When we talk about measurement, we must understand how knowledge differs from data and information.
In an informal conversation, the three terms get often used interchangeably, and this can lead to a free interpretation of the concept of knowledge. Perhaps the simplest way to differentiate the words is to think that the data get located in the world and experience is located in agents of any type, while the information adopts a mediating role between them.
An agent does not equal a human being. It could be an animal, a machine or an organization constituted by other agents in turn.
Data
A data is a discrete set of objective factors about a real event. Within a business context, the concept of data gets defined as a transaction log. A datum does not say anything about the way of things, and by itself has little or no relevance or purpose. Current organizations usually store data through the use of technologies.
From a quantitative point of view, companies evaluate the management of data regarding cost, speed, and capacity. All organizations need data, and some sectors are dependent on them. Banks, insurance companies, government agencies, and Social Security are obvious examples. In this type of organizations, good data management is essential for their operation, since they operate with millions of daily transactions. But in general, for most companies having a lot of data is not always right.
Organizations store nonsense data. This attitude does not make sense for two reasons. The first is that too much data makes it more complicated to identify those that are relevant. Second, is that the data have no meaning in themselves. The data describe only a part of what happens in reality and do not provide value judgments or interpretations, and therefore are not indicative of the action. The decision making will get based on data, but they will never say what to do. The data does not say anything about what is essential or not. In spite of everything, the info is vital for the organizations, since they are the base for the creation of information.
Information
As many researchers who have studied the concept of information have, we will describe it as a message, usually in the form of a document or some audible or visible communication. Like any message, it has an emitter and a receiver. The information can change the way in which the receiver perceives something, can impact their value judgments and behaviors. It has to inform; they are data that make the difference. The word “inform” means originally “shape” and the information can train the person who gets it, providing specific differences in its interior or exterior. Therefore, strictly speaking, it is the receiver, and not the sender, who decides whether the message he has received is information, that is if he informs him.
A report full of disconnected tables can get considered information by the one who writes it, but in turn, can be judged as “noise” by the one who receives it. Information moves around organizations through formal and informal networks. Formal networks have a visible and defined infrastructure: cables, e-mail boxes, addresses, and more. The messages that these networks provide include e-mail, package delivery service, and transmissions over the Internet. Informal networks are invisible.
They are made to measure. An example of this type of network is when someone sends you a note or a copy of an article with the acronym “FYI” (For Your Information). Unlike data, information has meaning. Not only can it potentially shape the recipient, but it is organized for some purpose. The data becomes information when its creator adds sense to it.
We transform data into information by adding value in several ways. There are several methods:
• Contextualizing: we know for what purpose the data were generated.
• Categorizing: we know the units of analysis of the main components of the data.
• Calculating: the data may have been analyzed mathematically or statistically.
• Correcting: errors have been removed from the data.
• Condensing: the data could be summarized more concisely. Computers can help us add value and transform data into information, but it is tough for us to help analyze the context of this information.
The widespread problem is to confuse information (or knowledge) with the technology that supports it. From television to the Internet, it is essential to keep in mind that the medium is not the message. What gets exchanged is more important than the means used to do it. Many times it is commented that having a phone does not guarantee to have brilliant conversations. In short, that we currently have access to more information technologies does not mean that we have improved our level of information.
Knowledge
Most people have the intuitive feeling that knowledge is something broader, deeper and more productive than data and information. We will try to make the first definition of knowledge that allows us to communicate what we mean when we talk about knowledge within organizations. For Davenport and Prusak (1999) education is a mixture of experience, values, information and “know-how” that serves as a framework for the incorporation of new skills and knowledge, and is useful for action. It originates and applies in the minds of connoisseurs. In organizations, it is often not only found in documents or data warehouses, but also organizational routines, processes, practices, and standards. What immediately makes the definition clear is that this knowledge is not pure. It is a mixture of several elements; it is a flow at the same time that it has a formalized structure; It is intuitive and challenging to grasp in words or to understand logically fully.
Knowledge exists within people, as part of human complexity and our unpredictability. Although we usually think of definite and concrete assets, knowledge assets are much harder to manage. Knowledge can be seen as a problem or as stock. Knowledge is derived from information, just as information gets derived from data. For information to become knowledge, people must do practically all the work.
This transformation occurs thanks to
• Comparison.
• Consequences.
• Connections.
• Conversation.
These knowledge creation activities take place within and between people. Just as we find data in registers, and information in messages, we can obtain knowledge from individuals, knowledge groups, or even in organizational routines.
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