CHAPTER 5 MANAGING ORGANIZATIONAL DATA AND INFORMATION

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CHAPTER 5MANAGING ORGANIZATIONAL DATA AND
INFORMATION
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Learning Objectives

• Discuss traditional data file organization and
  its problems
• Explain how a database approach overcomes the
  problems associated with traditional file
  environment, and discuss the advantages of the
  database approach
• Describe how the three most common data models
  organize data, and the advantages and
  disadvantages of each model
• Describe how a multidimensional data model
  organizes data
• Distinguish between a data warehouse and a data
  mart
• Discuss the similarities and difference between
  data mining and text mining

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Chapter Overview
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Case FedEx Pinpoints Profitable Customers

• The Problem
• customers are classified as good , bad, or ugly
  by the cost of doing business with them and the
  profits they return

• keep the good customers, improve the bad
  customers, and drop the ugly ones
• easy to identify customers who spend money with
  them but difficult to identify customers who are
  profitable for them

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Case (continued)

• The Solution

• use a data warehouse, stocked with customer data,
  that allows the company to compare the complex
  mix of marketing and servicing costs that go into
  retaining each individual customer versus the
  revenues he, she, or it might bring in

• The Results

• good customers - expect a phone call if their
  shipping volumes falter, which can prevent
  defections before they occur
• bad customers can be turned into profitable
  customers by charging higher shipping rates
• ugly customers can be ignored

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Case (continued)

• What have we learned from this case??

• Customized strategies can be developed to cut
  costs, transform the marginal customer into a
  profitable customer, and permit more profitable
  pricing structures
• Other types of data can give an organization
  important feedback about its products, services,
  markets, and coming trends

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Basics of Data Arrangementand Access

• The Data Hierarchy
• Field - a logical grouping of characters into a
  word, a small group of words, or a complete
  number
• Record - a logical grouping of related fields
• File - a logical grouping of related records
• Database - a logical grouping of related files
• Entity - a person, place, thing, or event about
  which information is maintained
• Attribute - each characteristic or quality
  describing a particular entity
• Primary Key - field that uniquely identifies the
  record
• Secondary Key - field that has some identifying
  information, but typically does not identify the
  file with complete accuracy

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Basics of Data Arrangementand Access (continued
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• Storing and Accessing Records
• Indexed Sequential Access Method (ISAM)
• uses an index of key fields to locate individual
  records
• index - lists the key field of each record and
  where that record is physically located in
  storage
• track index - shows the highest value of the key
  field that can be found on a specific track
• Direct File Access Method
• uses the key field to locate the physical address
  of a record
• transform algorithm - translates the key field
  directly into the records storage location on
  disk

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Traditional File Environment
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Introduction to Information Technology Turban,
Rainer and Potter Chapter 4 Computer Software

• The organization has multiple applications with
  related data files

Each application has a specific data file related
to it, containing all the data records needed by
the application
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Traditional File Environment (continued )

• Problems with the file approach
• data redundancy - the same piece of information
  could be duplicated in several places
• data inconsistency - the various copies of the
  data no longer agree
• data isolation - difficulty in accessing data
  from different applications
• security - new applications may be added to the
  system on an ad hoc basis
• data integrity - data values must often meet
  integrity constraints
• application/data independence - the applications
  and data in computer systems should be independent

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Database The Modern Approach

• Database Management System
• provides access to all the data
• Example University administration

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Database The Modern Approach (continued )

• Locating Data in Databases
• Centralized database
• all the related files are in one physical
  location
• used on large, mainframe computers
• saves the expenses associated with multiple
  computers
• provides database administrators with the ability
  to work on a database as a whole at one location
• files are not accessible except via the
  centralized host computer
• recovery from disasters can be more easily
  accomplished at a central location
• vulnerable to a single pint of failure
• speed problem

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Database The ModernApproach (continued )

• Locating Data in Databases (cont)
• Distributed database
• complete copies of a database, or portions of a
  database, are in more than one location, which is
  usually close to the user
• replicated database - complete copies of the
  entire database are delivered to many locations,
  primarily to alleviate the single-point-of-failure
  problems of a centralized database as well as to
  increase user access responsiveness
• partitioned databases - these are subdivided, a
  portion of the entire database in each location

