Management information system database management


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Management information system database management

  1. 1. Database Management
  2. 2. Why Study Data Resource Management?• Today’s business enterprises cannot survive or succeed without quality data about their internal operations and external environment.Definition:• A managerial activity that applies information systems technologies to the task of managing an organization’s data resources to meet the information needs of their business stakeholders
  3. 3. Foundation Data Concepts• Character – single alphabetic, numeric or other symbol• Field – group of related characters• Entity – person, place, object or event• Attribute – characteristic of an entity
  4. 4. Foundation Data Concepts• Record – collection of attributes that describe an entity• File – group of related records• Database – integrated collection of logically related data elements
  5. 5. Traditional File Processing vs DBMSDefinition: Traditional File Processing• Data are organized, stored, and processed in independent files of data recordsDefinition: Database Management Systems• Software that controls the creation, maintenance, and use of databases
  6. 6. File Processing Systems
  7. 7. Problems of File Processing• Data Redundancy – duplicate data requires an update to be made to all files storing that data• Lack of Data Integration – data stored in separate files require special programs for output making ad hoc reporting difficult• Data Dependence – programs must include information about how the data is stored so a change in storage format requires a change in programs
  8. 8. Database Management ApproachDefinition:• Consolidates data records into one database that can be accessed by many different application programs.• Software interface between users and databases• Data definition is stored once, separately from application programs
  9. 9. Database Management Approach
  10. 10. DBMS Software Components
  11. 11. Uses of DBMS Software
  12. 12. Types of Databases
  13. 13. Types of Databases• Operational – store detailed data needed to support the business processes and operations of a company• Distributed – databases that are replicated and distributed in whole or in part to network servers at a variety of sites
  14. 14. Types of Databases• External – contain a wealth of information available from commercial online services and from many sources on the World Wide Web• Hypermedia – consist of hyperlinked pages of multimedia
  15. 15. Fundamental Database Structures
  16. 16. Database Structures• Hierarchical – relationships between records form a hierarchy or treelike structure. A record is subdivided into segments that are connected to each other in one to many parent – child relationship. Relationships among the records are one-to-many.• Network – data can be accessed by one of several paths because any data element or record can be related to any number of other data elements. An older logical database model that is useful for depicting many-to-many relationships. The network structure can represent more complex logical relationships, and is still used by many mainframe DBMS packages.
  17. 17. Relational Database StructureDefinition:• All data elements within the database are viewed as being stored in the form of simple tables
  18. 18. Relational Database
  19. 19. Multidimensional Database StructureDefinition:• Variation of the relational model that uses multidimensional structures to organize data and express the relationships between data
  20. 20. Multidimensional Database Structure
  21. 21. Object-Oriented Database StructureDefinition:• Can accommodate more complex data types including graphics, pictures, voice and text• Encapsulation – data values and operations that can be performed on them are stored as a unit• Inheritance – automatically creating new objects by replicating some or all of the characteristics of one or more existing objects
  22. 22. Evaluation of Database Structures• Hierarchical data structure is best for structured, routine types of transaction processing.• Network data structure is best when many-to- many relationships are needed.• Relational data structure is best when ad hoc reporting is required.
  23. 23. Database Development• Enterprise-wide database development is usually controlled by database administrators (DBA)• Data dictionary – catalog or directory containing metadata• Metadata – data about data
  24. 24. Database Development Process• First, develop a Conceptual design – - an abstract model of the database from the user or business perspective . – - Create physical and logical view• Second, organize with Entity-Relationship (ER) modeling – process of planning the database design – Entity classes  Instance  Identifiers  Relationships
  25. 25. Database Development Process• Third, analyze the data structure by applying the Normalization process – method that reduces a relational database to its most streamlined form – Helps achieve • minimum redundancy • maximum data integrity • best processing performance
  26. 26. Database Development Process• Fourth, physically implement the data structure in the database management system software ( – Create tables – Define fields and field properties – Establish primary keys – Define table relationships – Add actual data (records) to tables
  27. 27. Logical vs. Physical Views• Logical – logical consist of conceptual design within an abstract model which data elements and relationships (subschemas) are used in the model.• Physical – the design shows how the database is arranged, physically stored and accessed on the storage devices of a computer system.
  28. 28. Logical and Physical Database Views
  29. 29. Database Maintenance• Updating a database continually to reflect new business transactions and other events• Updating a database to correct data and ensure accuracy of the data
  30. 30. Data Mining Definition:Analyzing the data in a data warehouse toreveal hidden patterns and trends inhistorical business activity
  31. 31. Data Mining Uses• Perform “market-basket analysis” to identify new product bundles.• Find root causes to quality or manufacturing problems.• Prevent customer attrition and acquire new customers.• Cross-sell to existing customers.• Profile customers with more accuracy.
  32. 32. Data Warehouse vs Data MartDefinition:Data Warehouse• Large database that stores data that have been extracted from the various operational, external, and other databases of an organizationDefinition: Data Mart• Databases that hold subsets of data from a data warehouse that focus on specific aspects of a company, such as a department or a business process
  33. 33. Data Warehouse System
  34. 34. Basic Characteristics of Data WarehouseOrganized by business dimension or subject• Data are organized by subject (by customer, supplier, vendor, product, region etc) and contain information relevant for decision and data analysis.Consistent• Data in different databases may encoded differently. E.g, gender data may be encoded 0 and 1 in one operational system and or M and F in another. In data warehouse , it must be coded in a consistent mannerNonvolatile• Data are not updated after they are entered into the warehouse
  35. 35. Basic Characteristics of Data Warehouse (cont’)Historical• The data are kept for many years so that they can be used for trends, forecasting and comparison over time.Use online analytical processing• Online Analytical Processing (OLAP) involves the analysis of accumulated data by end users which are designed to support decision makers.Multidimensional Structure• Data warehouse use multidimensional data structure so it allow users to view and analyze data from the various perspective of the various business dimensions.
  36. 36. How a Data Warehouse &Data Mart Hold Data