Page 93 -

92 Part I • Decision Making and Analytics: An Overview

• Document-driven DSS
• Knowledge-driven DSS, data mining, and management ES applications
• Model-driven DSS
There may also be hybrids that combine two or more categories. These are called
compound DSS. We discuss the major categories next.

coMMunications-Driven anD grouP Dss Communications-driven and group DSS
(GSS) include DSS that use computer, collaboration, and communication technologies
to support groups in tasks that may or may not include decision making. Essentially,
all DSS that support any kind of group work fall into this category. They include
those that support meetings, design collaboration, and even supply chain management.
Knowledge management systems (KMS) that are developed around communities that
practice collaborative work also fall into this category. We discuss these in more detail
in later chapters.

Data-Driven Dss Data-driven DSS are primarily involved with data and processing
them into information and presenting the information to a decision maker. Many DSS
developed in OLAP and reporting analytics software systems fall into this category. There
is minimal emphasis on the use of mathematical models.
In this type of DSS, the database organization, often in a data warehouse, plays
a major role in the DSS structure. Early generations of database-oriented DSS mainly
used the relational database configuration. The information handled by relational
databases tends to be voluminous, descriptive, and rigidly structured. A database-
oriented DSS features strong report generation and query capabilities. Indeed, this
is primarily the current application of the tools marked under the BI umbrella or
under the label of reporting/business analytics. The chapters on data warehousing and
business performance management (BPM) describe several examples of this category
of DSS.

DocuMent-Driven Dss Document-driven DSS rely on knowledge coding, analysis,
search, and retrieval for decision support. They essentially include all DSS that are text
based. Most KMS fall into this category. These DSS also have minimal emphasis on utiliz-
ing mathematical models. For example, a system that we built for the U.S. Army’s Defense
Ammunitions Center falls in this category. The main objective of document-driven DSS is
to provide support for decision making using documents in various forms: oral, written,
and multimedia.

knoWleDge-Driven Dss, Data Mining, anD ManageMent exPert systeMs
aPPlications These DSS involve the application of knowledge technologies to address
specific decision support needs. Essentially, all artificial intelligence–based DSS fall into
this category. When symbolic storage is utilized in a DSS, it is generally in this category.
ANN and ES are included here. Because the benefits of these intelligent DSS or knowledge-
based DSS can be large, organizations have invested in them. These DSS are utilized in the
creation of automated decision-making systems, as described in Chapter 12. The basic idea
is that rules are used to automate the decision-making process. These rules are basically
either an ES or structured like one. This is important when decisions must be made quickly,
as in many e-commerce situations.

MoDel-Driven Dss The major emphases of DSS that are primarily developed around
one or more (large-scale/complex) optimization or simulation models typically include
significant activities in model formulation, model maintenance, model management

M02_SHAR9209_10_PIE_C02.indd 92 1/25/14 7:45 AM