By Elizabeth Spain at November 09 2018 09:55:33
In the last years some organizations have emerged with the aim of creating professional communities around specific disciplines such as Software Development (SEI, ESI, etc...), Project Management (PMI), Business Process Management (BPMI), IT Service Management (ITSMF), etc... One of the objectives of these groups is to develop a body of knowledge that compiles the discipline's best practices in the form of reference frameworks, methodologies and maturity models. These assets should be considered by any organization interested in knowledge process management.
In mathematics, method of solving a problem by repeatedly using a simpler computational method. A basic example is the process of long division in arithmetic. The term algorithm is now applied to many kinds of problem solving that employ a mechanical sequence of steps, as in setting up a computer program. The sequence may be displayed in the form of a flowchart in order to make it easier to follow. As with algorithms used in arithmetic, algorithms for computers can range from simple to highly complex.
Multiple inputs to the process exist : Some of them would be competition, lifecycle stage of the market, brand image, budget, etc... ; Complex decisions are made : There are many possible ways to achieve the process objectives (reach planned sales, leverage brand image, etc...) ; Each decision implies different levels of risk and potential benefits : It is the responsibility of the worker to choose the best one (low price strategy, aggressive advertising campaign, etc...) ; There are three main characteristics that make knowledge processes different from highly structured processes: Focus is on communication instead of automation.
In medicine, programs have been developed that analyze the disease symptoms, medical history, and laboratory test results of a patient, and then suggest a diagnosis to the physician. The diagnostic program is an example of so-called expert systems-programs designed to perform tasks in specialized areas as a human would. Expert systems take computers a step beyond straightforward programming, being based on a technique called rule-based inference, in which preestablished rule systems are used to process the data. Despite their sophistication, systems still do not approach the complexity of true intelligent thought.