The availability of real-time data from IoT will completely destabilize traditional calculations.
IoT could become a destabilizing factor for some of the more traditional elements of the entire financial sector by increasing the availability of reliable and real-time data. This will disrupt the balance between information sharing and information asymmetry between market players.
To explain, we must first look at how companies in the financial sector have, up till now, made decisions.
The decision-making framework
In 2003, C.F. Kurtz and D.J. Snowden published a paper The new dynamics of strategy: Sense-making in a complex and complicated world. In it they presented a new framework, named Cynefin, which orders the rules for making decisions in various conditions of uncertainty.
The Cynefin framework can help visualize changes in decision-making mechanisms resulting from the migration in what is termed the “Chaos” domain – where most of the financial institutions operate right now – into the domain of “Complex” systems – to which they will need to migrate in order to stay competitive.
- Chaos domain: no visible cause and effect relationships. Interventions are focused on stability. IoT applications in this area can be used to examine how actions taken affect the functioning of the system. The Chaos domain is the basic area of application of statistical tools. Information asymmetry results from the difference in the quantity and quality of available data for statistical analysis.
- Complex domain: cause and effect are consistent only in retrospect and are not repeated. Decisions are based on observed patterns. This domain can be called “The domain of the IoT:” pattern detection algorithms (e.g., artificial neural networks) can introduce significant changes in the asymmetry of information.
- Complicated or Knowable domain: cause and effect are separated in time and space. The right analytical and reductionist approach. Information asymmetry is potentially due to differences in professional knowledge. IoT applications in this area are in particular a better understanding of the process and, to a lesser extent, a (nondisruptive) reduction of the asymmetry of information between the seller and the buyer.
Although IoT applications are possible in all of the domains, the most economically significant effect will be when their implementation moves the part of the system to a different domain because it will result in changes to decision mechanisms, especially when used by interrelated market players.
Different decision rules will result not only in modeling accuracy. Models based on current sensor data will also result in near real-time adaptations to the changes in the system conditions, while the statistical model will have significant delays before being accurate, because of the necessity to collect a representative amount of historical data.