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O'Brien MIS_ 6th ed

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O'Brien MIS_ 6th ed Powered By Docstoc
					  MNG03218:
  Strategic Information Systems

Topic 5 : IT Infrastructure Management I -
       Data Resource Management
        Jun Xu, Southern Cross University
      Modified by Wutnipong Warakraisawad
    Learning Objectives
   Discuss the importance of quality
    improvement in the business decision-
    making
   Describe various types of databases
    used by businesses
   Explain data warehouse and data mining




                                             2
Some Common Characteristics of High-
Quality Information




                                       3
    Some Sources of Low-Quality
    Information
    Online customers intentionally enter
     inaccurate information to protect their privacy
    Information from different systems that have
     different information entry standards and
     formats
    Call center operators enter abbreviated or
     erroneous information by accident or to save
     time
    Third party and external information contains
     inconsistencies, inaccuracies, and errors
                                                       4
    Potential Business Effects Resulting from
    Low-Quality Information
   Inability to accurately track customers
   Difficulty identifying valuable customers
   Inability to identify selling opportunities
   Marketing to nonexistent customers
   Difficulty tracking revenue due to inaccurate
    invoices
   Inability to build strong customer
    relationships – which increases buyer power

                                                    5
   Traditional File Processing
      (Sources: Laudon & Laudon 2005, p. 236)




*In the past, different functional areas and groups had their
own files independently. Traditional file environment creates
problems such as data redundancy and inconsistency,
program-data dependence, Inflexibility, poor security, and lack
of data sharing availability.
                                                                  6
The Contemporary Database Environment
               (Sources: Laudon & Laudon 2005, p. 238)




                                                         7
        Database and DMS
   A database is a collection of data organized to service
    many applications efficiently by storing and managing
    data so that they appear to be in one location. It also
    minimizes redundant data.
   Database Technology provides a common platform for
    business system development using (normally) a common
    database
   A database management system (DBMS) is special
    software (i.e., Microsoft Access) that permits an
    organization to centralize data, manage them efficiently,
    and provide access to the stored data by application
    programs and is software through which users and
    application programs interact with a database.            8
Database Advantages from a Business
Perspective
   Increased flexibility
   Increased scalability and performance
   Reduced information redundancy
   Increased information integrity
    (quality)
   Increased information security




                                            9
Database Management Systems




                              10
          Various Database Structures
   Hierarchical
       Treelike & One-to-many relationship (must be specified in advance)
       Used for structured, routine types of transaction processing
       Complex, Not flexible , Difficult to modify and doesn’t support ad hoc
        requests well
   Network
       More complex & Many-to-many relationship (must be specified in
        advance)
       More flexible but doesn’t support ad hoc requests well
   Relational
       Data elements stored in simple tables
       Can link data elements from various tables
       Very supportive of ad hoc requests but slower at processing large
        amounts of data than hierarchical or network models
       Easier to maintain than hierarchical and network model               11
         Various Database Structures (continued)
   Multi-Dimensional
       A variation of the relational model. Cubes of data and cubes within
        cubes
       Popular for online analytical processing (OLAP) applications
        (manipulating and analyzing large volumes of data from multiple
        perspectives)
       Not currently developed for broad business application use.
   Object-oriented
       Key technology of multimedia web-based applications
       Good for complex, high-volume applications
       Not currently developed for broad business application use
       Relatively ease to use and support inheritance-new objects can be
        automatically by replicating some or all of the characteristics of one
        or more parent objects.
                                                                              12
         (Sources: Laudon & Laudon 2005, p. 243)


Hierarchical Database




                                                   13
       (Sources: Laudon & Laudon 2005, p. 243)


Network Database




                                                 14
                  (Laudon & Laudon 2003)

Relational Database




                                           15
                   (O’Brien & Marakas 2006, p. 159)

Object Oriented Database




                                                      16
Multidimensional Database




                            17
             Distributed Databases
                  (Sources: Laudon & Laudon 2005, p. 246)




*There are alternative ways to distributing a database. The
central database can be partitioned (a) so that each remote
processor has the necessary data to serve its own local needs.
The central database also can be replicated (b) at all remote
locations.
                                                              18
                 (Laudon & Laudon 2003)

A Hypermedia Database




                                          19
        Data Warehouses
   Data warehouse
      Stores data extracted from operational, external, or
       other databases of an organization
      Consolidate for management decision making

      Central source of ―structured‖ data

      May be subdivided into data marts-small data

       warehouses
   Benefits: improved information, easy access to data,
    improved decision-making capability, and help
    organizations refocus their business, etc.

