When we can not make the right decision because of the lack of information about incompetence and data to make a correct decision in a new field, or “If I would have known that such problem exists at all, I would have hired specialists to fix it” is called the Dunning-Krueger effect.
… In 1999, when psychologists Dunning and Kruger first described this phenomenon, they argued that people who do not have the knowledge and skills in specific fields suffer a double curse. First, they make mistakes and make bad decisions. And second, the same gaps in knowledge prevent them from seeing their mistakes. In other words, bad workers do not possess the real competence necessary to understand how badly they perform.
In business, this effect appears constantly, and with the arrival of the digital economy and blockchain technology, we see a “bubble” formed precisely by incompetent enthusiasm-driven people, who do not have the opportunity to understand their mistakes before they affect business value.
In science, this effect is overcome with the help of preliminary analysis tools: all possible data is collected and analyzed on the subject of general laws and patterns. On the basis of the analysis, a study of dark areas is conducted, as a result of which discovered decisions are often of even greater value than the subject of the research itself.
As part of my advisory program, I invite entrepreneurs and ICO/STO advisors to carry out a preliminary study of a business model or project to find such dark areas, after which any expert will be able to deal with possible problems easier.
What if your project needs tokenomics, but your advisor focuses only on marketing? What if he just does not see that part of the strategic development of the project, where you need to research tokenomics?
Preliminary analysis is like bright driving headlights, it is a powerful tool in business analytics, which can help spot obstacles far on the business road, before they actually appear.