1. Evolution of Information Technology Infrastructure BA 572 - Week 1

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

3. 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 now being used to describe process of designing web sites

4. Performance Metrics “ROI”  How does IT add value?  What is purpose of IT applications? Automate Facilitate/Informate Enable

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

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

7. 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 Mainframe

8. 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 Mainframe

9. 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

10. IS Architecture: Centralized Corporate Structure Executive Operational Managerial Inbound Logistics Purchasing Raw Materials ProductionFinished Goods Outbound Logistics Sales Functional Transaction Processing System Management Information System Mainframe

11. 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

12. IS Architecture: Decision Support Systems  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 PC/LAN db

13. IS Architecture Executive Operational Managerial Inbound Logistics Purchasing Raw Materials ProductionFinished Goods Outbound Logistics Sales Functional Transaction Processing System Management Information System Desktop Decision Support System PC/LAN

14. 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

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

16. 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 Client/Server db

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

18. 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 db Distributed db

19.  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. Online Transaction Processing db Distributed db

20. Client/Server Systems Executive Operational Managerial Inbound Logistics Purchasing Raw Materials ProductionFinished Goods Outbound Logistics Sales Functional Transaction Processing System Client/Server System db

21. 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 db Distributed Computing Middleware

22. 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 db Distributed Computing Middleware

23. Object Request Broker (ORB)  ORB involves synchronous communication and location/platform transparency.  ORB uses object-oriented programming methods. db Distributed Computing Middleware

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

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

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

27. 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 db Distributed Computing Middleware

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

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

30. 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 db Distributed Computing Middleware

31. 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 db Distributed Computing Middleware

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

33. “Slice and Dice” an OLAP Cube

34. 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 db Distributed Computing Middleware

35. 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 db Distributed Computing Middleware

36. 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 Example: Shopping cart analysis db Distributed Computing Middleware

37. Network Enabling Software SupplierCustomer Enterprise Wide Systems Supply Chain Management Customer Relationship Management db Distributed Computing Middleware

38.  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 Internet Era

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

40. 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

41. Hurdles for web services  Standards are evolving, not set  Security  Web services do not 'solve' interoperability between applications Hence – need ERP db Web Services