Data even in the soup: but how to digest it?

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08/11/2020

At present, data seem to have gotten a high level of distinction in the world, or at least in some parts of it. This is, data seem to be more important than ever among human beings. But what should it be due to, if that were the case? So, in this way, we shall get near to data, answering that query and reviewing certain questions related to its antecedents, definitions, characteristics, consequences, benefits and limitations.

Thus, there would possibly be some factors that would at least be connected the distinction in question. By the way, two questions would at least have arisen regarding the up-to-date situation of data. So, the first question would be that some authors would already be talking about mankind could be working in a data society and economy [1]. While the second one would be related to the profession so-called Data scientist [2].

But what could those elements be? For instance, Internet, and its various usages by civil citizens, social networks on Internet, massive data sets available, digital devices with an unprecedented big storage capacity and a lasting need from human beings for finding out new supports so as to solve their daily matters.

On this occasion, however, data would not be a new support for the resolution of human beings questions, or at least for some of these. Since data would seem to have gotten to be among human beings from a long time ago. But when would data have appeared among such beings? The response, in this case, seems to be associated with symbolic thought [3], since this could be related to data. Aside, such a thought would allow us to perceive reality by means of representations and symbols, for example.

Hoffmann and others, in this sense, indicate that 115,000 years ago Iberian Neanderthals would already have made use of the symbolic usage of seashells and mineral pigments in order to resolve their everyday questions [4]. In such a way that it could be said that data would be as old as mankind, at least since the appearance of such men and women.

Otherwise, data have been meant in different ways by diverse authors from various areas of human endeavors [5]. For example, data can be understood from academic ambit as propositions which describe a part of a fact [6]. Consequently, according to this definition an example of data may be ‘Kevin likes chocolate ice cream’.

In this same way, data can be characterized by certain features, such as data unlike facts can be correct or incorrect, as well as true or false. Similarly, data would be constructed, but not facts, since facts would be either real ones or imaginary ones [7]. Nevertheless, there are other scholars who consider both data and facts would be constructed [8]. For example, Ludwik Fleck embraces a scientific fact is socially constructed [9].

In other respects, data have favored prediction and control of several natural phenomena. It has also favored either the identification of trends or the understanding of patterns related to human behavior, for instance. Likewise, it has also helped the elaboration of information and knowledge in a specific way, which in turn, have contributed to the solution of everyday questions in areas such as science and business [10].

The next two cases, thus, can show at least to some degree what it was said in the immediately preceding paragraph. So, the first case is related to a researcher who wants to know if time students from the ‘M’ classroom of ‘X’ High School dedicate to the study of the topics of their subjects shall be associated with their academic performance. To do that, such scientist uses a questionnaire so as to collect the following data: student A studies two hours per day, student B three hours and so on. Of course, all of such students shall be surveyed by researcher. 

So, that researcher can determine the mean (three hours) of data collected and, in this way, he can in turn produce the following information: ‘students from ‘M’ classroom of ‘X’ High School generally study the topics of their subjects three hours per day’. But not only that. Such a scholar can also create knowledge (once he has collected data regarding the performance in question and, in addition, has transformed it into information) such as ‘time spent by the students from ‘M’ classroom of ‘X’ high school in the study of the topics of their subjects is not associated with their academic performance’. By the way, the relationship in question in real life would be considered as an association of a complex nature [11].

Indeed, information can be defined as a meaningful data set [12]. While knowledge can be understood as the product that emerges from the relationship between information. By the way, it is also important to note that data, what contribute to the production of information, come from properties or attributes of a particular tangible or intangible object [13].

The second case, on the other hand, is framed in a business context. In this way, a new business has carried out several sales of its 'Z' product in its first month of operations. Previously, the business ran an advertising campaign. Then, its owner-manager drew up a sales report. This in turn allowed him to know ‘the whole amount of products he sold in the month in question’ (information), for instance.

What´s more, such a manager is an insightful guy and he likes to plan his business. Due to this, he did not only register sales by units and dollars, but he also attached certain data of his clients to the report in question. Now, he can inquire about other issues, which might go unnoticed by other individuals, in order to make better decisions for his business.

So, he examines ‘for each one of customers´ names’ (data) who bought products in the first month of activities of his business. Those data in turn involve some possible conclusions that can be clearly revealed in the information generated from it.

One of those information, thus, that can be inferred of the above mentioned data is that which could be formulated in the next way: ‘40.0% of the clients is made up by business owner´s relatives’. Therefore, owner-manager in question could produce with that information the subsequent conclusion: ‘possibly, my family members, who bought products in my business in this first month in question, or at least a significant proportion of them, would not never buy again in my business, since perhaps they only did it to give me a support in the beginning of my new entrepreneurship’.

That idea, by the way, in the field of business is not unreasonable if one consider individuals buy meanings and attitudes, but not products [14]. And, one of those meanings may just be the acquisition of a products by family members or friends, for example, in order to support the start of someone's entrepreneurship, but such an acquisition would not never happen again. Does that sound familiar to you?

Ultimately, the aforementioned questions, among others, would allow an individual, who is interested in working with data, to do so in an effective way.

[1]For more information, refer to Lammi, Minna y Pantzar, Mika (2019). The data economy: How technological change has altered the role of the citizen-consumer. Technology in Society, 59, 1-8.

[2]For more information, refer to Eastwood, Brian (2019). What Does a Data Analyst Do? Retrieved from https://www.northeastern.edu/graduate/blog/what-does-a-data-analyst-do/

[3]For more information, refer to Barnard, Alan (2012). Genesis of symbolic thought. New York: Cambridge University Press.

[4]Hoffmann, Dirk L. et al. (2018). Symbolic use of marine shells and mineral pigments by Iberian Neandertals 115,000 years ago, Science Advance, 4, 1-6; Hoffmann, D. L. et al. (2018). U-Th dating of carbonate crusts reveals Neandertal origin of Iberian cave art, Science, 359, 912-915.

[5]For more information, refer to Furner, Jonathan (2017). Philosophy of data: Why? Education for Information, 33, 55-70.

[6]Bunge, Mario (1999). Buscar la filosofía en las Ciencias Sociales. México: Siglo Veintiuno Editores.

[7]Ibid, Bunge, 1999.

[8]For more information, refer to Kreimer, Pablo (1999): De probetas, computadores y ratones. La construcción de una mirada sociológica sobre la ciencia. Bernal: Universidad Nacional de Quilmes.

[9]Fleck, Ludwik (1981) [1935]. Genesis and Development of a Scientific Fact. USA: The University of Chicago Press.

[10]For more information, refer to https://es.datachile.io/

[11]Gromada, Anna and Shewbridge, Claire (2016). Student Learning Time: A Literature Review. Education Working Papers No. 127. Retrieved from https://www.oecd-ilibrary.org/docserver/5jm409kqqkjh-en.pdf?expires=1604778534&id=id&accname=guest&checksum=20D341DDE8D7B58A91FBE170F194ADB2 

[12]Floridi, Luciano (2005). Is Semantic Information Meaningful Data? Philosophy and Phenomenological Research, LXX, 2, 351-370.

[13]Ibid, Bunge, 1999.

[14]For more information, refer to Chaín Palavicini, Magali (1998). La mente del estratega como líder del factor humano en un sistema de calidad. Adminístrate Hoy, V, 50, 19-24; Kotler, Philip y Keller, Kevin (2012). Dirección de Marketing. México: PEARSON EDUCACIÓN.

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