Welcoming Paul Vauterin as VP Visual Analytics

We’re glad to have Paul Vauterin join ONTOFORCE as Vice-President Visual Analytics. Paul’s an expert in software product creation and management, including vision, strategy and execution for biotech, human diagnostics, data mining and visual analytics applications.

Q: Welcome Paul. Can you tell us a bit about your background?

PV: After having completed a PhD in Physics at Ghent University, I co-founded Applied Maths and developed BioNumerics – a software package that pioneered the integrated databasing and analysis of various types of biological data. Next, I worked at the University of Oxford, where I was the architect of a suite of web applications for visual analytics and data mining of large-scale, genomic variation datasets. After that I joined Multiplicom, and coordinated the development of novel software solutions for analysis of the company’s NGS sequencing in-vitro diagnostics assays. I joined ONTOFORCE in June this year.

Q: What makes ONTOFORCE and DISQOVER stand out?

PV: Everybody agrees that data interpretation and knowledge discovery have become a bottleneck. But very few players offer a real & lasting solution that combines usability and power in a way DISQOVER does. In my view, this is the result of the consistent application of a set of key concepts and philosophies throughout the entire product. For example, these concepts underpin DISQOVER’s unique ability to merge data from a wide variety of sources into a single, coherent and linked environment.

Q: What do you see as the next big challenges for our industry?

PV: There are many, as this is still a rapidly changing field, but let me focus on two. First, there is the fundamental problem of data quality. For years, people have been prioritizing data quantity over quality, and are now realising that pollution by poor quality data can introduce noise and obfuscate the data analytics process. We will need clever automatic ways to validate and even improve the quality of various data sources. I expect AI to assume a major role in this context.

Second, I would like to highlight the more technical challenge of real-time advanced analytics on high-volume, streaming data. Most of today’s data processing approaches are not up to that task, at least not in a cost-effective way. And yet there is a huge potential here. Just think about knowledge discovery in data produced by all these sensors that we are introducing into our lives: smart houses, wearable devices, etc…


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