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IS605/606: Information Systems Instructor: Dr. Boris Jukic Managing Information Resources Data vs. Information vs. Knowledge Data: Raw (non-processed) facts that are recorded – – Information: – – May have an implicit meaning May be devoid of meaning if context not provided 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) Managing Data The ThreeLevel Database Model CONCEPTUAL LEVEL LOGICAL LEVEL PHYSICAL LEVEL Four (Logical) Data Models Hierarchical Model (Legacy) – Network Model (Legacy) – More than one parent allowed Relational Model – – – Standard tree-like structure 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 Managing Data: Getting Corporate Data into Shape Database administration – Data Administration – – Using and managing DB software and hardware 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? Enterprise Data Planning CASE EXAMPLE: Monsanto 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) – This is in contrast with the “stovepipe” approach Purchasing tables (databases), accounting tables (databases), engineering tables (databases) ERD “Stewardship” – – Purchasing view, engineering view, accounting view “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 Four Types of Information 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 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 E-R Model in class Web Content Management 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 Content Management 1. Internal and external content •The way content is seen by others •Outside-in approach •Localization •Multi-channel distribution 3. •Content management software •Document as a database •The way content is structured internally 2. 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