Today, Data Mining is mostly recognized as the process of collecting and analyzing amounts of personal data of customers and people for different purposes by an increasing number of companies, governments and public services. However, Data Mining or better its next generation, Knowledge Mining, is much more than this and can be much more useful in various problem fields. It can be very helpful, valuable, and even sometimes an unavoidable tool for finding solutions and getting insights into many real-world processes. Humans have for centuries been seeking proxies for real processes. A substitute that can generate reliable information about a real system and its behaviour is called a model and they form the basis for any decision. It is worth building models to aid decision making, because models make it possible to: Identify the relationships between cause and effect. This leads to a deeper understanding of the problem at hand by deriving an analytical relationship between them,

the relationships between cause and effect. This leads to a deeper understanding of the problem at hand by deriving an analytical relationship between them, Predict the respective objects can expect over a finite future time span, but also to experiment with models. Exactly the ability to make predictions about the future forms the core of intelligence at all.

the respective objects can expect over a finite future time span, but also to experiment with models. Exactly the ability to make predictions about the future forms the core of intelligence at all. Simulate the objects' behaviour by experiment with models, and thus answer "what-if" questions essential to decision-making,

the objects' behaviour by experiment with models, and thus answer "what-if" questions essential to decision-making, Control the objects by finding suitable means to effect the objects and enforce a specific behaviour.

The world around us is getting more complex, more interdependent, more connected and global. Uncertainty and vagueness, coupled with rapid developments radically affect humanity. Though we observe these effects, we most often do not understand the consequences of any actions, the dynamics involved and the inter-dependencies of real-world systems in which system variables are dynamically related to many others, and where it is usually difficult to differentiate which are the causes and which are the effects. There are many cases in practice where it is impossible to create analytical models using classical theoretical systems analysis or common statistical methods since there is incomplete knowledge of the processes involved. Environmental, medical and socio-economic systems are but three examples. In contrast, inductive models obtained by knowledge mining are derived from real physical data and represent the relationships implicit within the system without or with only little knowledge of the physical processes or mechanisms involved. There are a lot of complex problems, which do need decision-making, but the means - the models - for understanding, predicting, simulating, and where possible controlling such systems are simply missing increasingly, because we only have insufficient knowledge to follow theoretical modeling approaches. A more powerful and easy-to-use tool that fills this knowledge gap is inductive self-organizing modeling as implemented in Insights.