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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.

Questions about knowledge and learning

I present first part of my answer to Carlos’ comment on my last post:

Indeed the container shipping technology transformed the transportation of goods and made it easier, faster, and cheaper. Also, the commercial airlines did for people transportation what container shipping did for goods. The internet is responsible for information transfer which allowed control and management from great distances. However, I want to identify what technologies and tools (most likely internet-based) allowed the transfer of knowledge aiming at innovation developments.

What I wanted to show on my last post is that the focus of this thesis will be on finding and analyzing the internet tools that allow communication, interaction and development of innovation processes across international and firm borders, geographical spread of economic activities within the same effort.

However, in order to understand knowledge sharing, it is needed to understand 2 main questions: what is knowledge and what types of knowledge are relevant for the innovation process? I pose these questions first because analyzing effectiveness of knowledge sharing tools across distances will only be sustainable if first the essence of knowledge transmission is discussed. In other words: what are the particularities of each type of knowledge regarding its transfers? What makes knowledge more easily transferred?

Of course these questions will require a different set of reading than the ones I’m used to. As Lundvall and Johnson (1994) say, economists have little to say about “what is knowledge”. And that question may be too fundamental for a thesis that will use it in a practical sense. Anyhow it is needed to understand its concept and particularities in order to criticize tools that aim at facilitating knowledge transfers – the process known as learning.

So here I make a request for those of you who understand and know sources where I can gather some answers. Please, leave comments with some authors, books, articles and other sources that you know are related to these questions.

References:
LUNDVALL, B.A; Johnson, B. (1994): The Learning Economy. Journal of Industry Studies, Volume 1, Number 2.