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Organizing discourses

When data hits the organization

Thomas Ramge joined us for our international Strategy Meeting at Metaplan to discuss the socio-political effects of big data, having debated the matter with his co-author, Oxford Professor Viktor Mayer-Schönberger, in their bestseller, “Reinventing Capitalism in the Age of Big Data”. Ramge’s talk provided much food for thought regarding the extent to which big data will change organizations.

In the age of big data, artificial intelligence and learning algorithms markets will be structured by data: Thomas Ramge, a pioneer in the field of data policy, and Oxford Professor Viktor Mayer-Schönberger, author of the bestseller “Big Data: A Revolution That Transforms How We Work, Live, and Think”, are both proponents of this bold proposition. In “Reinventing Capitalism in the Age of Big Data” they sketch out a vision that raises socio-political questions and at the same time provides answers.

Mayer-Schönberger and Ramge describe how data, at least partially, takes over the informational function of money. The link between supply and price is no longer as strong as it has been. Due to a wealth of data, the informational function of money has partly been replaced by a matching of detailed preferences and priorities, urgency and availability. Three key technologies make this development possible: a) data ontologies, which help information flows to be categorized and made accessible; b) machine learning, i.e. methods for recognizing preferences, and c) matching algorithms as a decision support system which learn as they adapt to us and could even become similar to us. Driven by these technologies, markets are about to make a fresh start in a way that could benefit all stakeholders.

However, according to this analysis, an abundance of data not only has effects on markets, but more importantly on organizations such as companies and corporations. The proposition suggests that organizations have two possible strategies to adapt to these effects internally: they can automate their decision-making processes, since more and more networked data is available in real time; or they can set up their internal structures to be like markets in themselves, e.g. by competing for the allocation of resources. The organization becomes steadily less important as a traditionally hierarchical mechanism of order.

It is precisely these assessments of the future of the organization that have inspired us to open up the discourse, which has so far primarily only described these topics in abstract terms. We wanted to include concrete organizational references. As an organizational consultancy deeply rooted in a tradition of organizational theory, we argue that the organization needs to be taken into account as an independent system in this area. As a result, we’ve come up with the following four propositions that combine data and data-driven effects with the core business of organizations – decision making:

1. “More data = more rationality” – We disagree!

2. People overestimate the role of data in decision-making situations!

3. Informal loopholes don’t disappear when data comes into play – they are covered up more thoroughly!

4. Power games will not vanish due to increased transparency through data – they will shift!

Each of these propositions places the role of data back within its sphere of influence within organizations and therefore offers clear starting points for organization-related debates.

Stay tuned: We will be publishing short articles on each of these propositions in the coming weeks.






is Partner at Metaplan Germany and works as sociologist at the University of Potsdam. In consulting as well as in research, her focus is on leadership and digitalization.


is Partner at Metaplan Germany and a keen advisor in strategic planning and the intra-organizational powerplay.


is Partner at Metaplan in Princeton, NJ; he focuses on cross-functional leadership and co-creation processes.

Ein Kommentar zu “Organizing discourses

  1. Marius Alexander Schulz   17. September 2018 at 11:37 - Reply


    I´ve wrote a small case study about the relevance of right modelling in Data Science. Actually, there is a huge problem with algorithms for best-fit estimations: they produce errors, if you dont check the model for making sense.

    Here´s the case study article: https://data-science-blog.com/blog/2018/08/29/modelling-data-case-study-importance-of-domain-knowledge/ .


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