Decision-making has become an impressively complex discipline within business practise: Diversification of products and services, networked value chains and ongoing innovation increasingly require business decisions to be made on the basis of meaningful data. On a relatively low-threshold level, such as inventory optimization or individual marketing measures, this is already frequently the case. More complex cases, on the other hand, are less often based on collected data, as recent studies suggest.
Decision-making that is predominantly guided by intuition is, by its very nature, subject to errors that have a negative impact on the quality of the decisions made. In particular, the quantity of data that has to be taken into account often massively exceeds human processing capacity. For this reason, decision-makers usually tend to make selections from this information on the basis of their personal experience and that can lead to decisive influencing factors being overlooked.
Informed decisions require meaningful data
It is common for judgments and assessments to often be subject to biases and unconscious cognitive distortions: We humans usually tend to compartmentalize, to prefer a more familiar and comfortable approach and prefer to act rather than wait and give situations time to develop. Above all, the vast majority of us seem to believe that we have an objective picture of the world and this can lead us to underestimate the influence of feelings upon our thinking. So to avoid making costly wrong decisions, it’s imperative that managers be aware of these biases and make decision-making more data-centric.
However, the availability and use of data assets does not automatically translate into better decision-making. Because business processes are often interconnected, it’s necessary to break down existing silos, consider the data obtained from them collectively and then identify their correlations. In order to be able to react as quickly as possible to changing boundary conditions, the data has to be as up-to-date as possible.
How does Decision Intelligence (DI) support decision-making?
A new data culture established by taking these steps is the foundation for the successful use of DI. Based on machine learning, this approach enables a holistic view of the relevant data, shows connections and trends, captures external circumstances and then independently updates the information needed for forecasts. As a result, DI systems (such as paretos) are able to offer qualified and meaningful solution proposals. The final decision on implementation is then back into human hands. Future-proof decision-making thus combines the strengths of AI with entrepreneurial intuition.