Job Description for Head Strategic Business Unit Sbu Head - PowerPoint

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					           Chapter 4

Data and Knowledge Management

            Chapter 4       1
           Learning Objectives
1. Understand information system concepts
    including input, processing, and output,
    data and information.
2. Understand information system components
    including hardware, software, databases
    and telecommunications.
10. Be able to create a database using a
    relational DBMS such as Access.
    Demonstrate the ability to create reports,
    queries and join two tables in the DBMS 2
                      Chapter 4
Chapter 4   3
                 Why Databases?

 Difficulties of Managing Data.
     Data is a company’s most valuable resource.
     Amount of data increases exponentially.
     Data are scattered and collected by many
      individuals using various methods and devices.
     Data come from many sources including internal
      sources, personal sources and external sources.
     Data security, quality and integrity are critical.

                         Chapter 4                         4
                Data Hierarchy

 Bit (a binary digit): a circuit that is either on
  or off.
 Byte: group of 8 bits, represents a single
 Field: name, number, or characters that
  describe an aspect of a business object or

                       Chapter 4                      5
     Data Hierarchy (Continued)

 Record: collection of related data
 File (or table): collection of related
 Database: a collection of integrated and
  related files.

                  Chapter 4              6
Chapter 4   7
                 Before Databases:
              File Processing Systems

Traditional File Processing:
 Data are organized, stored,
   and processed in
   independent files of data

                                Chapter 4   8
           Problems of File Processing
 Data Redundancy – duplicate data requires an update
  to be made to all files storing that data
      Example – what if the same customer has a checking
       account, savings account, and loan?

 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
                              Chapter 4                     9
Why Data Redundancy is Bad

                      Savings, and
                      Loan files will
                      have customer
                      address, etc.

                      If address
                      changes in one
                      file and not the
                      others, you

          Chapter 4                 10
      Solution: Database Approach

 Database management system (DBMS) provides all users
  with access to all the data.
 DBMSs minimizes the following problems:
    Data redundancy: the same data stored in many places.

    Data isolation: applications cannot access data

     associated with other applications.
    Data inconsistency: various copies of the data do not


                          Chapter 4                          11
               Database Management

 DBMS = A software system that is used to create, maintain, and provide
  controlled and secure access to data
                                Chapter 4                              12
    Another example of a
Database Management System

            Chapter 4        13
      Database Approach (Continued)

 DBMSs maximize the following issues:
     Data security.
     Data integrity: data meets certain constraints, no
      alphabetic characters in zip code field.
     Data independence: applications and data are
      independent of one another, all applications are
      able to access the same data.

                         Chapter 4                     14
             Designing the Database

 Data model. Diagram that represents the entities in
  the database and their relationships.
      Entity is a person, place, thing or event.
      Attribute is a characteristic or quality of a particular
      Primary key is a field that uniquely identifies that
      Secondary keys are fields that have identifying
       information but may not identify with complete accuracy.

                            Chapter 4                        15
Data Modeling:
 Process where the relationships between data elements are
 Standard approach is to create an Entity-Relationship (ER)

                         Chapter 4                         16
      Entity-Relationship Modeling

 Database designers plan the database design in a process
  called entity-relationship (ER) modeling.
 ER diagrams consists of entities, attributes and
 Entity classes are a group of entities of a given type, e.g.
 Instance is the representation of a particular entity, i.e.
  STUDENT(John Smith, 123-45-6789, …).
 Attributes are fields that contain specific data about an
  entity instance.
 Identifiers (primary keys) are attributes unique to that entity
  instance, i.e. StudentIDNumber.

                            Chapter 4                          17

                Cardinalities of relationships:
                         1 – to – 1
                         1 – to – many
                         many – to - many
    Chapter 4                                     18
         fields that contain specific data about entities

     Primary key:
             the attribute that uniquely identifies an entity

                            Chapter 4                           19
One customer
may place many
orders, but each
order is placed by
a single customer
 One-to-many

     Chapter 4       20
One order has
many order lines;
each order line is
associated with a
single order
 One-to-many

       Chapter 4     21
One product can
be in many
order lines, each
order line refers
to a single
 One-to-many

        Chapter 4   22
Therefore, one
order involves
many products
and one product is
involved in many

 Many-to-many

    Chapter 4        23
ER Diagram for Product Inventory
     and Ordering System

            Chapter 4          24
ER Diagram of Employee Database

JobTypes   1|N   1|N

                       Employees           M|N            Projects

                                      Entities in boxes
Departments      1|N
                                      Relationships in

