“Do you want to keep optimizing flight schedules, or do you want to save lives?”
This was the question I asked myself four years ago, when I was considering leaving my data science position in the aviation industry to work in the pharmaceutical industry, for Boehringer Ingelheim.
I enjoyed my work in aviation, but I was struck by the personal difference I could make in fighting diseases. In the end, it was an easy decision.
Data science is important to most industries and companies, but it’s really vital in pharmaceuticals. We have huge amounts of data – from clinical trials, biobank data, electronic health records as well as supply chain and production data – and it has the potential to help us better understand how diseases originate and how we can cure them, or to bring new treatments to patients faster. But the information we need is often trapped in the data. The challenge is, how do we set it free?
At Boehringer Ingelheim, we’ve been using data for a long time – back in the old days, they tracked it in binders! Today, luckily, we track the information digitally. But certain data is stored in databases, in data warehouses, and we haven’t had full oversight of which data we have available.
We needed a big change, to make our data findable, but also to create the infrastructure and processes to use it effectively. This was central to our business objectives, as data offers huge potential along our entire value chain, from early drug discovery and clinical trials to production and sales.
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So we embarked on a revolutionary transformation. It’s called dataland.
It’s a company-wide program to create a seamless ecosystem, connecting data from different domains, and making all of our data findable, accessible, interoperable and reusable. We’re moving to a cloud infrastructure, where we’ll have all the technical capabilities needed to process huge amounts of data, and we’ll have a structured catalog where we can easily find out who the data owner is and what we can use it for.
To make dataland a success, we have to make sure we have enough data expertise. That’s where the new Data Science Academy comes in, a program intended to increase our pool of data scientists and engineers through retraining and recruitment and to strengthen our company-wide data literacy. This will increase the awareness about the business potential of data and foster a data-driven culture across the company, encouraging an openness to new ways of working.
It’s an exciting time for our data science community at Boehringer Ingelheim. We can see the company’s commitment to data-driven thinking, and we’re already noticing the impact of these initiatives on our work. We expect our numbers to double in the coming years, which gives us a strong sense of forward momentum and many new opportunities.
For the company, these changes mean we can be more effective in targeting diseases and developing breakthrough treatments. For me personally, it means getting closer to achieving my goal of having a positive impact – and being glad I made that decision four years ago to change industries and join Boehringer Ingelheim.