Transcript Organizational information systems

Management
Information Systems,
10/e
Raymond McLeod and George Schell
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
1
Chapter 8
Information in Action
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
2
Learning Objectives
►
►
►
►
►
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
3
Learning Objectives (Cont’d)
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.
►
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
4
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
5
Figure 8.1 Model of a TPS
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
6
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
7
Figure 8.2 Context Diagram of
Distribution System
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
8
Figure 8.3 Figure 0 Diagram of
Distribution System
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
9
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
10
Figure 8.4 Figure 1 Diagram of
Systems that Fills Customers Orders
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
11
Figure 8.5 Figure 2 Diagram of
Systems that Order Replenishment
Stock
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
12
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
13
Figure 8.6 Figure 3 Diagram of
Systems that Perform General
Ledger Processes
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
14
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
15
Figure 8.7 Model of MKIS
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
16
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
17
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
18
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
19
Figure 8.8 Model of HRIS
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
20
Figure 8.9 Model of Manufacturing
Information System
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
21
Figure 8.10 Model of Financial
Information System
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
22
Executive Information System
► Executive
information system (EIS) is a
system that provides information to upper-level
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
23
Figure 8.11 An EIS Model
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
24
Figure 8.12 Drill-down Technique
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
25
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
26
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
27
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)
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
28
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
29
Figure 8.13 Model of Data
Warehousing System
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
30
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
31
Figure 8.14 Simple Dimension Table
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
32
Figure 8.15 Sample Fact Table
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
33
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
34
Figure 8.16 Information Package
Format
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
35
Figure 8.17 Sample Information
Package
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
36
Figure 8.18 Star Schema Format
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
37
Figure 8.19 A Sample Star Schema
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
38
Information Delivery
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
39
Figure 8.20 Navigating the
Warehouse Data Repository
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
40
Figure 8.21 Drilling Across
Hierarchies Produces Multiple Views
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
41
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
42
Figure 8.22 ROLAP & MOLAP
Architectures
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
43
Figure 8.23 Example Report
Produced with ROLAP
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
44
Figure 8.24 Example Report
Produced with MOLAP
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
45
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.
© 2007 by Prentice Hall
Management Information Systems, 10/e
Raymond McLeod and George Schell
46