                          Chapter 4                                  25
    Entities in Employee Database

 Employees - data about people in the
 Departments - data about the organizational
 JobTypes - data about the work
 Projects - data about the current projects
                    Chapter 4                   26
          Relationships in Employee
 Each department has many employees, but each
  employee works for only one department (1:N)
 There are many employees of a given job type, but each
  employee has only one job title (1:N)
 An employee who is a manager has many employees
  under her, but each employee has only one manager to
  report to (1:N)
 Each employee can be working on several projects, and
  each project may have several employees working on it
                          Chapter 4                   27
Chapter 4   28
        What is a relational database?
 A database in which:
     entity classes are represented by TABLES (also
      called RELATIONS),
     specific entity instances are represented by ROWS
      in the TABLE (records)
     attributes are represented by COLUMNS in the
      TABLE (fields)
     relationships between entities are represented by
      associations between primary and foreign keys
      (identifier fields)
                           Chapter 4                      29
  A Table in Microsoft Access

Popular examples of relational databases
are Microsoft Access and Oracle.

                   Chapter 4                30
 Relationships View of the tables in
the Employee Database (MS Access)


                        8               M:N relationships require an
                                        additional table, called an
                                        intersection (or junction) table.
       The relationships are            The M:N relationship between
       implemented via associations     Employees and Projects is
       between primary keys             implemented via the
       (shown here in boldface) and     EmployeeProject intersection
       foreign keys of tables

                            Chapter 4                                       31
      Design View of Employees Table
               (MS Access)

                              Each field
                              is of a
specify the
                              data type

                 Chapter 4                 32
         Design View of Employees Table
                  (MS Access)

                                 You can
                                 like default
                                 values and
                                 for a field

                    Chapter 4                   33
                    1:N Relationship Between
                   Departments and Employees

The DepartmentID field of the Employees table is a
foreign key. It references the DepartmentID field of
the Departments table (primary key). In this way, we
can see that Sam Smith, Mike Mitri, Alice Friedman,
and Brendan Mitri are all in the Payroll department.
(DepartmentID = 2).
A department has several employees, but each
employee is in only one department.
                                                 Chapter 4   34
M:N relationship between
Employees and Projects

     The EmployeeProject table is an intersection table that implements the M:N
     relationship. The EmployeeID field of the EmployeeProject table is a foreign key
     that references the EmployeeID field of the Employees table. Likewise for the
     ProjectID fields. Here we see that James Smith is one of the four employees who
     works on Accounts Payable project. James Smith also works on the Accounts
     Receivable project.

     Each employee can have several projects and each project can have several
                     Chapter 4                                                 35
      Normalization for Well-Structured

 Normalization is a method for analyzing and
  reducing a relational database to its most
  streamlined form for:
     Mimimum redunancy;
     Maximum data integrity;
     Best processing performance.
 Normalized data is when attributes in the
  table depend only on the primary key.

                       Chapter 4              36
Non-Normalized Table

       Chapter 4       37
After Normalization

      Chapter 4       38
Relationships between Tables
  in Normalized Database

          Chapter 4            39
A database query is a request view certain,
selected data from a database.

                  Chapter 4                   40
             Query Languages

 Structured query language (SQL) is the
  most popular query language used to request
 Query by example (QBE) is a graphical
  grid or template that a user fills out to
  construct a sample or description of the data

                     Chapter 4                41
               The SELECT Statement
 Used for queries on tables in a relational database
 Parts of the SELECT statement:
      SELECT
          List the columns (and expressions) that should be returned from the query
      FROM
          Indicate the table(s) or view(s) from which data will be obtained
      WHERE
          Indicate the conditions under which a row will be included in the result
      GROUP BY
          Indicate categorization of results
      HAVING
          Indicate the conditions under which a category (group) will be included
      ORDER BY
          Sorts the result according to specified criteria

                                           Chapter 4                                   42
Suppose we have these tables

          Chapter 4            43
    Query Example #1
Get Names of Employees in
    Alphabetical Order


          Chapter 4                   44
   Query Example #2
Get Names and Salaries of
   Programmers Only


         Chapter 4             45
          Query Example #3
      Get Summary Information
(average salaries of different job titles)


                   Chapter 4               46
          Query Example #4
Get Related Data from Multiple Tables
 (This kind of query is called a JOIN)



                    Chapter 4              47
Chapter 4   48
Data Life Cycle

    Chapter 4     49
              Data Warehousing

 Data warehouse is a repository of historical
  data organized by subject to support decision
  makers in the organization and include:
     Online analytical processing which involves
      the analysis of accumulated data by end users;
     Multidimensional data structure which allows
      data to be represented in a three-dimensional
      matrix (or data cube).

                       Chapter 4                   50
    Benefits of Data Warehousing

 End users can access data quickly and easily
  via Web browsers because they are located in
  one place.
 End users can conduct extensive analysis
  with data in ways that may not have been
  possible before.
 End users have a consolidated view of
  organizational data.

