Receive up to € 20k to create your proof of concept


Access to leading corporate partners and opportunities for long-term collaboration
Receive up to EUR 20,000 in funding to draft a proof of concept – without giving up equity
Real-world use cases and unique datasets from established corporations

Data Challenges

DataHub Ruhr is a business building program that connects start-ups with established corporations in the Ruhr region. The program tasks start-ups with developing innovative, data-driven ideas to tackle use cases provided by our corporate partners. As part of a three-month collaboration, start-ups will draft a proof of concept, with the opportunity to receive up to EUR 20,000 in funding.
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Claim Predictor
The Ruhr Area is Germany’s largest metropolitan area and famous for its extensive mining history. In a post-mining era, RAG is committed to keeping the area safe and livable for its many inhabitants. Analyzing and understanding the spatial layout of more than 400 coalbeds is key in this endeavor. Nevertheless, property damages as a consequence of mining still exist today.. Can you help to better understand these damages and their expected costs?

Use Case

RAG has been producing hard coal by underground mining in Germany until the end of 2018 and continues to be responsible for the resulting consequences in North Rhine-Westphalia and Saarland. For almost 200 years, thousands of mine shafts and other cavities have been developed underground, many of them at a time when computers and 3D models were still unknown.

By performing continuous monitoring of ground anomalies RAG is able to identify potentially impacted areas early. (One very successful algorithm actually resulted from an previous Data Hub Challenge “spatial pattern recognition“.) But despite all prevention efforts property damages caused by (former) mining activities are still common in the region. RAG is liable for many of those damages and compensates affected owners. Therefore, a strong understanding of expected claims and compensation costs is critical to RAG business. In addition to decades of individual expertise, experts use already established prediction models based on size and population density of the shutdown area.

The goal of this use case is to use historical data of property damages to better predict future compensation costs. It is believed that the number and value of damages claimed in all shutdown areas is following a similar “curve” over time. Therefore, historical claim data from areas already shutdown for a long period should allow us to predict claim data of just recently stopped mining areas. We expect a more precise prediction than current models and an earlier recognition of potential overspending of provisions.

An additional way to improve prediction quality would be to consider the locality of claims. For example, if similar claims by 3 of 5 houses in a street are received, the probability of claims by the other 2 house owners is relatively high.   

Data of multiple thousands of claims per year from up to 43 shutdown areas is available. Especially, claim data since 2000 can be considered complete and high-quality (date, value, kind of damage, kind of building, location). 

What you will need

  • In-depth knowledge of correlation analysis and time-series prediction models
  • Advanced understanding of scenario analysis
  • Experience to work with data in an General Data Protection Regulation (GDPR) sensitive environment

Expected result

  • Improved prediction accuracy and timeliness of compensation costs
  • Continuously updated prediction based on new input data 
  • Detailed understanding of correlations between shutdown areas
  • Interactive scenario analysis
  • Graphical user interface, eg. Dashboard


The project can be divided into three milestones:

  1. The first milestone is reached when correlations between shutdown areas are proven. Additionally, the possibility and potential of correlation between local claims shall be investigated.  

  1. In the second milestone a prediction model shall be developed, which considers the outcomes of milestone 1 and outperforms current models on test data.

  1. Third, a graphical user interface is required, which allows interactive scenario analysis.

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Let's talk

Felix Schröder
Program Manager DataHub Ruhr

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Let's talk

Felix Schröder
Program Manager DataHub Ruhr