Algorithms, Humans, and the Duality of Data–A Futile Attempt to Define IS Phenomena

Aleksi Aaltonen
3 min readAug 31, 2023


My PhD supervisor Jannis Kallinikos once said that a field of research is defined by the phenomenon it studies, while an academic discipline constructs its object of research. By this definition, the study of information systems is merely a field, even if we as IS scholars often like to call ourselves a discipline...

I have a huge respect for academic disciplines such as economics but, at the same time, I am happy to work on a field that is in a perpetual and, indeed, seemingly hopeless chase of its phenomena. If you ask from ten IS scholars what makes something a study of information systems, you probably get ten different answers. Here is mine:

Information systems studies phenomena that are always, in one way or another, about algorithms, humans, and data.

Let me elaborate.

All digital information systems are made of algorithms. The study and development of algorithms as technological entities belong primarily to computer scientists and engineers. There are great scholars (and friends!) in the field of information systems who develop algorithms, but as a field our advantage is typically to put algorithms in context. For instance, the study of algorithmic management strives to understand how management and work changes when managerial action is taken by algorithmic procedures.

While algorithms are certainly a very important topic to study, there is a downside to the rise of evermore powerful algorithms and models. They tend to summon vague and often naive talk about ‘autonomous’ systems as if digital systems would be at the verge of taking a life on their own. Yet, no computational system is autonomous in the sense of operating independent of humans. Take, for instance, generative artificial intelligence. The generation of text and images is triggered by human prompting, not to mention that the systems are designed, trained, and tuned by humans. The freely available version ChatGPT has been trained with data until September 2021 — unless some humans decide to feed the system with more training data, the system will soon start showing signs of serious dementia.

Finally, data are the world in which algorithms live. Data constitute the fundamental link between the social world of humans and algorithmic procedures. In this sense, data are both technological entities, digital material, so to speak, and semantic entities that can be used to make sense of something. This duality makes data a particularly interesting element of information systems. Any attempt to use digital data are constrained by the way the data are designed, produced, and stored in practice, yet data also need to be embedded in specific human practices and institutions to function as data.

So what?

The trinity of algorithms, humans, and data usefully highlights the importance of keeping in mind that all three spheres are always involved. Actual research efforts often emphasize one sphere over the others, which is fine as long as we do not deny the existence of the others.

For instance, better awareness of all three spheres could help putting zombie concepts like ‘autonomous systems’ out their misery (1), or act as a reminder to those who wish take into consideration the interpretive flexibility of technology (or any of its numerous descendants) that any interpretation needs something to interpret, and that that something constrains the interpretation (otherwise one is not interpreting anything but merely fantasizing).

On a more personal level, to me it is interesting how data sits in-between the sphere of humans and the sphere of algorithms, highlighting what could be called the duality of data: digital data are always technological and social matter. To this end, the inability of information systems research to develop a strong disciplinary core can be seen as its strength — the field has remained open to bringing together the understanding of storage technologies, conceptual modeling and semiotics, and social practices in a way that allows us to do cutting edge research into some of the most exciting contemporary data-based phenomena.


(1) A zombie concept is an idea that refuses to die despite being repeatedly refuted. See, Peters, B. G. and Nagel, M. L. 2020. Zombie Ideas: Why Failed Policy Ideas Persist. Cambridge University Press.



Aleksi Aaltonen

I am a management scholar and thinker who writes about data and the production of academic knowledge —