IT Governance, Risk, and Compliance


September 16, 2010  4:08 PM

Compliance through Automation: Expert Systems – Part VII

Robert Davis Robert Davis Profile: Robert Davis

To assist in assessing decisional acumen, most managers are under observation for situational responses impacting the entity. Therefore, information reliability is critical. During the final stage of preparation for deployment, an expert system has to be validated to ascertain reliability and scope of decisional processes. In the model validation step a knowledge engineer and/or IT assurance professional identifies errors, omissions and mistakes in the knowledge base. Furthermore, since the constructed system is designed to simulate an expert’s decision making process, it should be tested against opinions of subject matter experts. Lastly, if the system is later updated to keep the knowledge base current, model reevaluation is necessary to ensure continued decisional reliability.

View Part I of the Compliance through Automation: Expert Systems series here

September 13, 2010  3:50 PM

Compliance through Automation: Expert Systems – Part VI

Robert Davis Robert Davis Profile: Robert Davis

After experts have been selected, the knowledge engineer must take the expert knowledge and transform it into a computational model. However, issues may arise because an expert discovers that they are unable to describe how a situational scenario is resolved. Typically, this is due to experts operating at a subconscious-level while performing some tasks to address a scenario. Considering the possibility of undefined steps generating misaligned logic paths in the inference engine, commonly, interdisciplinary teams of specialists must work in unison to formulate deductive reasoning processes for defined problems.

View Part I of the Compliance through Automation: Expert Systems series here


September 9, 2010  2:18 PM

Compliance through Automation: Expert Systems – Part V

Robert Davis Robert Davis Profile: Robert Davis

To incorporate human expert knowledge into a technology-based expert system, the right individuals must be identified and selected. Specialists tend to be trained in rather narrow domains and are best at solving problems within their defined domains. Assuming experts do exist and are willing to participate; good experts are those who are able to solve particular types of problem scenarios that most others cannot solve with the same efficiency and/or effectiveness. Additionally, considerable time can be saved in developing an expert compliance system if the knowledge engineer has experience in the area being modeled.

View Part I of the Compliance through Automation: Expert Systems series here


September 6, 2010  3:00 PM

Compliance through Automation: Expert Systems – Part IV

Robert Davis Robert Davis Profile: Robert Davis

Several methods exist for a knowledge engineer to obtain knowledge. One option is to go through textbooks and professional journals with the intent to extract definitions, axioms, and rules that apply to the issue. This type of knowledge acquisition is especially useful for teaching and reference situations because question-response paths are direct. However, how the question is posed to the expert system can lead to misleading results. Another method of acquiring knowledge is to ask human experts to explain their thought process and method for solving problem scenarios, sometimes referred to as verbal protocol analysis. Lastly, a human expert can enhance the information obtained from literary resources and often bring unpublished knowledge, gained through experience, to the decision process paths. As a result, this combinational knowledge makes human-based expert systems a valuable technology.

View Part I of the Compliance through Automation: Expert Systems series here


September 2, 2010  3:50 PM

Compliance through Automation: Expert Systems – Part III

Robert Davis Robert Davis Profile: Robert Davis

Expert system development is usually a four step process. It starts with the knowledge engineer obtaining an understanding of a particular judgment issue. It is followed by the acquisition of thought processes of experts in solving the issue. Next, a computer model is programmed to reproduce the adopted thought processes of defined situations; if a shell program is unavailable. Lastly, the system is tested and certified to ensure appropriate resulting decisions and usability. These steps are commonly known as: knowledge representation, knowledge acquisition, computational modeling, and model validation.

View Part I of the Compliance through Automation: Expert Systems series here


August 30, 2010  4:39 PM

Compliance through Automation: Expert Systems – Part II

Robert Davis Robert Davis Profile: Robert Davis

IT usually pervades all organizational formations pursuing effective and efficient processing in response to compliance requirements, thus facilitating better decision-making through various information delivery mechanisms and offering opportunities for business model development that may lead to value creation as well as competitive advantages. To construct an expert compliance system, a knowledge engineer, performing a function similar to a system or business analyst, is typically needed. A designated knowledge engineer is responsible for defining issues in manageable terms, soliciting the knowledge, skills and abilities of experts, and translating these talents into electronically encoded formats.

View Part I of the Compliance through Automation: Expert Systems series here


August 26, 2010  4:17 PM

Compliance through Automation: Expert Systems – Part I

Robert Davis Robert Davis Profile: Robert Davis

Technology is an ever changing tool driven by compliance requirements as well as entity-centric needs to satisfy market demands. For compliance requirements, IT deployments tend to be reactionary rather than a continuous, proactive process. Consequently, IT compliance efforts are typically lacking constancy and conformity. To combat this tendency, IT planners should focus design and transition efforts on three time frames for meeting entity needs: the current state, the near-term state, and the long-term state of compliance requirements. Within this context, expert systems can be an invaluable tool to implement mandates that satisfy immediate needs and simultaneously position the entity to effectively meet the next potential compliance issue.


August 23, 2010  4:15 PM

Compliance through Automation: Decision Support Systems – Part VIII

Robert Davis Robert Davis Profile: Robert Davis

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


August 19, 2010  2:49 PM

Compliance through Automation: Decision Support Systems – Part VII

Robert Davis Robert Davis Profile: Robert Davis

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


August 16, 2010  2:40 PM

Compliance through Automation: Decision Support Systems – Part VI

Robert Davis Robert Davis Profile: Robert Davis

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


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