Everyone’s Talking About Data Science and Analytics. Here’s How AbbVie Approaches It.

The average person generates 1.7 megabytes of data every second. That’s enough to fill a laptop hard drive every week. In science and medicine, the amount of data we have access to is also growing — in fact, it’s doubling every 18 months. But with so much data being generated, how can scientists best collect, analyze, and apply it to help improve patients’ lives and revolutionize the healthcare industry?

At AbbVie, diverse teams of life scientists, data scientists and engineers are creating new ways of doing just that. We call this field data convergence, and it’s evolving as quickly as we generate data.

Three leaders share their insights on how to harness data and what they’ve learned building a team of experts with unique skillsets.

Howard Jacob, Ph.D., vice president of genomics research and head of data convergence: Convergence is in some sense exactly what it sounds like — it’s bringing together different types of data to create knowledge. We have access to so much data here at AbbVie: preclinical and clinical trial data, public databases, data from scientific publications, and a million genomes — that’s a lot of data!

So, data convergence was born out of the question “how do we take all of that information and put it together so that we can drive new knowledge much faster?

Jennifer Van Camp, Ph.D., senior director and research fellow, R&D, information research: There are a lot of disciplines that are represented on data science teams at AbbVie that one doesn't necessarily think of as typical for the pharmaceutical industry. We have electrical engineers with experience in signal and image processing, computational linguists, data engineers, and life scientists like me on our data science teams. It’s the mix of different core strengths and experiences that make progress possible.

Jacob: We’ve built a central knowledge platform called the ARCH, which stands for AbbVie R&D Convergence Hub. It pulls together all these different data sets into one place. It incorporates different types of information tools that allow us to ask new questions and gain additional insights. With ARCH, we’re able to learn new things about disease definitions, discover new indications, and take a deeper look into understanding the molecular underprint of a disease. In a sense, it democratizes the data.

Brian Martin, head of artificial intelligence & research fellow, R&D, information research: The powerful thing about the ARCH is that it’s a knowledge platform versus a data platform. What makes it that way is our scientists and having them explain what makes a specific piece of data information meaningful.

Martin: The real-world data we have access to provides us with the opportunity to ask ourselves questions regarding patients’ experiences to quantify their unmet needs from various perspectives. For example, we can now look at preclinical data alongside clinical data and data from insurance claims. This can help us connect the various impacts that a certain compound might have on patients. That ability to bridge the gap from the real-world data back to preclinical data in the lab, that's the power of integration to affect the way we bring assets into our pipeline.

Van Camp: I don’t think there is a single answer – there’s not one class you should take or a degree you need. But I always believe in being relentlessly curious. As an organization, industry, and a society, we have to keep asking those hard questions that we can't answer.

Watch AbbVie’s LinkedIn Live event: “What’s the Formula? Tackling the digital health revolution” for more from AbbVie’s data convergence leaders.

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