McLeod_CH08.ppt 4985KB Feb 02 2009 06:55:14 AM

Management
Information Systems,
Raymond McLeod
10/eand George
Schell

© 2007 by Prentice Hall

Management Information Systems, 10/e R
aymond McLeod and George Schell

1

Chapter 8
Information in Action

© 2007 by Prentice Hall

Management Information Systems, 10/e R
aymond McLeod and George Schell

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Learning Objectives
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Know that a firm’s ability to develop effective
information systems can be a key factor in its success.
Recognize that the transaction processing system
processes describes the firm’s basic daily operations.
Be familiar with the processes performed by a
transaction processing system for a distribution firm.
Recognize that organizational information systems
have been developed for business areas &
organizational levels.
Be familiar with architectures of marketing, human

resources, manufacturing, & financial information
systems.

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ystems, 10/e Raymond Mc

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Learning Objectives (Cont’d)
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Know the architecture of an executive information system.
Understand what customer relationship management is &
why is requires a large computer storage capability.
Recognize how a data warehouse differs from a database.
Understand the architecture of a data warehouse system.
Know how data are stored in a data warehouse data
repository.
Know how a user navigates through the data repository.
Know what on-line analytical processing (OLAP) is.
Know the two basic ways to engage in data mining.

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ystems, 10/e Raymond Mc

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Information as a Critical Success
Factor
► Critical

success factor (CSF) was coined by
Ronald Daniel to identify a few key activities
that spell success or failure for any type of
organization.
► Transaction processing system (TPS) is the
information system that gathers data
describing the firm’s activities, transforms the
data into information, & makes the information
available to users both inside & outside the
firm.

 1st business application to be installed on computers.
► Also

electronic data processing (EDP) system &
accounting information system .

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ystems, 10/e Raymond Mc

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Figure 8.1 Model of a TPS

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System Overview

► Distribution

system is a TPS used by
distribution firms.
► Distribution firms distribute products or
services to their customers.
► We will use data flow diagrams, or
DFDs, to document the system.
► Figure 8.2 represents the highest level.
► Figure 8.3 identifies the three major
subsystems.
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ystems, 10/e Raymond Mc

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Figure 8.2 Context Diagram of

Distribution System

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ystems, 10/e Raymond Mc

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Figure 8.3 Figure 0 Diagram of
Distribution System

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Major Subsystems of
Distribution System
► Systems

that fill customer orders.

► Systems

that order replenishment stock.

 Order entry system enters customer orders into
the system.
 Inventory system maintains the inventory
records.
 Billing system prepares the customer invoices.
 Accounts receivable system collects the money
from the customers.
 Purchasing system issues purchase orders to
suppliers for needed stock.

 Receiving system receives the stock.
 Accounts payable system makes payments.

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Figure 8.4 Figure 1 Diagram of
Systems that Fills Customers
Orders

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Figure 8.5 Figure 2 Diagram of
Systems that Order
Replenishment Stock

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ystems, 10/e Raymond Mc

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Major Subsystems of
Distribution System (Cont’d)
► Systems

that perform general ledger processes.

 General ledger system is the accounting system
that combines data from other accounting systems
for the purpose of presenting a composite financial
picture of the firm’s operations.
 General ledger is the file that contains the
combined accounting data.
 Updated general ledger system posts records
that describe various actions & transactions to the
general ledger.
 Prepare management reports system uses the
contents of the general ledger to prepare the
balance sheet, income statement, & other reports.

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ystems, 10/e Raymond Mc

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Figure 8.6 Figure 3 Diagram of
Systems that Perform General
Ledger Processes

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Organizational Information
Systems
► Organizational

information systems
are developed to meet the needs for
information relating to those particular
parts of the organization.
► Marketing information system
(MKIS) provides information that relates
to the firm’s marketing activities.
 Consists of a combination of input & output
subsystems connected by a database.
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Figure 8.7 Model of MKIS

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MKIS
►
►

Output subsystems provide information about
critical elements in marketing mix.
Marketing mix consists of 4 main ingredients that
management manages in order to meet customers’
needs at a profit.

 Product subsystem provides information about the firm’s
products.
 Place subsystem provides information about the firm’s
distribution network.
 Promotion subsystem provides information about the
firm’s advertising & personal selling activities.
 Price subsystem helps the manager make pricing decisions.
 Integrated-mix subsystem enables the manager to
develop strategies that consider the combined effects of the
ingredients.

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ystems, 10/e Raymond Mc

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MKIS (Cont’d)
► Database

is populated with data from the
three MKIS input subsystems.
► Input subsystems

 Transaction processing system gathers data
from both internal & environmental sources & enters
the data into the database.
 Marketing research subsystem gathers internal
& environmental data by conducting special studies.
 Marketing intelligence subsystem gathers
environmental data that serves to keep
management informed of activities of the firm’s
competitors & customers & other elements that can
influence marketing operations.