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Centralized vs. Distributed Databases
User New York
User Los Angeles
Centralized Database
Distributed Database
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Database The ModernApproach (continued )

• Creating a Database
• Conceptual design - an abstract model of the
  database from the user or business perspective
• Physical design - shows the way a database is
  actually arranged with a storage devices
• Entity-relationship (ER) modeling
• process of planning the database design
• ER diagram - document of the conceptual data
  model
• Entity classes ? Instance ? Identifiers ?
  Relationships
• Normalization
• method for analyzing and reducing a relational
  database to its most streamlined form for minimum
  redundancy, maximum data integrity, and best
  processing performance

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Database Management Systems

• A software program (or group of programs) that
  provides access to a databases
• Permits an organization to store data in one
  location, from which it can be updated and
  retrieved
• Provides access to the stored data by various
  application programs
• Provides mechanisms for maintaining the integrity
  of stored information, managing security and user
  access, recovering information when the system
  fails, and accessing various database functions
  form within an application written in a
  third-generation, fourth-generation, or
  object-oriented language

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DBMS (continued )

• Logical versus Physical View
• Physical view - deals with the actual, physical
  arrangement and location of data in the direct
  access storage devices (DASD)
• Logical view - represents data in a format that
  is meaningful to a user and to the software
  programs that process that data

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DBMS (continued )

• DBMS Components
• Data model
• defines the way data are conceptually structured
• Data definition language (DDL)
• defines what types of information are in the
  database and how they will be structured
• functions of the DDL
• provide a means for associating related data
• indicate the unique identifiers (or keys) of the
  records
• set up security access and change restrictions

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DBMS (continued )

• DBMS Components (cont)
• Data manipulation language (DML)
• used with third-generation, fourth-generation, or
  object-oriented languages to query the contents
  of the database, store or update information in
  the database, and develop database applications
• Structured query language (SQL) - most popular
  relational database language, combining both DML
  and DDL features
• Data Dictionary
• stores definitions of data elements and data
  characteristics

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Logical Data Models

• A managers ability to use a database is highly
  dependent on how the database is structured
  logically and physically.
• In a logically structuring database, businesses
  need to consider the characteristics of the data
  and how the data will be accessed.
• Three common data models hierarchical, network,
  and relational
• Using these models, database designer can build
  logical or conceptual view of data that can then
  be physically implemented into virtually any
  database with any DBMS.

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Logical Data Models (continued )

• Hierarchical Database Model
• structures data into an inverted tree in which
  each record contains two elements rigidly

1st a single root or master field, often called
a key, which identifies the type
location or ordering of the records
2nd a variable number of subordinate fields,
which defines the rest of the data within a
record

• all fields have only one parent, each parent
  may have many children
• advantage speed and efficiency
• problem access to data is predefined before the
  programs and each relationship must be
  explicitly defined when the database is created

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Hierarchical Data Model
Sales
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Logical Data Models (continued )

• Network Database Model
• creates relationship among data through a
  linked-list structure in which subordinate
  records (members) can be linked to more than one
  data element (owner)
• pointer - explicit link, storage addresses that
  contain the location of a related record
• many-to-many relationships are possible
• complexity for every set of linked data
  elements, a pair of pointers must be maintained

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Logical Data Models (continued )

• Relational Database Model
• based on a simple concept of tables in order to
  capitalize on characteristics of rows and columns
  of data
• relations - tables ? tuple - row ?
  attribute - column
• select operation - creates a subset consisting of
  all records in the file that meet stated criteria
• join operation - combines relational tables to
  provide the user with more information than is
  available in individual tables
• project operation - creates a subset consisting
  of columns in a table, permitting the user to
  create new tables that contain only the
  information required

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Relational Database Model
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Company Data Models
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Logical Data Models (continued )

• Emerging Data Models
• Object-oriented database model - an object - a
  small amount of data put together with all the
  data needed in order to perform an operation with
  that data
• Object - similar to an entity in that it
  represents a person, place, or thing, but it also
  contains all of the data that the object needs in
  order to perform an operation
• Attributes - characteristics that describe the
  state of that object
• Method - an operation, action, or a behavior the
  object may undergo
• Messages - from other objects activate operations
  contained within the object
• Class - all the messages to which the object will
  respond, as well as the way in which objects of
  this class are implemented

