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 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 judgement 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 essential raw material for the creation of information.
INFORMATION: Like many researchers who have studied information, we will describe it as a message, usually in the form of a document or an audible or visible communication. As with any message, it has a sender and a receiver. Information is meant to change the way the receiver perceives something, to have an impact on his judgment and behavior. It must inform; it’s data that makes a difference. The word ‘inform’ originally meant ‘to give shape to’ and information is meant to shape the person who gets it, to make some difference in his outlook or insight. Strictly speaking, then, it follows that the receiver, not the sender, decides whether the message he gets is really information–that is, if it truly informs him. A memo full of unconnected ramblings may be considered ‘information’ by the writer but judged to be noise by the recipient. The only message it may communicate successfully is an unintended one about the quality of the sender’s intelligence or judgment.
Information moves around organizations through hard and soft networks. A hard network has a visible and definite infrastructure: wires, delivery vans, satellite dishes, post offices, addresses, electronic mail-boxes. The messages these networks deliver include email, traditional or ‘snail’ mail, delivery-service packages, and internet transmissions. A soft network is less formal and visible. It is ad hoc. Someone’s handing you a note or a copy of an article marked ‘FYI’ is an example of information transmission via soft network.
Quantitative measures of information management tend to include connectivity and transactions: How many email accounts or Lotus Notes users do we have? How many messages do we send in a given period? Qualitative measures measure informativeness and usefulness: Did the message give me some new insight? Does it help me make sense of a situation and contribute to a decision or the solution to a problem?
Unlike data, information has meaning–the ‘relevance and purpose’ or Drucker’s definition above. Not only does it potentially shape the receiver, it has a shape: it is organized to some purpose. Data becomes information when its creator adds meaning. We transform data into information by adding value in various ways. Let’s consider several important methods, all beginning with the letter C:
Contextualized: we know for what purpose the data was gathered.
Categorized: we know the units of analysis or key components of the data.
Calculated: the data may have been analyzed mathematically or statistically.
Corrected: errors have been removed from the data.
Condensed: the data may have been summarized in a more concise form.
Note that computers can help to add these values and transform data into information, but they can rarely help with context, and humans must usually help with categorization, calculation and condensing. A problem we will deal with throughout this book is the confusion of information–or knowledge–with the technology that delivers it. From Marshall McLuhan’s The Medium Is the Message, with its assertion that television would blind humanity into a global village and end world conflict, to recent statements about the transforming power of the internet, we have heard that information technology will change not only how we work but who we are. One important point we will make in this book is that the medium is not the message, though it may strongly affect the message. The thing delivered is more important than the delivery vehicle. Having a telephone does not guarantee or even encourage brilliant conversations; owning a state-of-the-art CD player is pointless if you use it only to listen to polkas played by a kazoo ensemble. In the early days of television, many commentators said that the new medium would raise the level of cultural and political discourse in the nation, a prediction that clearly did not come true. The corollary for today’s managers is that having more information technology will not necessarily improve the state of information.
KNOWLEDGE: Most people have an intuitive sense that knowledge is broader, deeper and richer than data or information. People speak of a ‘knowledgeable individual,’ and mean someone with a thorough, informed and reliable grasp of a subject, someone both educated and intelligent. They are unlikely to talk about a ‘knowledgeable’ or even a ‘knowledge-full’ memo, handbook or database, even though these might be produced by knowledgeable individuals or groups.
Since epistemologists spend their lives trying to understand what it means to know something, we will not pretend to provide a definitive account ourselves.
What we offer is a working definition of knowledge, a pragmatic description that helps us communicate what we mean when we talk about knowledge in organizations. Our definition expresses the characteristics that make knowledge valuable and the characteristics–often the same ones–that make it difficult to manage well:
Knowledge is a fluid mix of framed experience, values, contextual information and expert insight that provides a framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of
knowers. In organizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices and norms.
What this definition immediately makes clear is that knowledge is not neat or simple. It is a mixture of various elements; it is fluid as well as formally structured; it is intuitive and therefore hard to capture in words or understand completely in logical terms. Knowledge exists within people, part and parcel of human complexity and unpredictability. Although we traditionally think of assets as definable and ‘concrete,’ knowledge assets are much harder to pin down.
Just as an atomic particle can appear to be either a wave or a particle, depending on how scientists track it, knowledge can be seen as both process and stock.
Knowledge derives from information as information derives from data. If information is to become knowledge, humans must do virtually all the work. This transformation happens through such C words as:
Comparison: how does information about this situation compare to other situations we have known?
Consequences: what implications does the information have for decisions and actions?
Connections: how does this bit of knowledge relate to others?
Conversation: what do other people think about this information?
