Technology

A Decision Support System (DSS) is defined as all computer based components that support decision making.  Decision support systems use intelligent knowledge based systems and are a subsystem of organizational information system.  Decisions are a component of any operational system thus the inclusion of a support system that facilitates decision making is a measure that is directed towards improving systems within an organization.  Modeling and risk taking are aspecsts that are widely incorporated into organizational operations.  There are multiple internal and external factors that businesses consider in their operations.  The external environment is dynamic and the relationship between the internal factors that affect system operations is not always clear.  This paper seeks to determine how decision support systems relate to risk taking and modeling within organizations.  Determination of their relationship and its significance to organizations is the main issue that will be tackled in the paper.

Decision Support Systems
DSS support organizational decision making activities and if properly designed can help in the compilation of raw data that can facilitate value generation within firms.  It is evident that a DSS is not only an information repository but also has algorithms and models that can aid optimal solution of typical business problems (Burstein,  Holsapple, 2008).  The typical information that can be gathered by DSS applications includes an inventory of all information assets, comparative data and projected revenue figures (Shimizu, de Carvalho,  Jose Barbin, 2006).  The development of decision support systems is partly due to improvement in technology and multiple variables that businesses have to consider in decision making.  The dynamism displayed by operational variables in the modern business environment has led to increased awareness on the need for a universal view of operational variables.  This need for a comprehensive analysis of an organizational scenario before making any decision is also responsible for the increased use of business modeling and risk analysis.

The definition and taxonomy used in DSS are not universal though their role in facilitating organizational operations is universally acknowledged.  There are various categorizations of DSS based on either their functionalities or tasks they carry out.  On the basis of functionality and nature of interaction with the user, there are active, passive and cooperative DSS (Shimizu, de Carvalho,  Jose Barbin, 2006).  On the basis of functionalities, there is the communication driven, data driven, document driven, knowledge driven and model driven DSS.

Modeling 
Modeling is an aspect ingrained in business management that is concerned with the abstraction of real life requirements into a form that can be easily understood by persons with different views.  In an organization, every stakeholder has a different view of the requirements in business operations. For an organization to function optimally, the different views have to be incorporated in organizational decision making and strategic development.  A systemic view of an organization shows that address of the interest and issues faced by different stakeholders is a critical success factor in organizational operations.  Furthermore, business modeling aids in developing an understanding of the interaction between internal systems and external entities (Tennent,  Friend, 2005).  By doing so, a universal picture of the constraints that have to be considered in decision making is developed.  In operations management as an example, modeling plays a role in developing an understanding of the variables and the conditional constraints which can then be programmed to come up with an optimal solution.  Decision support systems can aid in not only identification of various entities and interaction between internal components but also optimization of the developed model.  Another area in which business modeling comes in handy is documentation of systemic development.  Documentation is a critical requirement in organizational operations that not only aids in knowledge development but also evaluation of project performance by aiding determination of whether a resulting system meets its specifications.  Modeling results in compact information laden abstractions of requirements can be analyzed from both high and low level perspectives.  Since DSS require an information repository and have intelligent systems capabilities, business modeling may help in developing the functionalities and accuracy of a DSS.

Risk Analysis
Business operations can be looked at as a combination of projects with clearly set goals and multiple internal and external constraints.  Risk analysis is the art of developing a concise picture of the variables that may impede attainment of set goals.  In a business setting, analysis of the internal and external environment with the aim of developing a concise picture of threats and weakness so that corrective measures can be put in place is referred to as risk analysis.  It is noteworthy that unlike business modeling which may be done once in a project, risk analysis is a continuous process due to the fact that operational variables interact and are dynamic (Aven, 2008).  Moreover, it is not easy to predict accurately the nature of the operational environment and the requirements that have to be considered in executing a project.  Thus, risk analysis within an organization is an ongoing process that facilitates minimization of the effects of threats while attempting to reduce the time and financial costs associated with their management.