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Other Organizational
Information System
► Human

Resources information system
(HRIS) provides information to managers
throughout the firm concerning the firm’s
human resources.
► Manufacturing information system
provides information to managers throughout
the firm concerning the firm’s manufacturing
operations.
► Financial information system provides
information to managers throughout the firm
concerning the firm’s financial activities.
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Figure 8.8 Model of HRIS

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Figure 8.9 Model of
Manufacturing Information
System

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Figure 8.10 Model of Financial
Information System

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Executive Information
System
► Executive

information system (EIS) is a
system that provides information to upperlevel managers on the overall performance
of the firm; also called Executive support
system (ESS).
► Drill-down capability allows for executives
to bring up a summary display & then
successively display lower levels of detail
until executives are satisfied that they have
obtained as much detail as is necessary.
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Figure 8.11 An EIS Model

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Figure 8.12 Drill-down
Technique

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Customer Relationship
Management
► Customer

relationship management
(CRM) is the management of the relationships
between the firm & its customers so that both
the firm & its customers receive maximum
value from the relationship.
► CRM system accumulates customer data over
a long term – 5 years, 10 years, or more - &
uses that data to produce information for
users.
 Uses a data warehouse.
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Data Warehousing
► Data

warehouse describes data storage that
has the following characteristics:
 Storage capacity is very large.
 Data are accumulated by adding new records, as
opposed to being kept current by updating existing
records with new information.
 Date are easily retrievable.
 Date are used solely for decision making, not for use
in the firm’s daily operations.

► Data

mart is a database that contains data
describing only a segment of the firm’s
operations.

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Data Warehousing System
► Data

warehousing is the creation & use
of a data warehouse or data mart.
► Primary data sources are TPS & data
obtained from other sources, both
internal & environmental; any data
identified as having potential value in
decision making.
► Staging area is where the data
undergoes extraction, transformation, &
loading (abbrev. as ETL process)
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Data Warehousing System
(Cont’d)
► Extraction

process combines data from the
various sources.
► Transformation process cleans the data, puts it
into standardized format, & prepares summaries.
 Data stored in both detail & summary form.

► Loading

process involves the entry of the data
into the data warehouse repository.
► Metadata
 “Data about data”.
 Data that describes the data in the data repository.
 Tracks data as it flows through the data warehouse
system.

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Figure 8.13 Model of Data
Warehousing System

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Storing Data in the Warehouse
Data Repository
► Dimension

tables store the identifying &
descriptive data.
 Dimension provides the basis for viewing the
data from various perspectives or dimensions.

► Fact

tables are separate tables containing
the quantitative measures of an entity.
 Combined with dimension table data, various
analyses can be prepared.
 Users can request information that involves any
combination of the dimensions & facts.

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Figure 8.14 Simple Dimension
Table

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Figure 8.15 Sample Fact Table

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Storing Data … (Cont’d)
► Information

package identifies all of the
dimensions that will be used in analyzing a
particular activity.
► Star schema - for each dimension, a key
identifies the dimension & provides the link to
the information package which results in a
structure that is similar to the pattern of a star.
 The warehouse data repository contains multiple
star schemas, one for each type of activity to be
analyzed.
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Figure 8.16 Information Package
Format

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Figure 8.17 Sample Information
Package

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Figure 8.18 Star Schema
Format

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Figure 8.19 A Sample Star
Schema

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Information Delivery

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Figure 8.20 Navigating the
Warehouse Data Repository

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Figure 8.21 Drilling Across
Hierarchies Produces Multiple
Views

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OLAP
►

►

On-line analytical processing (OLAP) enables the
user to communicate with the data warehouse either
through a GUI or a Web interface & quickly produce
information in a variety of forms, including graphics.
Relational OLAP (ROLAP) uses a standard relational
database management system.




►

ROLAP data exists in detailed form.
Analyses must be performed to produce summaries.
Constrained to a limited number of dimensions.

Multidimensional OLAP (MOLAP) uses a special
multidimensional database management system.

 MOLAP data are preprocessed to produce summaries at the
various levels of detail & arranged by the various dimensions.
 Faster summary ability, can use many dimensions – 10 or
more.

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Figure 8.22 ROLAP & MOLAP
Architectures

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Figure 8.23 Example Report
Produced with ROLAP

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Figure 8.24 Example Report
Produced with MOLAP

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Data Mining
► Data

mining is the process of finding
relationships in data that are unknown to the
user.
► Hypothesis verification begins with the user’s
hypothesis of how data are related.
 Retrieval process guided entirely by user.
 Selected information can be no better than user’s
understanding of the data.
 Traditional way to query a database.

► Knowledge

discovery is when the data
warehousing system analyzes the warehouse
data repository, looking for groups with common
characteristics.

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