Benjamin Franklin Center for Knowledge Management

Benjamin Franklin
Contents
Benjamin Franklin was arguably the first "knowledge manager" in America. From his newspaper, to Poor Richard's Almanack, he diffused information throughout the colonies and the world and showed people how to effectively apply it.
Knowledge is critical to a republic. Voters must understand the issues, understand their options and make informed decisions in the election booth to vote responsibly for politicians and on issues. Politicians and non-elected government leaders likewise must be able to apply relevant information to make informed decisions, solve problems, and effectively accomplish their normal functions. Without effective knowledge management a republic is doomed to failure.

In the foundational days of the republic, the press was perhaps the key element of knowledge management. After the trials of the Alien and Sedition Acts enacted by the Adams' administration, Jefferson, in his first Inaugural Address, remarked, "... the diffusion of information and arraignment of all abuses at the bar of public reason; freedom of religion; freedom of the press…” to underscore to importance of a free press.

So what is "knowledge" as opposed to "data" and "information"?

Data, Information, and Knowledge Simplistically data are recorded observations. They can be deliberately captured as in metrics, production statistics, and experimental observations or they can come about by chance. Regardless of the source, data must be both observed and recorded. The organization must establish its perceptual filters to expose it to data and then have some means to capture it. Perceptual filters both open and close the organization to observations of the internal and external environments. They can screen out elements in the environment and prevent the organization from obtaining awareness of them or they can allow elements in through the filters and provide awareness. But simply being aware of the observation is not enough. They must be recorded so the organization can access them as required.

Information is data with some form of context. By itself, a data point is a one dimensional observation. It can be simply a number or a number of units. Data is transformed into information when a context is applied and recorded with it. For example, "25 units" does not tell anyone very much. But "25 units of a commercial red dye for production at 2:00 PM on a Tuesday" provides more context. Likewise the record "Bob Jones" does not tell us much. It is a person, a high school, or a university? If a person, is the name short for "Robert Jones"? Even that may not tell us much given the number of people named Robert Jones. But if we record "Bob Jones visited the office at 4:00 to discuss the planning update" we have some context and information. There are still too few dimensions to be precise, but perhaps we can infer these dimensions if we can place the information in context with other information. The more attributes we add to the data, the richer the context that we develop. Thus, observation and recording systems that capture attributes provide richer information than those that do not.

Knowledge is information that can be applied to accomplish tasks, solve problems, and facilitate decision-making. The richer the context associated with the information, the more readily it can be applied. If we start a new planning update, we may find Bob Jones has some relevant role to play in it and could prove instrumental in this update. Perhaps a little digging back into organizational history would tell us that he provides some of the key environment assumptions, or maybe he did the catering for lunch. If he provided input on key assumptions, and that input was relevant, we may want to engage him again. Likewise, the "25 units on Tuesday" may be part of a series of data that by itself does not tell us much, but when put together in context may tell us we have an issue with production on Tuesdays.

In the diagram above we use puzzle pieces to help illustrate the process of turning data into knowledge and using it. Data are the individual puzzle pieces. They are often scrambled together in no apparent order. As we begin to exam the data, we try to determine context. In the case of a puzzle, it is shape, color, image components of the puzzle pieces. Given these contextual clues, we are then able to start putting pieces of the puzzle together and begin to develop information that helps further understand the puzzle and how it fits together. We may make inferences about missing puzzle parts and what the puzzle will show when its complete. As more and more pieces fill in the gaps, we begin to develop an understanding of the information and how we can incorporate it into decisions, tasks, and to solve problems. Wisdom allows us to actually apply the knowledge. If we are able to link elements of knowledge together, the knowledge potential increases. In the case of the puzzle above, perhaps we may even begin to understand why the lady smiles and how to get others to smile as well.

In the case of a New Enlightenment, knowledge is the key.


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