The DataMine project – in the framework of Horizon Europe’s Eurostars program – will support companies handling real-time sensor data as input to machine learning and AI. We will conduct pilot projects, in cooperation with BCG, with different industrial players in Europe as well as North and South America.
Evaluated by 3 independent experts, our project application achieved a score of 49 out of 54, summarized as follows: “The project reflects the deep knowledge about data science solutions in terms of the data mining process, data preparation, and deployment. (...)To my best knowledge, this is a state-of-the-art proposal.”
The innovation call received nearly 500 applications from 37 countries.
Businesses today are awash with data but are not able to extract real value without proper data preparation. This process unnecessarily consumes a significant share of data scientists’ time (80% according to a recent analysis by The Economist). Our proprietary data preparation framework has two main features: firstly it automates and quality controls the data mining, and secondly it provides higher quality models and results due to a framework for implementing domain-specific processing techniques, like event categorization, alignment and summarization.
The Eurostars funding enables us to further productize Squashy into a standalone product and also generalize it into a framework that can address more types of industrial data. Today we are using it as an internal tool for our own data scientists, but we truly see the potential benefit of extracting it as a standalone product. Together with our partners at BCG, we discovered that data mining is still a hurdle for many data scientists and we believe further development of Squashy can address some of these issues in a meaningful way.