| ``In a perfect world, free from budgets, every piece of data that is collectable would be collected, and every byte would be analyzed [...]''. Big data analysis frameworks have already found their way into mainstream application and have seen wide-spread deployment in scientific communities as well as in organizations across different industry fields. Moreover, also AI-support is a necessary requirement for modern (big data) analysis applications nowadays. An exemplar industrial application domain highlighting the necessity of AI-supported data exploration in a real-world big data analysis application scenario is the risk analysis (economic risks) of building and construction projects. Currently, economical risk analysis is largely based on so-called expert knowledge, an experience- and intuition-based analysis of the risk. Even this experience-based (manual) process can be formalized in different ways, ``construction projects are characterized by carrying a high level of uncertainty and complexity''. With a strong focus on the project execution phase, this paper outlines how ML algorithms can be applied to automatically predict the financial outcome based on financial controlling data and thus to potentially assist in mitigation of financial losses. |
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