Transcript The Importance of IS Management

Decision Support Systems and
Executive Information Systems
Chapter 12
Information Systems Management In Practice 5E
McNurlin & Sprague
What are Decision Support
Systems?
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Systems that support, not replace, managers in
their decision-making activities
Decision modeling, decision theory, and decision
analysis, attempt to make models from which
the “best decision can be derived, by
computation
DSS is defined as: Computer-based systems
 That help decision makers
 Confront ill-structured problems
 Through direct interaction
 With data and analysis models
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The Architecture for DSS:
Components
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DDM Paradigm is the interaction of
 Dialog (D) between the user and the
system
 Data (D) that support the system
 Models (M) that provide the analysis
capabilities
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The Architecture for DSS:
Components
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A good DSS should have a balance among
the three capabilities:
 Easy to use to support interaction with
non-tech users
 Access to wide variety (4 types) of
information sources
 Provide analysis and modeling in a
variety of ways
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The Architecture for DSS:
The Dialog Component
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The attributes of the dialog
components can be called a “dialog
style”
 Reference card
 Mouse to access pull-down menus
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The Architecture for DSS:
The Data Component
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Data sources
Data warehousing
Data mining
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The Architecture for DSS:
The Model Component
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Models provide the analysis
capabilities for a DSS. Using a
mathematical representation of the
problem, algorithmic processes are
employed to generate information to
support decision making.
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Types of DSS: Institutional
1. Institutional DSS: Intended for organizational
support on a continuing basis,written using a DS
language. Typically mainframe, now PC-based
A. For Marketing Analysis (e.g., Ore-Ida):
Support three main tasks in decision-making
process:
 Data retrieval - help manager answer “What
has happened?”
 Market analysis - answer question “Why did
it happen?”
 Modeling - helps manager answer “What will
happen if…?”
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Types of DSS: Institutional
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Institutional DSS: (cont.)
B. For Sales Forecasting (e.g., Sara-Lee):
 Previously sales forecasts came from sales
force - were too optimistic, inventories
were excessive.
 Then time-series analysis of historical data
was used, did not handle impact of sales
promotions well.
 Now companies use multiple regression
models in order to inject “explanatory
variables” into analysis of historical data and therefore into the forecasts
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Types of DSS: Quick Hit DSS
2. Quick hit DSS: Means a system that is quite
limited in scope, is developed and put into use
quickly, and helps a manager come to a decision
fast. Can be useful for:
 Getting managers started in using DSS
 Providing DS for certain types of management
decisions on an ad hoc or recurring basis
 Providing a basis for deciding whether or not
to build a full DSS
 For supporting decision situations where the
executives cannot wait for a full DSS to be
built
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Types of DSS: Quick Hit DSS
2. Quick hit DSS (cont.) - types:
 Reporting DSS: Select, summarize, and list
data from existing data files to meet
manager's specific info needs
 Short Analysis program: Analyze data as well
as print or display the data. Generally use a
small amount of data, which can be entered
manually, e.g., impact of ESOP
 DSS generators: Provide a way to develop
quick,high - payoff DSS. Include languages,
interfaces, and other facilities that aid in
setting up specific DSS within a class of
decision support applications
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Important Developments in DSS
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PC-based DSS has continued to grow.
Spreadsheets encompass some of the functions
previously performed by DSS generators.
Group DSS to support interdependent group
decisions
Focused versions targeted at specific users
DSS groups as support teams for variety of other
types of user support
User friendly capabilities
DSS refers mostly to systems for analysis of
complex situations, having absorbed most of the
work of management science and operations
research in business organizations
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Data Warehousing and Data
Mining
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Data warehouse: Houses data used to
make decisions. This data is obtained
periodically from transaction databases.
The warehouse provides a snapshot of a
situation at a specific time. Data
warehouses differ from operational
databases in that they do not house data
used to process daily transactions.
Operational databases have the latest data.
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Data Warehousing and Data
Mining
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Key Concepts:
 Metadata: The part of the warehouse that
defines the data. Metadata means “data
about data.” Metadata explains the
meaning of each data element, how each
element relates to each other, etc.