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Logical Data Models (continued )

• Emerging Data Models (cont)
• Object-relational database model - adds new
  object storage capabilities to relational
  database management systems
• Hypermedia database model - stores chunks of
  information in a form of nodes connected by links
  established by the user
• Other Database Models
• Geographical information database - contains
  locational data for overlaying on maps or images
• Knowledge database- stores decision rules used to
  evaluate situations and help users make decisions
  like an experts
• Multimedia database - stores data on many media
  sounds, video, images, graphics animation, and
  text.

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Data Warehouses

• A data warehouse is a relational and or
  multidimensional database management system
  designed to support management decision making.
• The data in the warehouse is stored in a
  single, agreed-upon format even when underlying
  operational databases store the data differently.

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Data WarehousesFramework and View
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Data Warehouses (continued ...)

• Data Warehouse Offers Many Business Advantages
• It provides business users with a
  customer-centric view of the companys
  heterogeneous data by helping to integrate data
  from sales, service, manufacturing and
  distribution, and other customer-related business
  systems.
• It provides added value to the companys
  customers by allowing them to access better
  information when data warehouse is coupled with
  Internet technology.
• It consolidates data about individual customers
  and provides a repository of all customer
  contacts for segmentation modeling, customer
  retention planning, and cross-sales analysis.

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Data Warehouses (continued ...)

• Data Warehouse Advantages (cont)
• It removes barriers among functional areas by
  offering a way to reconcile views from multiple
  sources, thus providing a look at activities that
  cross functional lines.
• It reports on trends across multidivisional
  and/or multinational operating units, including
  trends or relationships in areas such as
  merchandising, production planning, and so forth.

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Data Warehouses (continued ...)

• Multidimensional Database Model
• can be the core of data warehouses
• data are stored in arrays
• consists of at least three dimensions
• dimensions are the edges of the cube, and
  represent the primary views of the business
  data
• the data are intimately related and can be viewed
  and analyzed from different perspectives, which
  are called dimensions
• allows for the effective, efficient, and
  convenient storage and retrieval of large volumes
  of data

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Data Warehouses (continued ...)

• Data Marts
• a scaled-down version of a data warehouse that
  focuses on a particular subject area
• usually designed to support the unique business
  requirements of a specific department or business
  process. Example Marketing data mart
• takes less time to build, costs less, and less
  complex
• the indiscriminate introduction of multiple data
  marts with no linkage to each other, or to an
  enterprise data warehouse, will cause problems

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Data Warehouses (continued ...)

• Data Mining
• provides a means of extracting previously
  unknown, predictive information from the base of
  accessible data in data warehouses
• discovers hidden patterns, correlations, and
  relationships among organizational data
• predicts future trends and behaviors, allowing
  businesses to make proactive, knowledge-driven
  decisions
• functions of data mining
• classification clustering association
• sequencing forecasting

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Whats in IT for Me?

• For Accounting
• Data gathered about each transaction (business
  event) in the organization is stored in its
  databases
• For Finance
• Computerized databases external to the
  organization, such as CompuStat or Dow Jones,
  provides financial data on organizations in its
  industry

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Whats in IT for Me? (continued )

• For Marketing
• Databases including customer name, address,
  purchase, amount, etc, help to plan targeted
  marketing campaigns and to evaluate the success
  of previous campaigns.
• Data mining is critical for many marketing
  efforts to remain competitive.
• For Production/Operations Management
• Organizational databases are accessed for
  determining optimum inventory levels for parts in
  a production process
• Information in databases are used to know when to
  perform required service on machines

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Whats in IT for Me? (continued )

• For Human Resources Management
• Organizational databases contain extensive data
  on employees, such as name, address, gender,
  race, age, salary, hiring date, current job
  descriptions, past job descriptions, and past
  performance evaluations
• For MIS
• Vacancies for MIS include data entry and data
  storage management to database management and
  data analyst