-user” development • Undocumented spreadsheet models • Proliferation of localized data storage

13. PC/LAN IS Architecture Functional Transaction Processing System Executive Management Information System Desktop DecisionSupport System Managerial Purchasing Sales InboundLogistics RawMaterials Production FinishedGoods OutboundLogistics Operational

14. 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

15. Client/Server db Facilitating Software Systems • Office automation • IT for “office” employees • Document tracking, communication, scheduling, etc.

16. Client/Server db Facilitating Software Systems (cont’d) • Decision Support Systems • Provide information to support “semi-structured” decision making • Effectiveness focus • Expert Systems • Knowledge-base integrated with DSS • Most are “rule-based” systems that process facts, not numbers • Credit evaluation • Cisco/DELL tech support

17. Client/Server db Database Approaches • Centralized • All data in one location • Promotes maintenance and security • Subject to single point of failure

18. 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 an issue • Complex Concurrency Control

19. 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.

20. db db db db db db Client/Server Systems Functional Transaction Processing System Executive Client/Server System Managerial Purchasing Sales InboundLogistics RawMaterials Production FinishedGoods OutboundLogistics Operational

21. 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

22. 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

23. 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.

24. Distributed Computing Middleware db db db db ORB (cont’d) • ORB architecture: ORB activate service locate service establish connection Remote Service Client communicate

25. Distributed Computing Middleware db db db db File Sharing • Napster: ORB activate service locate service establish connection Stored Files Request communicate

26. Distributed Computing Middleware db db db db Peer-to-Peer File Sharing Member • Kazaa: Member Member Member Member Member Request Member Member Member Member Member Member

27. Distributed Computing Middleware db db db db Advantages of ORB Middleware • 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

28. Distributed Computing Middleware db db db db Disadvantages of ORB Middleware • Switching costs are high • Upgrade from previous “Middleware” solutions • Requires high technical expertise • Tend to outsource • Lengthy deployment time

29. Distributed Computing Middleware db db db db Unresolved Issues with ORB • Security • Scalability • Related to network capacity • Rapidly changing technologies

30. 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 Discovery • Process 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

31. Distributed Computing Middleware db db db db Data Warehouse • 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

32. 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

33. Data Warehousing Process OLTP, DW and DM - Data Characteristics • OLTP - Raw Detail • No/Minimal History • DW-Integrated • Scrubbed • History • Summaries • Targeted • Specialized (OLAP) Data Warehouse OLTP Systems Functional IS Central Repository External Data • Design • Mapping • Extract • Scrub • Transform • Load • Index • Aggregation Data Mart End User Workstations • Replication • Data Set Distribution

34. Distributed Computing Middleware db db db db Multidimensional Database (cont’d) • Data marts • Scaled-down version of a data warehouse that focuses on a specific area • e.g., a department, a business process

35. Distribution Sales Product Customer Accounts Marketing Operations and Inventory Finance Vendors An Incremental Approach Glossary Common Business Metrics Common Business Rules Common Business Dimensions Common Logical Subject Area ERD Individual Architected Data Marts

36. Distribution Sales Product Customer Accounts Marketing Operations and Inventory Finance Vendors Enterprise Data Warehouse The Eventual Result Architected Enterprise Foundation

37. Distributed Computing Middleware db db db db Multidimensional Database • OLTP not good when doing analysis of data – poor performance • OLAP – on-line analytical processing

38. 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:

39. “Slice and Dice” an OLAP Cube

40. Distributed Computing Middleware db db db db Advantages of OLAP • All hierarchical or aggregated values can be pre-calculated in the cube rather than accessing the Warehouse • Major reduction in query time • Each cube makes “business sense” • Not normalized data structures

41. Distributed Computing Middleware db db db db Massive Data Analysis • 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 • Automated prediction of trends and behaviors • Automated discovery of previously unknown patterns

42. Data Mining • Some Benefits: • Market Segmentation • Fraud Detection • Market Basket Analysis • Trend Analysis

43. Business Intelligence • BI/Analytics software (suite): • Used 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?

44. Business Intelligence Enterprise BI Suites and Platforms

45. The Decision Making Roadmap Business Planning Actions Vision Knowledge Transaction Systems Decision Support Systems Executive Information Systems? Data Information RUN MANAGE GROW • Operational • Functional • Current • Detailed • Analyze What If Scenarios • History • Detailed • Multi-Dimensional • History • Summary Management Users Knowledge Brokers

46. Distributed Computing Middleware db db db db Network Enabling Software Supply ChainManagement Customer Relationship Management Enterprise Wide Systems Enterprise Wide Systems Enterprise Wide Systems Supplier Customer

47. 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

48. Business use of the Internet:Electronic Commerce • E-business: • Subset of e-commerce • Transactions between business partners • B2C: Internet • B2B: Extranet • B2E: Intranet Enterprise Supplier/ Customer Individual Extranet Internet Intranet

49. Web-based Solutions • Early attempts to incorporate WWW into inter-organizational 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/2005/09/30/what-is-web-20.html • http://en.wikipedia.org/wiki/Web_2.0

50. Web Services db db db Web Services • Standards are evolving • Security? • Do web services 'solve' interoperability between applications? • Need ERP?