Read Aloud the Text Content

This audio was created by Woord's Text to Speech service by content creators from all around the world.

Text Content or SSML code:

What Do We Talk about When We Talk about Knowledge? KNOWLEDGE is neither data nor information, though it is related to both, and the differences between these terms are often a matter of degree. We start with those more familiar terms both because they are more familiar and because we can understand knowledge best with reference to them. Confusion about what data, information, and knowledge are -- how they differ, what those words mean -- has resulted in enormous expenditures on technology initiatives that rarely deliver what the firms spending the money needed or thought they were getting. Often firms don't understand what they need until they invest heavily in a system that fails to provide it. However basic it may sound, then, it is still important to emphasize that data, information, and knowledge are not interchangeable concepts. Organizational success and failure can often depend on knowing which of them you need, which you have, and what you can and can't do with each. Understanding what those three things are and how you get from one to another is essential to doing knowledge work successfully. So we believe it's best to begin with a brief comparison of the three terms and the factors involved in transforming data into information and information into knowledge. A Working Definition of Knowledge A word of qualification before we proceed with our definitions. We're aware that some researchers identify more than the three entities of data, information, and knowledge -- going on, for example, to describe wisdom, insight, resolve, action, and so forth. Since we've noticed that firms have enough difficulty distinguishing among three related concepts, however, we're not inclined to address more. For practical purposes, we'll lump higher-order concepts such as wisdom and insight into knowledge. And things like "resolve" and "action," while desirably pointing to 1 of 15 2/11/05 7:00 PM ACM: Ubiquity - Working Knowledge: How Organizations Manag... http://www.acm.org/ubiquity/book/t_davenport_1.html the need to do something with knowledge, we'd put into a different category of "things you do with knowledge" rather than a variation on knowledge itself. With that caution, let's proceed to some definitions. Data Data is a set of discrete, objective facts about events. In an organizational context, data is most usefully described as structured records of transactions. When a customer goes to a gas station and fills the tank of his car, that transaction can be partly described by data: when he made the purchase; how many gallons he bought; how much he paid. The data tells nothing about why he went to that service station and not another one, and can't predict how likely he is to come back. In and of themselves, such facts say nothing about whether the service station is well or badly run, whether it is failing or thriving. Peter Drucker once said that information is "data endowed with relevance and purpose," which of course suggests that data by itself has little relevance or purpose. Modern organizations usually store data in some sort of technology system. It is entered into the system by departments such as finance, accounting, and marketing. Until recently it has been managed by central information systems departments that respond to requests for data from management and other parts of the company. The current trend is for data to be somewhat less centralized and available on demand from desktop PCs, but the basic structure of what it is and how we store and use it remains the same. Quantitatively, companies evaluate data management in terms of cost, speed, and capacity: How much does it cost to capture or retrieve a piece of data? How quickly can we get it into the system or call it up? How much will the system hold? Qualitative measurements are timeliness, relevance, and clarity: Do we have access to it when we need it? Is it what we need? Can we make sense out of it? All organizations need data and some industries are heavily dependent on it. Banks, insurance companies, utilities, and government agencies such as the IRS and the Social Security Administration are obvious examples. Record keeping is at the heart of these "data cultures" and effective data management is essential to their success. Efficiently keeping track of millions of transactions is their business. But for many companies -- even some data cultures -- more data is not always better than less. Firms sometimes pile up data because it is factual and therefore creates an illusion of scientific accuracy. Gather enough data, the argument goes, and objectively correct decisions will automatically suggest themselves. This is false on two counts. First, too much data can make it harder to identify and make sense of the data that matters. Second, and most fundamentally, there is no inherent meaning in data. Data describes only a part of what happened; it provides no judgment or interpretation and no sustainable basis of action. While the raw material of decision making may include data, it cannot tell you what to do. Data says nothing about its own importance or irrelevance. But data is important to organizations -- largely, of course, because it is