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Decision Suppport resources on the web

Definition (from whatis.com)

A DSS (decision support system) is a computer program application that analyzes business data and presents it so that users can make business decisions more easily. It is an "informational application" (in distinction to an "operational application" that collects the data in the course of normal business operation).Typical information that a decision support application might gather and present would be:

A decision support system may present information graphically and may include an expert system or artificial intelligence (artificial intelligence). It may be aimed at business executives or some other group of knowledge workers.

Further Information

Related Topics

<Obtained from http://www.sdgcomputing.com/glossary.htm >

Decision Support System (DSS)

A computer system designed to assist an organization in making decisions.

The Decision Support Systems and Enterprise Information Systems of the 1980's and early 1990's were forerunners of today's Business Intelligence Tools.

Business Intelligence Tools

Software that enables business users to see and use large amounts of complex data.

The following three types of tools are referred to as Business Intelligence Tools:

  1. Multi-Dimensional Analysis Software - Also Known As Multi Software or OLAP (On-Line Analytical Processing) - Software that gives the user the opportunity to look at the data from a variety of different dimensions.
  2. Query Tools - Software that allows the user to ask questions about patterns or details in the data.
  3. Data Mining Tools - Software that automatically searches for significant patterns or correlations in the data.

Multi-Dimensional Analysis

Also Known As: OLAP (On-Line Analytical Processing)

A process of analysis that involves organizing and summarizing data in a multiple number of dimensions.

People can comprehend a far greater amount of information if that information is organized into dimensions and into hierarchies. The wide use of spreadsheets and graphs illustrates the need for people to have their information organized.

A spreadsheet is a two-dimensional analysis tool. If a person could comprehend 10 individual facts, they could possibly comprehend 100 facts if they were arranged in a spreadsheet.

If 3 or 4 or 5 dimensions could be displayed, the amount of information that could be comprehended would be increased exponentially - to 1000 facts, 10,000 facts, and 100,000 facts.

We can't directly display more than two dimensions on a flat surface. But a variety of companies are producing multis - multi-dimensional analysis tools - that use a variety of clever ways of displaying multiple dimensions.

These tools also organize the data hierarchically, allowing users to "drill down" for more detailed information, "drill up" to see a broader, more summarized view, and "slice and dice" to dynamically change the dimensions that are being viewed.

OLAP (On-Line Analytical Processing)

The use of computers to analyze an organization's data.

"OLAP" is the most widely used term for multi-dimensional analysis software. The term "On-Line Analytical Processing" was developed to distinguish data warehousing activities from "On-Line Transaction Processing" - the use of computers to run the on-going operation of a business.

In its broadest usage the term "OLAP" is used as a synonym of "data warehousing". In a more narrow usage, the term OLAP is used to refer to the tools used for Multi-Dimensional Analysis.

"Think of an OLAP data structure as a Rubik's Cube of data that users can twist and twirl in different ways to work through what-if and what-happened scenarios." - Lee The, Editor, Datamation (May 1995)

Data Mining

The process of finding hidden patterns and relationships in the data.

Analyzing data involves the recognition of significant patterns. Human analysts can see patterns in small data sets. Specialized data mining tools are able to find patterns in large amounts of data. These tools are also able to analyze significant relationships that exist only when several dimensions are viewed at the same time.

Users can ask data questions using standard queries when they know what they're looking for. Queries can be written for questions like this: "Which of our out-of-town customers have given us the most business in the last year?"

Data mining is needed when the user's questions are more vague or general in nature. Data mining questions would include: "What attributes characterize the customers that gave us the most business in the past year?"

How Do DSS Environments Support Decision-Making?

( http://www.dpapplications.com/content.cfm?p=b0806000 ) DSS environments support the generic decision-making model above in a number of ways:

  1. In decision preparation, DSS environments provide data required as input to the decision-making process. This is interestingly enough, about all most data mart and data warehousing environments do today.
  2. In decision structuring, DSS environments provide tools and models for arranging the inputs in ways that make sense to frame the decision. These tools and models are not pivot tables and other aspects of data presentation found in query tools. They are actual decision-making tools, like fault tree analysis, Bayesian logic and model-based decision-making based on things like neural networks.
  3. In context development, DSS environments again provide tools, and provide the mechanisms for capturing information about a decision's constituencies (who's affected by this decision), outcomes and their probabilities, and other elements of the larger decision-making context.
  4. In decision-making, DSS environments may automate all or part of the decision-making process and offer evaluations on the optimal decision. Expert systems and artificial intelligence environments purport to do this, but they work only in very limited cases, because of some fundamental flaws in the technology (namely, their inability to deal with non-binary, or fuzzy, choices, like "it's more likely that we'll lose market share than win it," which is a rule that no traditional AI-based system can code).
  5. In decision propagation, DSS environments take the information gathered about constituencies and dependencies and outcomes and drive elements of the decision into those constituencies for action.
  6. In decision management, DSS environments inspect outcomes days, weeks and months after decisions to see if (a) the decision was implemented/propagated and (b) if the effects of the decision are as expected.
So, what commercial products do this today? Outside of very limited cases, focused on specific problems in very high-risk areas (like bomb disposal, scheduling airplanes into airports, arranging railroad cars on trains), none do. Most commercial DSS products don't do anything except decision preparation: they dump data on the desktops of decision-makers, saying in effect, "You must know what to do with this."

See Also

( http://dssresources.com/ )

Slides/Notes about DSS

Additional Stuff

< Obtained from SearchEBusiness.com, SearchDatabase.com, and SearchCRM.com >

SolEuNet, Peter.Flach@bristol.ac.uk. Last modified on Thursday 29 November 2001 at 16:19. © 2001 SolEuNet