One way of viewing possible relationships between data, information and knowledge is to consider an information system from the perspective of an IPO (input-process-output) model. On the input side we have data, as discussed previously. These data are then massaged or manipulated in some way (e.g., sorting, summarizing, filtering, formatting) to obtain information. Note that the transformation of data into information may be completed by a person (e.g., using a calculator) or by a computer program, although for our purposes we are typically more interested in situations where computer programs are employed.
A simple example could be the “what if” type of analysis that an electronic spreadsheet package offers. We can use our current understanding of a situation to develop a model of how sales will go up or down by a certain factor based on the amount we spend on advertising and other factors such as price.
The resulting information is used by a human to reach decisions (how many people to hire, how many products to produce, how much to spend on advertising). The outcomes of these decisions are observable results, such as sales volume during a certain time period, or the number and size of back-orders, etc. If these objective outcomes (results) are monitored and examined, then knowledge may be gained (e.g., how to avoid inventory shortages, or how to balance inventory carrying costs against costs associated with product shortages).
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