The importance of risks analysis is mitigation that is done by minimization of the probability of threats to project completion or success occurring.  From this dimension, it is evident that some element of programming is required in minimization of the likelihood that a threat will occur and development of measures aimed at addressing threats.  This brings out a clear link between DSS and risk analysis in that a decision support systems can facilitate risk analysis.  Moreover, organizational decision making not only involves considerations of organizational ability but also the risks involved in any endeavor (Aven, 2008).  Risk analysis can aid decision support systems develop a comprehensive scenario analysis thus presenting with an accurate picture of the requirements and even options that a business has in seeking its operational goals.

Discussion
To develop a critical understanding of how DSS relates to modeling and risk analysis, the internal structure and functionality of DSS have to be brought to the fore.  A typical DSS is made up of a database, a decision making criteria or rather the model and a user interface that facilitates interaction with system functionalities.

It is noteworthy that this architecture explains the technical aspects, though people are an important component of a DSS considering that it is part of the overall information system (Tennent,  Friend, 2005).

The development of a DSS and even changes in its functionalities affect organizational operations.  Thus, the operations and management of a DSS are associated with multiple HR risks, for instance - resistance which can only be determined via extensive risk analysis.  From the technical architecture of a DSS, the decision making criteria or model plays a vital role in determining its functionality (Burstein,  Holsapple, 2008).

Development of an effective model requires thorough consideration on business processes which can be facilitated by the use of business, system and conceptual models.  Modeling in general plays a vital role in ensuring that the functionalities of a DSS are in tandem with business requirement (Tennent,  Friend, 2005).  It is evident that various modeling concepts in developing decision support systems facilitate the formulation of an effective algorithm base.

Current DSS have knowledge development or learning capability due to incorporation of intelligent systems.  Therefore, as knowledge based DSS are used, they develop more trends thus improving their ability and knowledge of organizational processes and operational variables.  This in turn results in DSS that can facilitate modeling by presenting an accurate abstraction of an organization, trends in the external environment and possible lines of action that can be adopted by an organization in seeking its operational goals.  In this way, decision support systems facilitate the development of accurate models that aid organizational success.
Risk analysis is a core aspect in project management and general business operations.  To develop an effective DSS system, specific user requirements, security systems and existing systems have to be considered (Aven, 2008).  A major development like DSS not only affects the technical platform that supports an organizations information needs but may also affect how employees interact with the existing systems.  Such extensive changes are likely to be faced by both people and technical threats.  This is the main reason for the importance allocated to risk analysis in development of DSS.  Another aspect that highlights the importance of risk analysis in business operations is the fact that a DSS is an IT based structure thus it is affected by the multiple risks associated with rapid development in IT.

On the other hand, risk analysis is a complex process that requires collection of data from multiple resources, generations of trends, development of models and even programming (Aven, 2008).  Moreover, risks analysis may result in a scenario where a business has to choose from multiple competing strategies.  In such cases, DSS comes in hand due to its extensive information repository, ability to generate model, information processing capability and interactive nature that allows incorporation of information that may not be available in the central repository (Burstein,  Holsapple, 2008).  It is thus evident that risk analysis in an organization is facilitated by the existence of DSS.  Another important observation is the role of DSS in facilitating interaction between modeling and risk analysis.  Analysis of risk, especially calculation of the likelihood of their occurrences, and even optimization of resource usage under given constraints have to be modeled.  On the other hand, a good model must be appreciative of uncertainties and threats that exist in the internal and external operational environment. DSS provides an extensive platform where the interaction between modeling and risk analysis is error free and results in operations that are efficient and value laden.

DSS are gaining relevance in business due to multiple uncertainties that businesses face in their operations and increased need for efficiency.  Risk analysis and modeling are also gaining audience in organizations due to similar reasons.  However, this is not the only point of interaction between these important organizational operations.  The development of an extensive DSS system depends on how well the modeling and risk analysis aspects are handled.  On the other hand, an extensive DSS system supports modeling and risk analysis in an organizationg