When considering how knowledge affects personal decision making and reasoning, we need to understand what knowledge is and how it relates to information. We distinguish between knowledge and information by recognizing that they are fundamentally different. Information consists of data organized to characterize a particular situation, condition, context, challenge, or opportunity.

Knowledge consists of facts, perspectives and concepts, mental reference models, truths and beliefs, judgments and expectations, methodologies, and know-how. In part, knowledge also consists of understanding how to juxtapose and integrate seemingly isolated information items to develop new meanings —to create new insights with which to approach effective handling of the target situation. We use information to describe and specify what things are. We use information to describe a situation and its context as they exist and develop. We use information in the form of data tables to describe everything from the physical characteristics of metals to today’s and yesterday’s stock market statistics and projections of its future performance. Clearly, much information is created by the application of knowledge to describe and explain. However, that does not make information knowledge.

We use knowledge to evaluate and handle situations, decide how we, for example, use physical tables, or assess how to trade our investment portfolio given stock market information. We use knowledge to assess, decide, problem-solve, plan, act, and monitor.

Actionable knowledge is possessed by humans as well as by other active entities (agents) such as process control computers that are programmed to take actions to manipulate process variables to achieve a desired performance. Actionable knowledge is used to receive information and to recognize and identify; analyze, interpret, and evaluate; synthesize and decide; plan, implement, monitor, adapt, and act. In other words, knowledge is used to reason to determine what a specific situation means, how it should be handled, and to carry out the resulting decision in action. In this context, knowledge serves two purposes: (1) methodological knowledge controls the reasoning process; (2) domain knowledge provides the content of reasoning. In addition, information is needed to describe the state of the situation that is the subject of reasoning.

Passive knowledge may exist in repositories —in systems and procedures, books, documents, databases, and in many other forms. Structural intellectual capital consists mostly of passive knowledge except when embedded in active agents such as computerbased action systems. We use passive knowledge when it is obtained by an active agent and is operationalized. It can, for example, be operationalized and activated by a person who learns about it by reading a description of it, reasons with it, and acts on it. In a less obvious manner, it can be embedded in an organizational structure through specified systems and procedures that are operationalized by people observing managerial intents through their daily actions. Knowledge is accumulated and integrated and held over time by receiving new information, using prior knowledge to interpret it and create hypotheses about its meaning, relevancy, and acceptability. If found “believable,” the new knowledge can be accepted and internalized by establishing its relationship (associations) and deeper meanings relative to what already is known. This is the case with personal knowledge when the process takes place in a person’s mind. It is also the case with creating structural intellectual capital (organizational knowledge) when knowledge is acquired and incorporated in repositories. A brief, practical example portrays differences between information and knowledge. In this system, information on the operating state of the process is obtained continuously by the computer. Knowledge from process experts is embedded in, and operationalized and activated by, the process control computer programs to automate operations. The experts provide personal knowledge and deep understanding of physical and operational principles and specific cases on how to deal both with routine and undesired operating situations. They pool their precise process knowledge with that of other experts, who have embedded general knowledge on optimization and control principles in teaching materials, scientific papers, textbooks, and generic computer software used to generate the control algorithms. That knowledge is assembled by programmers and built into control programs. The static and dynamic operating history of the process is analyzed by conventional, but sophisticated, statistical methods or advanced knowledge discovery in databases (KDD) to obtain data on selected process characteristics, including process dynamics. This historical knowledge becomes part of the control algorithms embedded in the control computer. Hence, the process control computer uses historical knowledge to regulate and control the process as a “business- as-usual” process. The computer cannot create new knowledge or innovate or improvise even when required.