 Quality data: Is the cleaning process
 Data marts: Is a subset of data pulled off
the warehouse for a specific group of
users
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Data Warehousing and Data Mining
Give people new insights into data
 Uncover unknown similarities,
correlations that exist within one
customer group that differentiates them
from other groups
 Is an advanced use of data warehouses,
and it requires huge amounts of detailed
data
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Executive Information Systems (EIS)
& Executive Support Systems (ESS)
ESS:
1. Company performance data: sales,
production, earnings, budgets, and
forecasts
2. Internal communications: personal
correspondence, reports, and meetings
3. Environmental scanning: for news on
government regulations, competition,
financial and economics developments,
and scientific subjects
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EIS and ESS Cont.
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EIS is a DSS that provides access to
(mostly) summary performance data,
 using sophisticated graphics to display
and visualize that data,
 in a very easy to use fashion,
 and with a minimum of analysis for
modeling beyond the capability to “drill
down” in summary data to examine
components.
ESS adds communications and
environmental scanning.
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Pitfalls in ESS Development
1. Lack of executive support - executives
must provide the funding, but are the
principal users and supply the needed
continuity
2. Undefined system objectives - the
technology, the convenience, and the
power of EIS are impressive, but the
underlying objectives and business
values of an EIS must be carefully
thought through
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Pitfalls in ESS Development (cont)
3. Poorly defined information requirements:
EIS typically need non - traditional
information sources - judgments, opinion,
external text-based documents - in
addition to traditional financial and
operating data.
4. Inadequate support staff: support staff
must have technical competence,
understand the business, and ability to
relate executives, and be a permanent
team to manage evolution of systems
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Pitfalls in ESS Development
(cont.)
5. Poorly planned evolution: highly
competent system professionals using
the wrong development process will fail
with EIS; EIS are not developed,
delivered, and then maintained. They
should evolve over a period of time
under the leadership of a team that
includes the executive sponsor, the
operating sponsor, executive users, the
EIS support staff manager, and the IS
technical staff
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What is a strong reason to
install an EIS?
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Attack a critical business need: EIS can be viewed
as an aid to dealing with important needs that
involve the future health of the organization
A strong personal desire by the executive: The
executive sponsoring the project may want to get
information faster than he/she is now getting it, or
have a quicker access to a broader range of
information, or have the ability to select and
display only desired information and to probe for
supporting detail, or to see information in graphical
form
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A weak reason to install an EIS
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“The thing to do”: An EIS is seen as
something that modern management
must have, in order to be current in
management practices. The rationale
given is that the EIS will increase
executive performance and reduce time
that is wasted by such things as
telephone tag.
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The Main Role of EIS
1. A Status Access System: Filter, extract,
and compress a broad range of up-todate internal and external information. It
should call attention to variances from
plan. It should also monitor and
highlight the critical success factors of
the individual executive user. EIS is a
structured reporting system for
executive management, providing the
executive with the data and information
of choice and desired form.
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The Main Role of EIS
2. Human Communications Support: This
viewpoint sees an EIS in terms of human
communications support that it provides.
Manager can call on “network of help”
(peers, subordinates, clients, customers,
suppliers, etc). Manager makes
requests, gives instructions, asks
questions to selected members of this
network, and acts through
communications. EIS supports these
communications.
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DSS Trends
 Personal computer based DSS: Newer
packages
 For the institutional DSS that support
sequential interdependent decision
making: Distributed DSS
 For interdependent decision support:
Group DSS
 Decision support system products are
incorporating tools and techniques from
artificial intelligence
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DSS Trends
 Continued efforts to leverage the
usefulness of DSS: EIS
 DSS development groups have become
less like special project commando
teams and more a part of the end user
support team
 Cutting across all the preceding trends is
the continued development of user
friendly capabilities: Dialog support,
speech recognition
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Future of DSS
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Application of technologies to improve
the performance of information workers
in organizations, specially dealing with
ill-structured problems.
Challenges:
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Integrated architecture: a common interface
at the desktop as common dialog interface to
access all IS
Connectivity: an integrated part of IS
Document in addition to data
More intelligence
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