The World Datanomic Forums are supported by investors and members who organise sessions to solve problems based on data.
We host so called 'Datathons' to mine, organise and evaluate data together with valued partners in the fields of politics, data security, the private economy and social sciences.
Before, during and after each Datathon, the World Datanomic Forum provides expert advice in regards to the accurate storage, security and transcription of the data of interest.
Once the supporting data about a situation is assessed and evaluated, relevant parties are again invited to a separate collaborative discussion about further barriers of progress that need to be addressed in order to reach a satisfying improvement of a situation, or to achieve a solution to a problem statement.
Expected returns @ 4000% in 2018
Expected returns @
25% in 2018
About The Benefits Of Datanomic Systems Engineering
Groups of chaotic and unstructured data can be worse than useless - a pointless drain of cash, memory and men power. However, when it is leveraged, organised and categorised by skilled data scientists, data usually amounts to valuable and expensive business intelligence.
Data first needs to be transformed into information to then be organised in ways that are useful for the actual application. The job of a usual data scientist is to extract insight from raw data, to gather and creatively combine resources and to define questions that could be posed into the data.
Intuition and logical thinking are essential in the process of data analysis - to frame the right questions, form a path to a desired goal and continue with the supervision of data. This is Datanomics.
A good data scientist is able to give you a probability but not a path to a desired goal or outcome.
Despite imperfections, humans outrun computers in one key area:
Computers are great for handling purely functional questions but they are lousy at conjecturing and figuring out the ‘why’ of data. They simply cannot do it. At least not yet. Even if Artificial Intelligence reaches a point in which computers can give information about the ‘Why’ of a situation, they will still need creative and intuitive help from humans. The human factor in Data Science is essential. Datanomics is first and foremost a social science.
Big data does not a cure all - the shire volume of data needs a social science behind it because numbers cannot speak for themselves to be useful. We must learn to speak for them and refrain from detaching them from their subjective reality.
Asking the right questions and having a path forward for the curation and analysis of data is fundamentally necessary to achieve a useful and effective overall equation. Social science and undeniable results result in data driven performance and operational results.
Data science is a maverick, revolutionary upstart element which challenges and disrupts the assumptions of traditional business models and shines a new light on previously dark knowledge, thus creating new business intelligence to fundamentally transform procedures, products and profitabilities of companies.
Datanomics infers an evolution beyond the traditional output of aggregated data - it is a use case driven, iterative social science with the intent to derive insight and operationalise insights into down-stream applications - without forgetting the value of human intuition.
Introducing Datanomic systems thinking, problem solving and engineering usually requires fundamental changes within an enterprise. This includes but is not limited to reasons for decision making and the behaviour of decision makers. In the traditional corporate model with an absence of precise data, it has made sense for the enterprise to rely on the leaders at the top of the company to lead -mainly driven by intuition.
This freedom of decision making of bosses was not only justified by the question of power, but can also be seen as a natural consequence of the fact that the leaders of a company used to be the ones with the most experience.
Pre Datanomics, this was certainly true. With little accessible data, the leaders of a company were indeed those who, due to their experience, knew better.
Today, ‘old knowledge’ of senior executives is often an asset of declining - if not useless - value.
To be truly data driven, the enterprise must engage in a culture wherein the decision making is organised around clear metrics and results.