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From Uncertainty Regarding Probability to Dataism

An exposé on three generations of risk management

Ever since human thought and action have been handed down from generation to generation – both individually and collectively – importance has been placed on knowing or not knowing about the future. The word “uncertainty” has become a linchpin term for defining the nature and concept of risk, not only in ISO standards but also in many other areas. There are numerous other definitions of risk, which happily coexist and complement one another. Dr. Peter Meier, Steinbeis Entrepreneur at the Steinbeis Transfer Center for Risk Management, and management consultant Munok Kwon take a look back and forward at risk management for TRANSFER magazine, offering a personal and highly critical judgment of the discipline.

In the course of his work, Peter Meier uses a definition that revolves around the value and statistical aspects of risk, which he describes as a “future, uncertain, and negative value-position.” This definition makes risk measurable and manageable, just like other numbers. With their differentiated views, risk managers deal with risk in keeping with DIN standard ISO 31000:2018, which provides guidelines on managing risk. This contrasts with people in commercial roles, who see risk from their integrated perspective. This reflects the dual nature of the “venture” of trading, hand in hand with unavoidable speculation regarding the future and corresponding impacts on the bottom line.

Risk management over the ages

In the early 18th century, the Enlightenment brought revolutionary change to Europe with concepts of logical and mathematical statistics. Gaussian distribution became an important tool of risk assessment and management. This contrasts to China, where logical and mathematical views of the future were used to gauge the probability of different scenarios as early as the 5th century BC. While Europeans were still seeing natural phenomena as indications of supernatural and divine forces, the philosopher and sociologist Confucius and his contemporary, military strategist General Sun Tzu, were already establishing the fundamental concepts for the predictability of the future.

Fast-forward to the present day, and statistical views of risk are still dominated by predictions of future scenarios based on numerical simulations in combination with random numbers from the casinos of Monte Carlo. The crucial issue is which and how many numbers are subject to unfavorable or favorable forces. In 2007, the German philosopher Ludger Heidbrink conducted a comprehensive analysis of people responsible for decisions and actions against the backdrop of such high levels of uncertainty, viewing this as a feature of culture, one that is both contemporary and timeless, albeit changing. This is linked to the delegation of responsibility within organizations, as well as the classic practice of weighing up trust and control in relationships. This problem is amplified by the current situation, in which multiple crises are happening at the same time, and there is a systematic search for certainty through information and knowledge, in order to compensate for risk through positive and certain numbers (Meier, Kwon, 2020).

Risk management in the future

With all people, things, and non-things being digitized as part of the big data concept, we are currently witnessing a new, still incomplete, and revolutionary development, bringing with it the next level of managing uncertainty. Referring to big data in an article in the New York Times in 2013, journalist David Brooks coined the term data-ism as a synonym for a digital phase of the information society. But we also see anti-enlightenment within this change – the next cultural revolution, which is sweeping through all sections of society – in technological, social (including cultural and political), financial, and legal terms. This applies to both private individuals and the economy.

Whereas uncertainty characterizes the age before the Enlightenment and probability characterizes the age after the Enlightenment, dataism describes the current age. Previously, it was predominantly stories and words that were told and interpreted – thus verbally expressed prosaic and lyrical “narratives” – whereas now we predominantly witness the counting and calculation of numbers and characters, and thus written symbolic and logical “numeratives.” To be part of those numeratives, humans equipped with natural intelligence need machines, which will soon be equipped with artificial intelligence. As such, humans are thus dependent on technology. Human beings are therefore relinquishing human intelligence and competence in a state of nirvana, a misunderstood digital system to which we have delegated responsibility for our own decisions and actions (Han, 2012).

Transparency and objectivity are only promised, but not delivered. As with any form of technology, the dual use of things digital is the new normal. Whereas people used to say knowledge is power, now they say data is power, which raises fundamental questions regarding our rights to data and the legitimacy of power based on it. In our opinion, admittedly critical, dataism is returning human beings to a new form of immaturity – one self-created, for which we ourselves are responsible.

Seeing risk as the prospect that there will be uncertain and negative numbers in the future has not yet been fully comprehended or defined within the context of big data. Learning AI algorithms (predictive data mining) are currently being used to trawl through big data, and data gathered over time (timeline data mining) is being analyzed within the context of learning. Qualitative data structures and the quantitative features of data patterns relating to factual and personal circumstances are being examined, interpreted, and applied to correspondences between the yesterday of the past, the today of the here and now, and the tomorrow of the future. Big data does not ask for the conditional and causal theories that affect if-then-perhaps; everything can be found in relevant data – it’s all data. Nobody is interested in standard distribution curves anymore, but everyone still wants the data. And thus all kinds of organizations now run their own situation rooms and intelligence departments to search for, find, and analyze data that’s pertinent to them within the pool of big data. Data will be considered particularly pertinent if it relates to the future, how uncertain the future is, and venture involving risk and opportunity.


Munok Kwon (author)
Management consultant specialized in the internationalization of medium-sized companies
Employed by an international governmental organization from South Korea

Dr. Peter Meier (author)
Steinbeis Entrepreneur
Steinbeis Transfer Center Risk Management (Langen)

  • Anderson, Chris: The End of Theory – The Data Deluge makes the Scientific Method obsolete. Wired, June 23, 2008 (freely available at www.wired.com)
  • Brooks, David: Opinion – The Philosophy of Data, New York Times, Feb. 4, 2013 (freely available at www.nytimes.com)
  • Han, Byung-Chul: Psychopolitik – Neoliberalismus und die neuen Machttechniken, S. Fischer, Frankfurt 2014
  • Heidbrink, Ludger: Handeln in der Ungewissheit – Paradoxien der Verantwortung, Kulturverlag Kadmos, Berlin 2007
  • Meier, Peter and Kwon, Munok: It all comes down to the right crisis management – management methods used in the military as a basis for dealing with the current crisis, p. 76, special issue of the Steinbeis TRANSFER magazine, Stuttgart 2020
  • Meier, Peter: Making Decisions and Leading People with the OODA Model – How to lead the company strategy and operations in a crisis situation or period of change, p. 68, Steinbeis TRANSFER magazine, Issue 3/2021