Knowledge is distinct from data and information. People absorb both data and information to develop knowledge. They also transform data into information using knowledge.
Here’s an example of the data-information-knowledge connection:
A policy analyst is asked to determine whether a policy change regarding the marketing of B.C. wines has had a positive effect on the industry. She starts by reviewing a table of numbers on the sale of B.C. wine in the province over the last three years. The table contains data.
The analyst reads these numbers, and creates a trend line that shows that B.C. wine sales have steadily increased over the three year period but most notably after the policy was implemented. The data has been transformed to information.
To come up with possible explanations for the increase in sales, the analyst considers additional information, speaks with known wine producers and distributers, and consults with an expert she met at a conference. After examining all this information and using her own knowledge and judgment, she concludes that the efforts to market local wine expanded significantly because of the policy change and this in turn has positively influenced sales.
In this scenario, the policy analyst used both existing knowledge and generated new knowledge. She applied her “know-how” to reach an understanding of a particular issue. By turning data into information, using her existing knowledge to tap into stakeholders and other information sources, considering the industry knowledge obtained from those sources, and reflecting on all of this, she came to her conclusion. In effect, she generated new knowledge (Davenport and Prusak 1998).