The Data You Eat Defines Who You Are by ralucaelenab

Sociologist Deborah Lupton illustrates how the digital data we consume and produce can tell a lot about our personal identity and how it can shape our future behaviour. Do you want that data define your identity?

Copenhagen, March 16, 2017: On a daily basis, each one of us browses the Internet and consumes data from various sources: social media, news, entertainment, ads and recommendations. It turns out that what we do read or view can tell a lot about our preferences and feelings and it can easily expose things even us do not know about ourselves. Who has the power to control this data and what does it happen to it after we consume it? Is there a way to approach it and establish a productive relationship with it?

 There is a growing concern and interest among citizens about digital data, especially about data privacy and security. However, little is talked about the ways people interact, understand and perform data assemblages. Deborah Lupton’s  journal article strives to shed some light on how sociocultural theories can help understanding the relationship between humans and digital data, while raising a series of questions every data ‘consumer’ should think about.

Lupton argues how data became ‘companion species’, which is a term used by Donna Haraway to describe the relationships that human species has not only with animals, but also with technology. Haraway claims that both humans and nonhumans are not pre-established, but they emerge through their interactions and they are co-evolving. What makes human species companion with nonhumans they engage with is that each species is learning from and influence each other.

Based on Annemarie Mol’s work, Lupton states that ‘the human subject may be conceptualized as both data-ingesting and data-emitting in an endless cycle of generating data, bringing the data into the self, generating yet more data’. Our daily environment is data-saturated and we can also individually wear personalized data-generating devices (smartphones, sensor embedded wristbands or watches).

Our relationship with the digital data assemblages around us is imminent and Haraway’s work recognizes the importance of learning to develop a productive relationship and realize the mutual dependency between human species and digital data.

A common example of how data can influence our behaviour can be drawn from our exposure to commercials in the online medium. Ads are not randomly displayed on our screen anymore, but they are based on previous data we shared, data that turned us into prospects for companies that have something to sell. Our actions, of course, vary in relation to what we see, but we do act on it.

The way humans engage and generate data every day raises questions about the human agency and subjectivity. Lupton relates the data consumption with the action of eating an apple taken from Mol’s work. We can choose what kind of apple to eat, but after that we have no control over what the apple becomes in our body. The same happens to digital data. What we consume, be it food or digital data, becomes part of us and it does have the power to influence our behavior.

Can we identify the digital data we consume? What happens to the data that we generate and what does it say about us? Do we recognize some of the data as appropriate for consumption and others not appropriate? What is our individual ‘yum’ and ‘yuk’ regarding our digital data ‘ingestion’? To what extent are we able to control it?

While an intense interest in digital data is visible these days, we still do not know much about how and why people interact with the digital data they generate. In her journal article, sociologist Deborah Lupton looks at Donna Haraway and Annemarie Mol’s work in the field of science and technology studies to demonstrate how their concepts have much to offer to critical data studies.

Hopefully this article raised some curiosity about what happens to data when we produce it and what does it do to us.
For more in-depth information read Deborah Lupton’s journal article Digital companion species and eating data: Implications for theorising digital data-human assemblages available at

Data as Relation – Peer review by Raluca Elena B


The article challenges the presumption that Big Data provide the best information for social scientists and is built on existing literature and studies of the topic. The authors critically interrogate this phenomenon, taking into account its limitations, subjectivity and the ethical issues involved.


The overall structure and language of the article supports the arguments the authors make and the text is generally clear to the target audience. Highlighting the six provocations by dividing the text into sections with title, gives a good idea about what the author will discuss next. It seems to me that the beginning of the paper is uncertain and I cannot decide if it is meant to be an introduction or an abstract; either way, the article begins with exactly the same text and it is better to pick one or the other. The article would also benefit if it had closure – a summary of further possible studies/discussions, as now it leaves the impression that it is not finished.

The major problem with this article, in my view, is the transitions between one idea to another. For example, on page 668 – “Just because Big Data presents us with large quantities of data does not mean that methodological issues are no longer relevant” – seems confusing in relation with the beginning of the paragraph. I think that there should have been mentioned before if methodological issues are considered as being relevant or not in the context of Big Data.

On page 655 – “We also recognize… it is time to start critically interrogating this phenomenon, its assumptions, and its biases” – I it find more suitable in the introduction.

On page 664, the last paragraph mentions “some significant and insightful studies”, but it does not name the studies. I think it would be good if the readers had a source to the studies referred to, unless they are hidden from the public.

This process is inherently subjective” (page 667) – I think it requires more elaboration into why is it subjective or an example of where data interpretation was subjective based on the decisions made by the researchers.

The example given on apophenia (page 688) requires more elaboration or maybe another example. I think that “apophenia” is an interesting issue related to Big Data and it would be good if the text provided more information about it.

Again on page 668,  the paragraphs “Twitter provides an example…” and “Twitter does not represent ‘all people’…” can constitute one single, short paragraph. There is no need to mention that scholars choose Twitter data “because it is easy to obtain”, as it does not bring any value to the arguments presented. Again, I identified a transition problem, where the word ‘people’ is introduced before the author describes what it refers to, which creates confusion.