Transforming data into knowledge


Potentially the interpretation of data into information is a very complex issue. The figure (below) highlights that the interaction between data interpretation and knowledge drives the transformation from data to information. Humans achieve this relatively easily and make use of a variety of means:

  • cultural background
  • unconscious intuitions,
  • concrete memories or similar observations of the past,
  • expectations (depending on context),
  • text book knowledge, and
  • domain dependent heuristic rules.

Computers cannot do such things as easily, although advances have been made in the fields of artificial intelligence, this level of sophistication is currently beyond the scope of knowledge based computational systems. This does however, highlight the ways in which information is created by putting data into context through interpretation.

Image of: Fig 1. The role of knowledge in driving the transformation process

Fig 1. The role of knowledge in driving the transformation process


Elaboration occurs once data has been interpreted as information. This may be regarded as a secondary interpretation process. Information may be elaborated on for two reasons, these are:

  • so that the information can be better understood, and
  • for deriving, or inferring new information from current information.

Inferred information includes:

  • additional problem features,
  • generated hypotheses,
  • consequences of hypotheses,
  • suggested solutions to problems,
  • explanations and justifications of suggestions, and
  • critiquing arguments.

If we consider that the interpretation of data into information is a form of pre-processing, then during decision making frequent interaction occurs with the environment and this in turn blurs the overall picture. Therefore a more realistic view of this is that in a decision making process, elaboration and data interpretation are often interleaved.

Learning as knowledge acquisition

A systems knowledge grows and changes depending on interactions within its environment. The process of change of knowledge is generalised as learning. One particular view is that: learning is the integration of new information into an existing body of knowledge, in such a way that makes it potentially useful for later decision making.

Where does knowledge come from?

Knowledge within a system can be derived from two sources:

  • as integrated new information via the process called learning, and
  • as a result of an inference process. This is where knowledge can use existing knowledge to infer further knowledge.

These two processes are visualised in the above figure.

A definition of knowledge

Knowledge is information that can be used within a reasoning process. In practice knowledge can be viewed in a number of ways, knowledge:

  • can be viewed as an integrated totality such that it consists of a connected network of interrelated subcomponents that give it its power of:
    • data interpretation,
    • information elaboration, and
    • learning.
  • is the outcome of a learning process which links knowledge to its potential use (as we have stated the purpose of knowledge is to be used in a reasoning process), or that learning as a process is always related to a purpose.
  • is flexible such that when it is acquired within a particular context it can be re-used dynamically in different situations.
  • is: that which we come to believe and value based on the meaningful organized accumulation of information through experience, accumulation or inference.
  • is information incorporated in an agents reasoning and made ready either for active use within a decision process or for action. It is the output of a learning process.

As we have shown knowledge is an active concept about action knowledge must be used for some purpose. From a practical point of view it would seem that this is intuitively correct, since knowledge is key to all the transformational processes such as interpretation, elaboration and learning.

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