                                                              20
       Database and Data Warehouse
   Data Warehouse is the extension of database
   Data warehouse is the main repository of an organization’s
    data
   Data in the data warehouse are processed (i.e., EFL)
    therefore is more integrated and consistent
   While the information in the database tends to be real-time,
    the information in the data warehouse can be updated
    regularly.
   While database focuses on automating the process of
    collecting and organizing information, data warehouse looks
    more at assisting managers in performing more advanced
    analysis and thus making better decisions (i.e., market
    prediction) via tools such as data mining, online analytical
    processing (OLAP), and other tools.
                                                              21
        Definition of Data Warehouse by William
        Inmon-the father of Data Warehouse
   Subject-oriented
       The data is organized so that all the data elements relating
        to the same real-world event or object are linked together;
   Time-variant
       The changes to the data are tracked and recorded so that
        reports can be produced showing changes over time;
   Non-volatile
       Data is never over-written or deleted - once committed, the
        data is static, read-only, but retained for future reporting;
        and
   Integrated
       It contains data from most or all of an organization's
        operational applications, and that this data is made
        consistent.                                                    22
          (Sources: Laudon & Laudon 2005, p. 250)


Components of a Data Warehouse




                                                    23
    Date Warehouse Fundamentals
   Extraction, transformation, and loading
    (ETL) – a process that extracts information
    from internal and external databases,
    transforms the information using a
    common set of enterprise definitions, and
    loads the information into a data
    warehouse
   Data mart – contains a subset of data
    warehouse information for specific
    functions/departments.
                                                  24
Data Warehouse Model   (Hagg et al. 2005)




                                            25
         Data Mining
   Data mining
       A major use of data warehouse databases
       Data is analyzed to reveal hidden correlations, patterns,
        and trends
       involves sifting through an immense amount of data to
        discover previously unknown patterns
   Data mining—searching for valuable business
    information in extremely large databases
   New business opportunities generated by
    conducting:
       Automated prediction of trends and behaviors
       Automated discovery of previously unknown patterns and
        relationships
                                                                    26
                               (Laudon & Laudon 2003; O’Brien 2006)


      On-line analytical processing (OLAP):
      Multidimensional Data Analysis

   Another important
    data analysis
    method
   Supports
    manipulation and
    real-time analysis of
    large volumes of
    data from multiple
    dimensions and
    perspectives
                                                                      27
    Integrating Information among Multiple
    Databases
   Forward integration – takes information entered into
    a given system and sends it automatically to all
    downstream systems and processes




                                                           28
    Integrating Information among Multiple
    Databases (2)
   Backward integration – takes information entered
    into a given system and sends it automatically to all
    upstream systems and processes




                                                            29
Integrating Information among Multiple
Databases (3)
   Building a central repository specifically for integrated
    information (i.e., customer information)




                                                            30
              (Sources: Laudon & Laudon 2005, p. 254)

Linking Internal Databases to the Web




                                                        31
        Management Challenges in managing
        data resources (Laudon & Laudon 2005)
   Organizational obstacles to a database
    environment
       Change in information & Information management-
        Power shift
       Make the requisite organizational change for the whole
        organization’s interest in data
   Cost/ benefits considerations
       Designing an enterprise wide database can be a
        lengthy and costly process
       Short-time upfront investment VS often intangible,
        back loaded and long-term (i.e., five years) benefits
                                                                32
    Some Suggestions for developing advanced
    business intelligence (BI) capabilities
    (See Reading 5.1)

   Establish an organizational climate where
    business professionals are expected to be in
    command of their information destiny
   Encourage rich social networking across the
    organization
   Establish a distributed support infrastructure
    that emphasizes local ―first-responders‖.



                                                     33
Topic 5 Case: Search for Revenue-Google
(pg 788-791 of Textbook)




                                      34

				
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