                    Chapter 4                51
        Data Marts & Data Mining

 Data mart is a small data warehouse,
  designed for the end-user needs in a strategic
  business unit (SBU) or a department.
 Data mining involves searching for valuable
  business information in a large database, data
  warehouse, or data mart.
     Used to predict trends and behaviors.
     Identify previously unknown patterns.

                        Chapter 4              52
Data Warehouse Framework and

          Chapter 4            53
Relational Database

      Chapter 4       54
Converted to Multidimensional

           Chapter 4            55
Chapter 4   56
     Online Analytical Processing
 Enables mangers and analysts to interactively
  examine and manipulate large amounts of
  detailed and consolidated data from many

                    Chapter 4                 57
          Analytical Operations

 Consolidation – aggregation of data

 Drill-down – detail data that comprise
  consolidated data

 Slice and Dice – ability to look at the
  database from different viewpoints
                     Chapter 4              58
               OLAP Technology

          Online Demo of OLAP Technology

                        Chapter 4                       59
databases can
be visualized as
dimension refers
to possible
values of

                   Chapter 4   60
        OLAP can be Done in Excel:
 Three dimensions (page, row, column)
      This enables slice-and-dice, allowing user to view any
       combination of variables for each dimension
 Summary information can be viewed for any
  combination of variables
      This enables consolidation (aggregation of data)
 For any given piece of summary information, user
  can get details
      This enables drill-down, obtaining details of aggregate
                             Chapter 4                           61
                      Data Mining

 Analyzing the data in a data warehouse or data mart to reveal
  hidden patterns and trends in historical business activity

                           Chapter 4                         62
              Data Mining Uses
 Perform “market-basket analysis” to identify new
  product bundles.

 Find root causes to quality or manufacturing

 Prevent customer attrition and acquire new

 Cross-sell to existing customers.

 Profile customers with more accuracy.
                       Chapter 4                     63
          Data Mining Applications

 Retailing and sales. Predict sales, prevent theft and fraud,
  determine correct inventory levels and distribution
 Banking. Forecast levels of bad loans, fraudulent credit card
  use, predict credit card spending by new customers, etc.
 Manufacturing and production. Predict machinery
  failures, find key factors to help optimize manufacturing
 Insurance. Forecast claim amounts, medical coverage costs,
  predict which customers will buy new insurance policies.

                           Chapter 4                         64
          Data Mining Applications
 Policework. Track crime patterns, locations,
  criminal behavior; identify attributes to assist in
  solving criminal cases.
 Health care. Correlate demographics of patients
  with critical illnesses, develop better insight to
  identify and treat symptoms and their causes.
 Marketing. Classify customer demographics to
  predict how customers will respond to mailing or
  buy a particular product.

                        Chapter 4                       65
Chapter 4   66
      Data Visualization Systems

 Graphically represent complex data using
  interactive three-dimensional forms such as
  charts, graphs, and maps

 Data visualization tools help users to
  interactively sort, subdivide, combine, and
  organize data while it is in its graphical form.

                      Chapter 4                  67
Chapter 4   68
Chapter 4   69
Chapter 4   70
       Data Visualization Technologies

 Geographic Information Systems (GIS) is a
  computer-based system for capturing, integrating,
  manipulating and displaying data using digitized
  maps. (example: GoogleMaps)
      Find locations for new restaurants.
      Emerging GIS applications integrated with global
       positioning systems (GPSs).
 Virtual Reality is interactive, computer-generated,
  three-dimensional graphics delivered to the user
  through a head-mounted display.

                           Chapter 4                      71
Virtual Reality

    Chapter 4     72
Chapter 4   73
Chapter 4   74
         Knowledge Management

 Knowledge management (KM) is a process that
  helps organizations manipulate important
  knowledge that is part of the organization’s
  memory, usually in an unstructured format.
 Knowledge is information that is contextual,
  relevant and actionable; information in action.
 Intellectual capital (or intellectual assets) is
  another term often used for knowledge.

                       Chapter 4                     75
             Knowledge Management
 Explicit knowledge deals with more objective, rational and
  technical knowledge.
 Tacit knowledge is the cumulative store of subjective or
  experiential learning.
 Knowledge management systems (KMSs) use modern
  information technologies – Internet, intranets, extranets, data
  warehouses - to systemize, enhance and expedite intrafirm
  and interfirm knowledge management.
 Best practices are the most effective and efficient ways of
  doing things, readily available to a wide range of employees.

                            Chapter 4                          76
Knowledge Management System

            Chapter 4         77
       Knowledge Management System

   Create knowledge. Determine new ways.
   Capture knowledge. Identify as valuable.
   Refine knowledge. Make it actionable.
   Store knowledge. Store in a reasonable format.
   Manage knowledge. Verify it is relevant, accurate.
   Disseminate knowledge. Made available.

                         Chapter 4                       78

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