1. IS605/606: Information Systems Instructor: Dr. Boris Jukic Managing Information Resources

2. Data vs. Information vs. Knowledge Data: Raw (non-processed) facts that are recorded – May have an implicit meaning – May be devoid of meaning if context not provided Information: – Processed data used for decision-making – Data provided with specific context Knowledge – Skill, know how – Information with implied direction or intent Intelligence (as in military or business intelligence)

3. The Three- Level Database Model Managing Data CONCEPTUAL LEVEL LOGICAL LEVEL PHYSICAL LEVEL

4. Four (Logical) Data Models Hierarchical Model (Legacy) – Standard tree-like structure Network Model (Legacy) – More than one parent allowed Relational Model – First truly data and structurally independent model – No predetermined navigational maps as in two older models – The Database technology of choice Object Model – Tables become objects

5. Managing Data: Getting Corporate Data into Shape Database administration – Using and managing DB software and hardware Data Administration – Managing data architecture and definitions – Until recently, not always taken very seriously Problem of Inconsistent Data Definitions – Same data in different files under different names with different update cycles – Different data with same name – Inconsistent view of the facts within en organization – ERP often viewed as the best solution Software or Policy?

6. Enterprise Data Planning  CASE EXAMPLE: Monsanto

7. Enterprise Data Planning: Monsanto ERD: Enterprise Reference Data – Same set of tables used for different purposes – Single master table can be presented in many different views (combination of columns) Purchasing view, engineering view, accounting view – This is in contrast with the “stovepipe” approach Purchasing tables (databases), accounting tables (databases), engineering tables (databases) ERD “Stewardship” – “Data Police” function: independent form the rest of the MIS department, enforces data standards – Entity (Table) Specialists: key personnel most knowledgeable and interested in particular group(s) of data: purchasing, engineering, etc. Use “standard” external codes whenever possible – Others are likely to use them – Tested for validity and uniqueness

8. Four Types of Information

9. Data Records vs. Documents Data records – Explicit structure – Defined rules – Use standard DB tools to search and query Documents – Implied (or no) structure – Ambiguous rules with many exceptions – Hard to search and query with standard tools Specialized algorithms needed

10. Bridging the gap between the documents and records Example: business letter formatted with XML – http://people.clarkson.edu/~bjukic/IS400/examples/ch20_XML/letter.xml http://people.clarkson.edu/~bjukic/IS400/examples/ch20_XML/letter.xml E-R Model in class

11. Old way: Webmaster encodes a document in in html and posts it as a file on the corporate web server – Each department does it independently New way: content is dynamic and modular (XML) – Tags have meaning beyond formatting – More systemic approach is needed Web Content Management

12. Content Management Internal and external content Content management software Document as a database The way content is structured internally The way content is seen by others Outside-in approach Localization Multi-channel distribution 1. 2.3.

13. Case : Eastman Chemical Company Flat HTML files create a maintenance bottleneck Content management product based on preapproved templates – Also manages rights to update or publish web documents Site redesign based on external markets rather than internal product divisions – See site index