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Problems with the Traditional File Environment

Problems with the Traditional File Environment (Continued) . Lack of data sharing and availability: Information cannot flow freely across different functional areas or different parts of the organization. Users find different values of the same piece of information in two different systems. Poor se

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Problems with the Traditional File Environment

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    2. Problems with the Traditional File Environment Data redundancy and inconsistency: the presences of duplicate data in multiple data files so that the same data are stored in more than one place or location Data inconsistency the same attribute may have different values Program data dependence: the coupling of data stored in files and the specific programs required to update and maintain those files Lack of flexibility: traditional file systems can deliver routine scheduled reports, but cannot deliver ad-hoc reports or respond to unanticipated requirements.

    3. Problems with the Traditional File Environment (Continued) Lack of data sharing and availability: Information cannot flow freely across different functional areas or different parts of the organization. Users find different values of the same piece of information in two different systems. Poor security: Because there is little control or management of data, management will have no knowledge of who is accessing or even making changes to the organizations data.

    4. Other Database Concepts Object-oriented database model Successor to the relational model Integration of data and programs Handles wider variety of field types Entity-relationship diagrams Graphical method of displaying relationships between tables Tool for IS professionals

    5. Types of Database Models Hierarchical Network Relational Object-oriented Extension of the relational model Stores both data and the procedures that act on the data Stores more complex types of information (graphics)

    7. Physical versus Logical Views In managing information, physical deals with the structure of information as it resides on various storage media. Logical deals with how knowledge workers view their information needs, and includes such terms as: CHARACTER - our smallest unit of information. FIELD - group of related characters. RECORD - group of related fields. FILE - group of related records. DATABASE - group of logically associated files. DATA WAREHOUSE - information from many databases.

    8. Other Logical Structures in a Database DATA DICTIONARY - contains the logical structure of information in a database. An INTEGRITY CONSTRAINT is a rule that helps assure the quality of the information in a database. A registration database at your school includes integrity constraints concerning prerequisites for certain classes. Designating primary keys, enforcing referential integrity, using input masks, and validation rules are ways to establish integrity constraints

    10. Components of a DBMS

    11. More Components of a DBMS DATA MANIPULATION SUBSYSTEM- helps you add, change, and delete information in a database and mine it for valuable information Tools in this subsystem include views, report generators, query languages (QBE and SQL) SQL is both a DML and DDL APPLICATION GENERATION SUBSYSTEM-contains facilities to help you develop transaction-intensive applications. Programming languages specific to a particular DBMS Interfaces to commonly used programming languages (e.g., COBOL or C++).

    12. More Components of a DBMS DATA ADMINISTRATION SUBSYSTEM-helps you manage the overall database environment by providing facilities for: Backup and recovery Security management

    13. Database Architectures- Centralized Centralized database use a single central processor or multiple processors in a client/server network. The major feature is that the database is in a single physical location. Advantages of this design are that security tends to be higher and risks are lower When data demands in terms of access are highly decentralized this design tends to be costly and inflexible

    14. Database Architectures- Distributed Databases can be decentralized either by partitioning or by replicating Partitioned database: Database is divided into segments or regions. For example, a customer database can be divided into Eastern customers and Western customers, and two separate databases maintained in the two regions. Duplicated database: The database is duplicated at two or more locations. The separate databases are synchronized in off hours on a batch basis.

    18. Data Warehouse Definition- a database with tools that stores current and historical data that is designed to support business analysis activities and decision-making tasks of managers; typically a relational database model is used Benefits improved access improved information isolation from operational systems tools permit advanced data analysis Users Data marts

    19. Comparison of Data in a Data Warehouse and Operational Data Operational Data Data is on many systems Current operational data Inconsistent data definitions Functionally organized data Data are constantly changing Warehouse Data Integrated in one enterprise-wide system Recent and historical data Consistent data definitions Data are organized around business entities Data are stabilized

    20. Building a Data Warehouse (ETL) Extraction phase create files on the computer that will store the data warehouse and move transaction data to this machine; data may come from many sources or parts of the organization Transformation phase cleanse and standardize the data. Why is this necessary? Load phase transfer the data from the transformation phase into the data warehouse The ETL process becomes automated to make regular transfers of transaction data into the data warehouse

    21. Data-Mining and Data-Mining Tools Data-mining is the process of selecting, exploring, and modeling large amounts of data to discover previously unknown relationships that support decision making. Traditional data mining tools answer questions about variables that we think are related Query languages (QBE or SQL) Report generators Multidimensional analysis tools (OLAP and pivot tables) Standard statistical procedures (regression, ANOVA) Knowledge discovery Data-mining tools look for relationships that are not discernable to the human eye (see next slide)

    22. Data-Mining

    23. Multidimensionality Multidimensional data analysis enables users to view data using various dimensions, measures and time frames OLAP dimensions: products, business units, country, industry (categories) measures: money, unit sales, head count, variances time: daily, weekly, monthly, quarterly, yearly) This type of analysis also provides the ability to view data in different ways (tables, charts, 3-D, geographically) OLAP tools provide for this Pivot tables in Excel or Access

    25. Examples of OLAP Tools Go to www.fedscope.opm.gov Under data cubes on entry page click on employment Demonstrate drill down and adding charts Data for this example comes from the Central Personnel Data File (CPDF) of the federal government The OLAP tool used to build this site is from a company named Cognos (PowerPlay) OLAP tools based on Excel http://wLCubed.com http://www.cubularity.com

    26. Databases and the Web Physical relationship of the hardware The role of middleware (conversion of HTML to SQL; conversion of query result back to HTML). Using the Web The browser is a virtual standard and easy to use The browser does not require training in a database query tool The use of the browser requires no change to the internal database; this enables firms to provide access to internal databases with little cost thus leveraging their investment in older systems.

    28. Management Opportunities and Challenges Effectively managing an organizations data resources is more than selecting a logical database design Ongoing commitment requiring discipline Requires organizational and conceptual changes Management commitment and understanding required Huge opportunities to improve performance by managing data better Obstacles Cost/benefit is difficult; costs are upfront and benefits are in the future

    29. Solutions Data administration function Data are the property of the organization Establish a group to administer data Data-planning and modeling methodology Enterprise planning for data using a common methodology Database technology, management, and users New software requires new personnel trained on the software Database administration Increased training for end users

    31. Spreadsheets Versus DBMS Linkage between elements spreadsheet - between cells in same table DBMS - between elements in different tables Orientation spreadsheet is toward calculations DBMS is tilted toward organization and linkage of data elements in different tables Capabilities DBMS has extensive querying and reporting power spreadsheet is limited Memory requirements entire spreadsheet table must be in memory not true for the database table

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