Data versus Information (or What I already know about knowledge, part 1)

Out of innocence or perhaps over-ambition I thought of discussing the fundamentals of knowledge. On my last post that was my first intention but, after looking a bit more into it, I’ll have to take it back. Nevertheless, some distinction and a working definition of knowledge are needed to deal with the subject.

The reason behind this simplification is the same present in Davenport and Prusak (2000) and Lundvall and Johnson (1994): the fundamentals of knowledge discussed today go back to Plato’s definition of it as a “justified true belief”, one that has been questioned often (Wikipedia/Knowledge). I have no intention of getting in a philosophical debate for two reasons. First, I’m no philosopher and my contribution would be very little. Second, it would deviate from the focus of my thesis, which requires only a working definition of knowledge and distinction between different types of knowledge.


First, it is necessary to understand the distinction between data, information and knowledge. The first two I deal with in this post. Their distinction to knowledge and different types of knowledge will be presented on a future post.

“Data is a set of discrete, objective facts about events” (Davenport and Prusak, 2000), and better explained through an example. When someone buys something, the receipt offers a set of data. What items were bought, at what time and date, and how much did it cost, for example. Those are all examples of data.

Data differs from information. Peter Drucker made this distinction: “data is endowed with relevance and purpose” (apud Davenport and Prusak, 2000). According to him, data has no relevance or purpose. It simply carries objective facts with no whys. Information on the other hand is meant to have some impact on the receiver’s understanding of events. According to Davenport and Prusak (2000), “it’s data that makes a difference”. And to turn data into information, the authors suggest five methods:


  • Contextualized: to tell the reason why the data was gathered for;
  • Categorized: to explain the units of analysis or key components of data;
  • Calculated: the data went through some mathematical or statistical analysis;
  • Corrected: errors have been removed from data; and
  • Condensed: the data was summarized.


The outcome for this distinction is that data can be easily stored into computer systems while information, to be called so, needs to go through some human analysis. Storing data requires only that the amount stored does not affect the speed with which relevant data can be accessed. On the contrary, humans are indispensable for turning data into information, even though the process can be highly supported by computer systems. Adding meaning to data can only be done by humans.

REFERENCES:

DAVENPORT, Thomas; Prusak, Laurence (2000): Working Knowledge: How organizations manage what they know. Boston: Harvard Business School Press.

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