MGMT 382 1st Edition Lecture 9Last Lecture I. Vasa Case AnalysisCurrent LectureI. Intro to Databases II. Before Databases III. Problems with Data Dependency IV. Scrubbing it V. DBMSVI. Advantages of DBMSVII. Costs and Risks Current Lecture I. Introduction to Databasesa. Data- meaningful facts, text, graphics, images, sound, video segments b. Information: data processed to be useful in decision making c. Database: An organized collection of logically realted data i. Her at Purdue: student record—library use, co-rec use, registrar, advisor in krannertd. Metadata: description of dataII. Before Dataa. File Processingb. Disadvantagesi. Program- data dependence; all programs maintain metadata for each file they use- each language expected to see data in a particular data 1. Mac vs Windows//Ex) reading someone elses notes that are in Spanish—not useful to you ii. Data Redundancy: duplication of data- different systems/programs have separate copies of the same data-result of program-data dependency 1. Duplication causes eventual problems can cause inconsistency iii. Limit data Sharing 1. No centralized control of the data 2. Cant understand it not usefuliv. Lengthy development times 1. Programmers most design their own file formats These notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.v. Excessive program maintenance 1. 80% of information systems budget 2. EX) employee’s salary—metadata has to be written the same, so if it changes for every employee, it changes for the metadata—as data is changed--? Hunt down every place that the data gets changed c. Problems with Data dependency i. Each application programmer must maintain own data ii. Needs to include code for metadata file iii. Own processing routine iv. Lack of coordination and central control v. Non-standard file formats EX) Accounting department uses Fortran, [7,2]= 5 figure salary, in a data file, the HR departmentuses Cobol so when they take the data file from Accounting, problems will occur because each system reads the data differently I. Scrubbing- even you scrub the data in the previous example: a. Redundancy- salary shows up in the Accounting department and HR- waste of space to duplicate data- causes maintenance issues b. ***BIGGEST PROBLEM: when data changes in one file, it usually causes inconsistency (this compromises the data- bad data bad information  bad decisions bad action) c. Solution: databasei. Central repository of shared dataii. Data is managed by a controlling agent iii. Stored in a standardized, convenient form II. Requires a database Management System (DBMS)- data storage and retrievalsystem which permits data to be stored non-redundantly* while making it appear to the user as if data is well-integrated III. Advantagesa. Program data independence: metadata stored in DBMS so applications don’t need to worry about data formats b. Queries and updates managed to process data programs don’t need to process data access routines i. Results in: increased app development maintenance productivity c. Minimal Redundancy i. Increase integrity and consistency d. Improved data sharing- different users get different views e. Enforment of standards f. Improved data quality- constraints, data validation rules g. Security backup, recover, consistency- disaster recovery is easierIV. Costs and Risks a. Up front costs i. Installation management cost and complexityii. Conversion cots b. Ongoing costs i. Requires new, specialized personnel ii. Need for explicit backup and recovery c. Organizational conflict i. Old habits die hard V. E-R Model Constructs a. Entity instance: person, place, object, event, concept (row)b. Entity type: collection of entities (table)c. Attribute: property/characteristics of an entity type (field)d. Relationship instance: link between entities i. Primary key-foreign key equivalencies 1. Relationship types: link between entity