If you think about what’s most important in life, it really boils down to health – living a healthy life. When I was young, I always wanted to learn how life works on the molecular level, and was fascinated with biology. But I was also very interested in information science, and had been programming since I was a kid. After university, I was fortunately able to combine my passion for information and life sciences with computational biology.
I became a consultant for Natural Language Processing (NLP), building solutions to allow computers to process data in forms that machines cannot readily interpret, such as text, images and videos. After initially focusing on applying this technology in the life sciences, I worked across several industries, from automotive and transportation to electronics and retail. I learned a lot in those different domains, but I often worked for companies where you’re trying to convince consumers to buy a product they may not actually need.
Pharmaceuticals, on the other hand, is an area where our products truly improve the lives of people. We’re talking about drugs to treat cancer, diabetes, chronic kidney disease, or heart failure – conditions where people have really lost their quality of life. I saw the opportunity to help these patients who are really struggling, so I decided to join Boehringer Ingelheim.
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“I often worked for companies where you’re trying to convince consumers to buy a product they may not actually need. In pharmaceuticals, our products truly improve the lives of people.”
I’m now the IT liaison for the “Generate Insights from Hidden Knowledge” capability, and the main problem we’re trying to solve is how to automate the analysis of large amounts of unstructured data. For instance, we are looking at text transcriptions of questions from patients, or conversations with doctors. It’s a treasure trove of information, but it takes a lot of manual effort to read through them and make sense of them on a larger scale, to see if there are global trends or patterns.
Automating that process means our colleagues can read significantly less, but still have the opportunity to start from a high-level view on what’s happening, then can drill down into the individual notes. It’s very rewarding when I hear colleagues have found something interesting that they wouldn’t have discovered if it weren’t for the solutions we provided.
It’s an exciting time at Boehringer Ingelheim because there’s such a strong emphasis on becoming data-driven. Data science is being taken seriously, with strategic initiatives and large investments. To add to that, we have the technology to really make a difference at large scale. We are at the end of point solutions. Now is the time to clean up and leverage the synergies. There’s a great sense of momentum, and it’s wonderful to be part of this change.
