Selling an Idea or a Product by 9S760l


									Chapter 1: Data, Information,
and Decision

 Instructor: Paul K Chen
   Data, Information, and Decision

   Information System Categories

   System Analysis and Design: What is it?

   Roles of System Analysts

   System Development Life Cycle – A brief

   CASE Tools: What, Why? Categories

   Reengineering: What and Types

   Object-Oriented Analysis and Design –To be
    discussed in Chapter 22

   Decision Support System –Data Warehousing
      Information as A Competitive
Information technology and quality information are not
the goals, but merely to support organizations to reach
goals of

   Superior products and services

   Greater productivity

   Eventually success
       Data, Information, and Decision
   Data                              Data Resource Management

   Information (Data + Process)      MIS (OLTP) & OOAD

   Knowledge                         KM (Knowledge Mgt), KWS
                                       (Knowledge Work Systems)

   Decision (Information +           DSS; ESS, EIS (Executive Level)
             Knowledge)                GDSS, CSCW

                                      Data Warehousing/Data
   Data/Information/Decision          Mart/Data Mining/OLAP
                                       (Executive, Collaborative and
      Data, Information, and Decision
   Data                   Data processing
     + Processing           System Analysis/Design
   Information             MIS, Database Systems
   Object (Data+Processing) Object-Oriented SD/DA

   Knowledge                   Artificial Intelligence
    + Information                Expert system
   Decision (executive level) DSS, EIS
   Decision (all levels, sophisticated) Data warehousing
                                           Data Mining
     DRM (Data Resource Management)

 Definition

  Data resource management (DRM) is the business
  discipline which focuses on how data can be managed
  to most efficiently support the business enterprise.
  DRM addresses the management of all enterprise data.
  When combined with other enterprise processes, DRM
  provides information when needed, where needed, in
  the form needed, with desired accuracy and at
  minimum cost for business enterprise.
      DRM: Why?

Data resource management becomes increasingly critical
to the success of the corporation in the marketplace due to
these new realities:

   The competitive, global environment that business is
   Explosive growth of the web over the internet
   Increasing use of data warehouse systems to make
    better decisions
   Business intelligence dependent on reliable information
      DRM: What?

   Providing a unified and integrated approach for
    planning, control and integration of our data assets in
    support of enterprise’s business

   Encouraging the reduction of unnecessary data

   Encouraging the reuse and sharing of high quality data

    Done right, the investment can be paid back many
    times over.
      DRM Approaches-How

   Understanding data structure via data modeling: A
    comprehensive data resource model is mandatory to
    properly manage and design the data resource.
   Deploying strategies for managing data server
   Standardizing the use of tools and procedures
   Designating data stewardship
      XML (Extensible Markup
      Language)for Data Management
   Quickly becoming de facto standard for the sharing of
    information in the e-business arena.

   Proven itself extremely versatile and highly qualified
    for data exchange, interoperability, and integration.

   Enabling legacy data from relational databases and
    other files to be migrated into future applications.

   Integrating the structured data with unstructured data
    in text documents, reports, email, graphics and images,
    audio and video files to present the new applications.
     Information System Categories

   TPS (Transaction process systems)

   OAS (Office automation systems)

   KWS (Knowledge work systems)

   MIS (Management information systems)

   DSS (Decision support systems)
      Information System Categories

   ESS (Executive support systems)

   GDSS (Group decision support systems)

   CSCW (Computer supported collaborative systems)

   Data Warehousing, Data Mart, Data Mining(OLAP
    Online Analytical Processing)
      Information System Categories
 CRM     (Customer Relationship Management)

   ERP (Enterprise Resource Planning)

   SCM (Supply Chain Management)

   EAI ( Enterprise Application Integration)
      System Analysis and
      Design: What is it?
   System Analysis and Design is a systematic approach to
    identifying problems, opportunities, and objectives,
    analyzing the information flows in organizations;
    designing computerized information systems to solve a
      Roles of System Analysts

   Systems analysts act as outside consultants to business,
    as supporting experts within a business,
    and as change agents.

   Analysts are problem solvers, and require
    communication skills

   It’s important for analysts to be aware of their ethical
    framework as they work to build relationships with
    users and customers.
      System Development Life
      Cycle – A brief overview
It is a systematic approach to solving business
problem. It’s divided into seven phases:

   Identifying problems, opportunities, and objectives
   Determining system requirements
   Analyzing system needs
   Designing the recommended systems
   Developing and documenting software
   Testing and maintaining the system
   Implementing and evaluating the systems
    System Development Life
    Cycle – A brief overview
Why should a system development project be
segmented in phases?

