IT Governance, Risk, and Compliance » Linear Programming

Compliance through Automation: Decision Support Systems – Part VIII

Robert Davis — Mon, 23 Aug 2010 16:15:45 +0000

Decision-making is the process of evaluating alternatives and choosing from among them. Information may drive leadership; however, data accuracy and completeness are prerequisites to ensuring appropriate decisions are made. A DSS commonly assists middle-level and upper-level managers in long-term, non-routine, and often unstructured decision-making. Typically, the deployed system contains a least one decision model, is usually interactive, dedicated, and time-shared; but need not be real-time. Thus, a DSS should be viewed as an aid in decision-making rather than simply the automation of decision processes. Managers should concentrate upon making professional compliance decisions based on a DSS that reduces the risk of inappropriate responses to the entity’s environment.

“View Part I of the Compliance through Automation: Decision Support Systems series here“

Compliance through Automation: Decision Support Systems – Part VII

Robert Davis — Thu, 19 Aug 2010 14:49:46 +0000

Information asset value is continuously increasing in this information age due to integration into decision-making processes. Compliance decisions are of high visibility, often offer immediate results, tend to be goal focused, and are directive. Although there are various techniques that can be applied to compliance decision types, the final disposition is judgmental. Normally, IT processes can be adapted to support judgmental decisions through the utilization of engineered processes. In order to ensure robustness for the intended application, DSS models must pass three tests: relevance, accuracy, and aggregation. Relevance is measured by the alignment of the condition to the problem. Yet, depending on the decision being made, accuracy can vary. While, aggregation permits grouping of a number of individual quantities into a larger quantity.

“View Part I of the Compliance through Automation: Decision Support Systems series here“

Compliance through Automation: Decision Support Systems – Part VI

Robert Davis — Mon, 16 Aug 2010 14:40:10 +0000

DSS models are abstractions that operate as substitutes for actual circumstances under evaluation. A model-driven DSS emphasizes access to and manipulation of statistical, financial, optimization, or simulation archetypes. Consequently, a model-driven DSS utilizes datum and parameters provided by users to assist decision makers in analyzing a situation; however, they are not necessarily data-intensive. Compliance DSS model construction includes: making a large number of assumptions about the nature of the environment in which the entity’s programs, systems, processes, activities and/or tasks operate; the operating characteristics of components; and about the way animate and/or inanimate objects are likely to behave. Managerial application is established through knowing when the model matches the set of objectives and attributes requiring analytical consideration.

“View Part I of the Compliance through Automation: Decision Support Systems series here“

Compliance through Automation: Decision Support Systems – Part V

Robert Davis — Thu, 12 Aug 2010 18:40:28 +0000

There are many types of detail variables that may be associated with a mathematical model. Binary variables are employed for “go” and “no-go” decisions. Furthermore, discrete variables are utilized for any of a finite number of values. Questions of “which” and “when” are represented as specific discrete values. Such datum need not be continuous, however, continuous variables present an infinite number of possible values, and all the values will lie within a specific range. Among the other characteristics of variables, they can be random variables that model uncertainty and are expressed as probabilities. They can also be exogenous variables, ones that are external to the model and cannot be influenced by decision makers.

“View Part I of the Compliance through Automation: Decision Support Systems series here“

Compliance through Automation: Decision Support Systems – Part IV

Robert Davis — Mon, 09 Aug 2010 18:31:03 +0000

At a minimum, compliance decision support systems should include word processing, database, spreadsheet, and modeling capabilities. Of these capabilities, modeling is crucial to reducing response uncertainty regarding circumstances that require a compliance decision. Rudimentarily, a model is comprised of variables and objectives; where the structure must reflect the purpose for construction. The variables in a quantitative model constitute a mathematical description of the relation between elements that can be classified as: decision, intermediate, or output variables. Decision variables are controlled by the decision maker and vary in accordance with the alternative selected. Whereas, intermediate variables link decisions to outcomes; thus functioning as consolidation variables. Lastly, output variables measure decision performance, and are referred to as ‘attributes’.

“View Part I of the Compliance through Automation: Decision Support Systems series here“

Compliance through Automation: Decision Support Systems – Part III

Robert Davis — Thu, 05 Aug 2010 16:13:25 +0000

Generally, supporting decisions with software and hardware is wholly inadequate if there is no specific idea about the kinds of decisions to be made. There are different types of decisions regarding compliance. The first type, routine decisions, is commonly treated within the entity’s framework of policies and procedures. The second type, non-routine decisions, typically requires one-time or non-repetitive solutions based on environmental considerations. The third type, non-routine motley decisions, is generally ill-structured, complex, one-of-kind situations that rely on optimal responses through scientific assessments. Nevertheless, decision techniques have become largely synonymous with quantitative approaches or mathematical analysis; well suited for IT processes. Some types of the latter are financial and statistical analysis that depending on the circumstances may be addressed through game theory, linear programming, simulation, and operations research.

“View Part I of the Compliance through Automation: Decision Support Systems series here“