Clearly, these knowledge-creating activities take place within and between humans. While we find data in records or transactions, and information in messages, we obtain knowledge from individuals or groups of
knowers, or sometimes in organizational routines. It is delivered through structured media such as books and documents, and person-to-person contacts ranging from conversations to apprenticeships.
Knowledge in action
One of the reasons that we find knowledge valuable is that it is close–and closer than data or information–to action. Knowledge can and should be evaluated by the decisions or actions to which it leads. Better knowledge can lead, for example, to measurable efficiencies in product development and production .We can use it to make wiser decisions about strategy, competitors, customers, distribution channels, and product and service life cycles. Of course, since knowledge and decisions usually reside in people’s heads, it can be difficult to trace the path between knowledge and action.
We’ve observed and analyzed over a hundred attempts to manage knowledge in organizations. To the managers of most of them we’ve posed the question, “How do you make the distinction between data, information, and knowledge?” Many make no hard distinction in practice, and most of these initiatives involve a mixture of knowledge and information, if not some data as well. Many pointed out that they just tried to add value to what they had–to move it up the scale from data toward knowledge.
Chrysler, for example, stores knowledge for new car development in a series of repositories called ‘Engineering Books of Knowledge.” The goal of these ‘books,’ which are actually computer files, is to be an ‘electronic memory’ for the knowledge gained by automobile platform teams. The manager of one such ‘book’ was given a series of crash test results for inclusion in the repository. However, he classified the results as data and encouraged the submitter to add some value. What was the context of the results–why were the crash tests performed? How about comparisons to the results of other models, previous years and competitors’ cars? What consequences did the results suggest for bumper or chassis redesign? It may be difficult to note the exact points at which data becomes information or knowledge, but it’s easy to see how to move it up the chain.
Knowledge can also move down the value chain, returning to information and data. The most common reason for what we call ‘de-knowledging’ is too much volume. As one Andersen Consulting knowledge manager told us, “We’ve got so much knowledge (not to mention a lot of data and information too) in our Knowledge Xchange repository that our consultants can no longer make sense of it. For many of them it has become data.” Aeschylus made a similar point clearly twenty five centuries ago: “Who knows useful things, not many things, is wise.”
Because knowledge is such a slippery concept, it’s worth reflecting a bit on some of its key components, such as experience, truth, judgment and rules of thumb.
EXPERIENCE: Knowledge develops over time, through experience that includes what we absorb from courses, books and mentors as well as informal learning. Experience refers to what we have done and what has happened to us in the past. ‘Experience’ and ‘expert’ are related words, both derived from a Latin verb meaning ‘to put to the test.’ Experts–people with deep knowledge of a subject–have been tested and trained by experience.
One of the prime benefits of experience is that it provides a historical
perspective from which to view and understand new situations and events.
Knowledge born of experience recognizes familiar patterns and can make connections between what is happening now and what happened then. The application of experience in business may be as simple as an old hand’s identifying a downturn in sales as a seasonal phenomenon and therefore no cause for alarm. It may be as complex as a manager’s noticing subtle signs of the corporate complacency that led to problems in the past, or a scientist’s having a sense of which new avenues of research will likely lead to useful results. These experience-based insights are what firms pay premiums for; they show why experience counts.
GROUND TRUTH: Experience changes ideas about what should happen into knowledge of what does happen. Knowledge has ‘ground truth,’ to borrow the phrase the US Army’s Center for Army Lessons Learned (CALL) uses to describe the rich truths of real situations experienced close up: on the ground, rather than from the heights of theory or generalization.
For obvious reasons, effective knowledge transfer is a critical issue for the army. Knowing what to expect and what to do in military situations can be literally a life-or-death matter. Ground truth means knowing what really works and what doesn’t. Experts from CALL take part in real military operations as learning observers and disseminate the knowledge they gather through photos, video tapes, briefings and simulations. Lessons learned in Somalia and Rwanda in the early 90s, for example, were passed on to the troops involved in the 1994 Haitian mission. The experiences of the first units in Haiti that went from house to house looking for weapons were also videotaped to provide guidance to those who followed.
A key aspect of the arm’s success at knowledge management was its ‘After Action Review’
(AAR) program. This exercise involves an examination of what was supposed to happen in a mission or action, what actually happened, why there was a difference between the two, and what can be learned from the disparities. Enlisted soldiers and officers meet together in a climate of openness, collaboration and trust. Results from the AAR are quickly incorporated into army ‘doctrine,’ or its formally documented procedures and training programs. The AAR program was developed not as a knowledge management vehicle but rather as a means to return to values of integrity and accountability. These values had suffered considerably during the Vietnam War, and army leaders adopted the AAR and an orientation to ground truth to restore them–initially in training missions, and latter for all types of missions. Over the past few years the army has realized that it had a knowledge and learning tool in the