“Data knife”? What data knife?!

In German we have a nice saying: “Auf Messers Schneide” reads as “on the (cutting) edge of the knife” or maybe rather on its blade. The phrase indicates a delicate situation. A situation that can turn out in one or the other result, but will never be in between if you cannot stabilize it on the edge. On the very tiny-tiny sharp line that cuts like a ridge in the mountains one slope from the other.

In Philosophy the knife is also the classical example for a dual use problematic: You can stab someone with a knife or you can cut a pineapple with it; the different goals are contradictory as the mountain sides that fall from the ridge. In the fields of natural science and technology the term “dual use” has become mostly known for the development of knowledge that can be applied for military purposes though theses purposes would not be intended by the initial research. In both cases, the knife or the weapons (bringing war or peace), the value of the item does not depend on itself, but on the agent that drives it. Therefore the description of an item and the ethics how to use it need to be differentiated.

Another example for dual or rather triple use is medication or poison or drug. Here we even have different words depending on the use of the same substance: it depends on the quantity if the substance heals or harms. Compared to the knife, one more capacity comes here into the picture of dual use techniques: It’s not only the agent and her motivation, but also the amount or constitution of the item.

We have a similar situation with the data revolution we are experiencing in these times. Data is also an example for a dual use item turning to one or the other side of the edge. And this is why I chose the title “on the edge of the data knife”. The collection of data and the analysis of data can be used in favor of the people as well as against them.

We have seen how protesters organized support structures and demonstrations during the so called “Arabic Spring” 2011 with social networks (Khondker 2011). And we observe similar movements nowadays during the protests in Hongkong in Summer 2019 (Schmidt 2019). On the other hand governments request messenger services to hand out information on communication and connections of their citizens. Luckily, in the case of Iran protests in 2015 the messenger Telegram rejected the attempt by the regime to spy activists (BBC 2015). We also witness how companies like Facebook and Cambridge Analytica use those techniques for personal optimized advertisements and so called “dark ads” (personalized campaigning) that also influenced elections not only in the USA election 2017 but also in India, Kenia and others (Kreysler et al. 2019).

So, how to react to those developments? We could throw away all kinds of consumer cards, user accounts, social network representations, messenger and tracking apps, email-accounts and try to live an analogue life. Frankly, I’m convinced future punks will go further in this direction with all my sympathies. Yet, I don’t believe this will work out if you want to remain a part of mainstream society. We cannot escape the data driven world we just entered. However, within the brave new data world we do not only act as data producers. We can also analyze the data ourselves we cannot run away from.

Because quantitative data analyses render social realities in statistics, rankings, indicators or networks they have to be utilized with caution. Numeric models of reality legitimize political decisions and contribute to the reproduction of social conditions (Angermuller/van Leeuwen 2019). However, if they do so, these models could also justify and substantiate a development towards an emancipatory orientation of society.

Maybe this blog takes too early position. As it also accompanies the development of a data analysis tool it already answered the question if we should stay and try to deal with the data techniques without contributing to the “automation of inequalities” (Eubanks 2018) . “Playing the game” of quantitative techniques in order to understand how they work seems for now to be preferred over backing up off a game we cannot exit. Furthermore, understanding how techniques of data analyzing work is the very condition to criticize and to deal with them deliberately.


Angermuller, Johannes, and Thed van Leeuwen. 2019. “On the Social Uses of Scientometrics: The Quantification of Academic Evaluation and the Rise of Numerocracy in Higher Education.” In Quantifying Approaches to Discourse for Social Scientists, ed. Ronny Scholz, Cham, 89–119.

BBC. 2015. “لگرام ‘پس از عدم همکاری با دولت ایران موقتا مسدود شد’ (Telegram ’temporarily blocked after non-cooperation with Iranian government’).” BBC Persian. https://www.bbc.com/persian/iran/2015/10/151019_u04_telegram_iran (October 8, 2019).

Eubanks, Virginia (2018): “Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor.” New York.

Khondker, Habibul Haque. 2011. “Role of the New Media in the Arab Spring.” Globalizations 8(5): 675–79.

Kreysler, Peter, Kapohl, Matthias, and Schiller, Wolfgang. 2019. “Digitale Brandbeschleuniger Der unregulierte Wahlkampf im Netz.” Deutschlandfunk. https://www.deutschlandfunkkultur.de/der-unregulierte-wahlkampf-im-netz-digitale.3720.de.html?dram:article_id=456530.

Schmidt, Fabian. 2019. “Hongkong: Wie mit Apples AirDrop die Demonstrierenden in Hongkong mobilisiert werden – Gerechtigkeit – Bento.” https://www.bento.de/politik/hongkong-wie-mit-apples-airdrop-die-demonstrierenden-in-hongkong-mobilisiert-werden-a-3075b1aa-06fc-4980-b6c0-e5cdd7f19da7 (October 8, 2019).

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