Decision Support Systems (DSS)
Decision
Support Systems (DSS) help executives make better decisions by using historical and current
data from internal Information Systems and external sources. By combining
massive amounts of data with sophisticated analytical models and tools, and by making
the system easy to use, they provide a much better source of information to use
in the decision-making process.
Decision Support Systems (DSS)
are a class of computerized information systems that support decision-making
activities. DSS are interactive computer-based systems and subsystems intended
to help decision makers use communications technologies, data, documents,
knowledge and/or models to successfully complete decision process tasks.
While many people think of
decision support systems as a specialized part of a business, most companies
have actually integrated this system into their day to day operating
activities. For instance, many companies constantly download and analyze sales
data, budget sheets and forecasts and they update their strategy once they
analyze and evaluate the current results. Decision support systems have a
definite structure in businesses, but in reality, the data and decisions that
are based on it are fluid and constantly changing.
Types
of Decision Support Systems (DSS)
1.
Data-Driven DSS take the massive amounts of data
available through the company’s TPS and MIS systems and cull from it useful
information which executives can use to make more informed decisions. They
don’t have to have a theory or model but can “free-flow” the data. The first
generic type of Decision Support System is a Data-Driven DSS. These systems
include file drawer and management reporting systems, data warehousing and
analysis systems, Executive Information Systems (EIS) and Spatial Decision
Support Systems. Business Intelligence Systems are also examples of Data-Driven
DSS. Data-Driven DSS emphasize access to and manipulation of large databases of
structured data and especially a time-series of internal company data and
sometimes external data. Simple file systems accessed by query and retrieval
tools provide the most elementary level of functionality. Data warehouse
systems that allow the manipulation of data by computerized tools tailored to a
specific task and setting or by more general tools and operators provide
additional functionality. Data-Driven DSS with Online Analytical Processing
(OLAP) provide the highest level of functionality and decision support that is
linked to analysis of large collections of historical data.
2.
Model-Driven DSS A second category, Model-Driven
DSS, includes systems that use accounting and financial models,
representational models, and optimization models. Model-Driven DSS emphasize
access to and manipulation of a model. Simple statistical and analytical tools
provide the most elementary level of functionality. Some OLAP systems that
allow complex analysis of data may be classified as hybrid DSS systems
providing modelling, data retrieval and data summarization functionality.
Model-Driven DSS use data and parameters provided by decision-makers to aid
them in analyzing a situation, but they are not usually data intensive. Very
large databases are usually not needed for Model-Driven DSS. Model-Driven DSS
were isolated from the main Information Systems of the organization and were
primarily used for the typical “what-if” analysis. That is, “What if we
increase production of our products and decrease the shipment time?” These
systems rely heavily on models to help executives understand the impact of
their decisions on the organization, its suppliers, and its customers.
3.
Knowledge-Driven DSS The terminology for this third
generic type of DSS is still evolving. Currently, the best term seems to be
Knowledge-Driven DSS. Adding the modifier “driven” to the word knowledge
maintains a parallelism in the framework and focuses on the dominant knowledge
base component. Knowledge-Driven DSS can suggest or recommend actions to
managers. These DSS are personal computer systems with specialized
problem-solving expertise. The “expertise” consists of knowledge about a
particular domain, understanding of problems within that domain, and “skill” at
solving some of these problems. A related concept is Data Mining. It refers to
a class of analytical applications that search for hidden patterns in a database.
Data mining is the process of sifting through large amounts of data to produce
data content relationships.
4.
Document-Driven DSS A new type of DSS, a
Document-Driven DSS or Knowledge Management System, is evolving to help
managers retrieve and manage unstructured documents and Web pages. A
Document-Driven DSS integrates a variety of storage and processing technologies
to provide complete document retrieval and analysis. The Web provides access to
large document databases including databases of hypertext documents, images,
sounds and video. Examples of documents that would be accessed by a
Document-Based DSS are policies and procedures, product specifications,
catalogs, and corporate historical documents, including minutes of meetings,
corporate records, and important correspondence. A search engine is a powerful
decision aiding tool associated with a Document-Driven DSS.
5.
Communications-Driven and Group DSS Group Decision Support Systems
(GDSS) came first, but now a broader category of Communications-Driven DSS or
groupware can be identified. This fifth generic type of Decision Support System
includes communication, collaboration and decision support technologies that do
not fit within those DSS types identified. Therefore, we need to identify these
systems as a specific category of DSS. A Group DSS is a hybrid Decision Support
System that emphasizes both the use of communications and decision models. A
Group Decision Support System is an interactive computer-based system intended
to facilitate the solution of problems by decision-makers working together as a
group. Groupware supports electronic communication, scheduling, document
sharing, and other group productivity and decision support enhancing activities
We have a number of technologies and capabilities in this category in the
framework – Group DSS, two-way interactive video, White Boards, Bulletin
Boards, and Email.
Components
of DSS
Traditionally, academics and MIS
staffs have discussed building Decision Support Systems in terms of four major
components:
·
The user
interface
·
The
database
·
The
models and analytical tools and
·
The DSS
architecture and network
This traditional list of
components remains useful because it identifies similarities and differences
between categories or types of DSS. The DSS framework is primarily based on the
different emphases placed on DSS components when systems are actually
constructed.
Data-Driven, Document-Driven and
Knowledge-Driven DSS need specialized database components. A Model- Driven DSS
may use a simple flat-file database with fewer than 1,000 records, but the
model component is very important. Experience and some empirical evidence
indicate that design and implementation issues vary for Data-Driven,
Document-Driven, Model-Driven and Knowledge-Driven DSS.
Multi-participant systems like
Group and Inter- Organizational DSS also create complex implementation issues.
For instance, when implementing a Data-Driven DSS a designer should be
especially concerned about the user’s interest in applying the DSS in
unanticipated or novel situations. Despite the significant differences created
by the specific task and scope of a DSS, all Decision Support Systems have
similar technical components and share a common purpose, supporting decision-
making.
A Data-Driven DSS database is a
collection of current and historical structured data from a number of sources
that have been organized for easy access and analysis. We are expanding the
data component to include unstructured documents in Document-Driven DSS and
“knowledge” in the form of rules or frames in Knowledge-Driven DSS. Supporting
management decision-making means that computerized tools are used to make sense
of the structured data or documents in a database.
Mathematical and analytical
models are the major component of a Model-Driven DSS. Each Model-Driven DSS has
a specific set of purposes and hence different models are needed and used.
Choosing appropriate models is a key design issue. Also, the software used for
creating specific models needs to manage needed data and the user interface. In
Model-Driven DSS the values of key variables or parameters are changed, often
repeatedly, to reflect potential changes in supply, production, the economy,
sales, the marketplace, costs, and/or other environmental and internal factors.
Information from the models is then analyzed and evaluated by the
decision-maker.
Knowledge-Driven DSS use special
models for processing rules or identifying relationships in data. The DSS
architecture and networking design component refers to how hardware is
organized, how software and data are distributed in the system, and how
components of the system are integrated and connected. A major issue today is
whether DSS should be available using a Web browser on a company intranet and
also available on the Global Internet. Networking is the key driver of
Communications- Driven DSS.