 Project Management– easier to understand and
  manage its deliverables and track its progress
 Resources – Better utilize the resources related to
  technology, skills, and time
 Risk –Minimize commitment and cost in case the
  project restarts.
      CASE Tools: What, Why? And


   CASE (Computer-aided Software Engineering)

CASE is not just a technology or class of products but a
problem-solving approach, a set of methods and
disciplines, maybe even a philosophy that guides software
development toward a real engineering discipline.
    CASE Tools: What, Why? And


 To improve analyst productivity
 To facilitate communication among users and
 To provide continuity between life cycle phases
 To assess the impact of maintenance
      CASE Tools: What, Why?
      And Categories
CASE tools categorized relative to project lifecycle

   Front-end products (Upper CASE): focus on the
    strategic planning, analysis and logical design

   Back-end products (Lower CASE): emphasize
    physical design and construction
   3 Rs of Software Engineering
What? Take a guess.
      3 Rs of Software Engineering
 Reusability

   Re-engineering

   Reverse-engineering
      Reusability: What?
   When we speak of reuse in software engineering, we
    mean everything that can be reused at a later time.
    This includes all the information and knowledge that
    has been developed, system architectures and
    development methods.

   Normally we talk about reuse, we focus on component.
    A component is a standard building unit in an
    organization that is used to develop applications.
     Reusability: What?
Components are characterized by:
 A high quality product due to careful design and
 Not bond to any specific application
 Packages for reuse with a well-designed interface,
  documentation, etc.
 General so that it can be used in several places.

Components such as: Use cases, classes, framework,
subsystems, interfaces.
    Reengineering: What? and
 Business  reengineering is the fundamental
  rethinking and radical redesign of business
  processes and product to achieve dramatic
  improvements in critical measure of
  performance, such as cost, quality, capital,
  services and so on.
      Reengineering: What? and
 Product   Reengineering

   Process Reengineering
    Total quality management
    Just-in-time mfg
    E-business and E-Commerce
    Data Warehousing, OLAP, Data Mining
      Ways to Improve Process
 People   – Teams, Experience

   Tools – Testing and Development Tools

   Techniques – Modeling; Prototyping

   Physical Environment – Workflow, Procedures
      Decision Support System –
      Data Warehousing

1.      A central database that is loaded
        multiple operational databases for the
        purpose of end-user access and
      What is a Data Warehouse? -
2.     A data warehouse differs from an
       operational system in that the data it
       contains is normally static and
       in a scheduled manner through
       loading procedures.
   What is a Data Warehouse? -
3. A data warehouse is developed to
   accommodate random, ad hoc queries
   and to allow users to ‘drill down’ to
   minute levels of detail.

Bill Inmon defines a central data warehouse
  as a
database that is:

1. Subject Oriented
   Data naturally congregates around major
  categories within any corporation. These
  categories are called subject areas. For
  example, subject areas are bill of material,
  customer, product, and criminal profile. The
  subject area will be designed to contain only
    Definition - Continued

2. Integrated
   Data integration is displayed by consistence
  in the measurement of variables, naming
  conventions, physical data definitions
  across the data. There will be only one
  definition, identifier, etc., for each subject
    Definition - Continued

3. Time Variant
  Data in the DW is historical and accurate as
  of some point in time. Since DW data is
  extracted from operational systems, it must
  have an element of time as part of its key
    Definition - Continued

4. Static
  Since the data in DW is a snap shot extracted
  from operational system, it must be static or
   The Benefits of Data
 Enable  workers to make better and wiser
  A data warehouse is specifically developed to
 allow users the ability to explore data in an
 unlimited number of ways, accommodating
 essentially any query a manager could dream
 up and providing access to the data sources
 that are behind the results. For example,
 information gleaned from a data warehouse
 can change pricing information.
    The Benefits of Data
 Identifyhidden business opportunities
 A data warehouse performs a second, and
 very valuable function by searching data for
 trends and abnormalities which users may
 not know to look for.

   For example: Assisting companies in spotting
  sales trends, and detecting erroneous or
  fraudulent billings.
   The Benefits of Data
 Bending  with the customer
 A data warehouse can help companies by
 really understanding who their customers are
 and what services they are using.

  For example, by collecting and analyzing
 internet portal click stream data, companies
 are able to build extensive user profiles to
 boost profits through sales channel.
   The Benefits of Data
 Precision Marketing
 A data warehouse can aid in detecting
 segments of the marketplace (geographically
 and demographically) which remain
 untapped, and help show the best way to
 reach out to these potential customers (rapid
 response to market and technology trends).
OLTP vs. Data Warehousing
       Typical Data Warehouse Queries- A
       National Real Estate Agent Case
   Which type of property sells for prices above the average selling
    price for properties in the main cities of USA and how does this
    correlate to demographic data?

   What are the three most popular areas in each city for renting
    property in 1997 and how does this compare with the figures for
    the previous two years?

   What is the current monthly revenue for property sales at each
    branch office, compared with rolling 12-monthly prior figures?

   What is the relationship between the total annual revenue
    generated by each branch office and the total number of sales staff
    assigned to each branch office?
Typical Architecture of a Data

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