For this week’s reading, I found the “Conceptual Approaches for Defining Data, Information, and Knowledge” of Chaim Zins is quite inspirational.
In this article Zins argues identifies the concepts and concretes of “data”, “information” and “knowledge” with a semantic method. With Zins’ concept, the data are existing symbols, the knowledge is embodiments and the information is just like something “in-between”. This reminds me an idea of McLuhan that “the content of a medium is another medium”. The stream of data forms information, and then the intention of human beings to learn transfers it to knowledge.
Here, the concern is rising. The intention to conduct data, to categorize them, to utilize them for learning is the key point in transferring pure “mechanical” data into “knowledge”. Without this intention and related procedures, data are just bunches of dead existence. Then think about this “big data” age. Data are now so popular among different domains. Data flooding is a type of “fashion” in current digitalized social environment, but on the other hand it could also become a disaster if we failed to control and handle it, just as the real flooding. If we collect data but do not process them, big data would just become redundant archive. If we do process them but do that in an improper way, data could be misused and guide us to wrong path. Comparing with the collecting of data, I think what’s more important is to enhance the operation and flow of data, such as data categorization and analysis. In a word, data should be stored in an accessible and usable situation in order to activate its potential of transferring into knowledge.
I remember that Neil Postman had expressed a negative attitude towards doing social research with data analysis in Technopoly. Yet in my opinion, if we have the intention and ability to activate the big data stream, data analysis could also be a very pertinent method in the research of social science, especially in the domain of digital media and communication studies.