Analytical science stands at a crossroads. Historically driven by overcoming barriers—first generating enough data, then managing its overwhelming volume—the field now grapples with leveraging emerging artificial intelligence and machine learning technologies to unlock deeper biological insights. Success in this new paradigm demands more than innovative tools; it calls for a fundamental shift in how scientists think, work, and collaborate. Chris Hagen, President of SCIEX, explores how embracing flexible mindsets, reimagining training approaches, prioritizing sustainability, and challenging conventional thinking can lead analytical scientists toward more impactful discoveries.
Looking ahead, what area of analytical science do you believe is most in need of a paradigm shift—and why?
In the past, there was a technology barrier, where we needed to generate more and more data. Then we hit a data crunch barrier where we needed better and faster ways to digest data. Now, emerging machine learning and artificial intelligence tools are changing the dynamics again, demanding even more data. We need to take these new analytical tools, drop them on top of the data, and use them to drive biological insights. This is the paradigm shift I believe we will encounter next.
The interesting part is that we seem to be in a cycle. Once we are past one barrier to novel insights, the new tools created will then demand that we produce more data with novel approaches.
How do you see the relationship between automation and human expertise evolving in high-complexity labs?
Data drives much of what we do today—from navigation to television programs—science is no exception. We can generate instruments that produce a ton of data. We can use automation to produce more. In the future, we will create integrated systems that generate beyond that. I see the relationship between automation and human expertise evolving in two areas.
First, we need the tools to interpret the data. The tools that allow us to ask the right questions and give back stronger visualizations. This will enable scientists to prove or disprove a hypothesis and proceed to the next.
Second, there is a need for a mindset shift. Scientists will have to shift from a straight analytical science mindset to something more open minded and slightly less well defined, with more flexible boundaries. Let’s call this the data interpretation mindset.
How should training evolve to prepare scientists for increasingly hybrid roles that combine analytical chemistry with data science and automation?
There is such a wide array of scientists that “training” may be too simplistic. We have new users who need to access the capability through intuitive software. On the other hand, we have scientists who write scripts to crunch through terabytes of data. You can’t oversimplify, and you can’t overcomplicate. The expertise of the tools needs to mirror the expertise of the people using them.
Instead of training as we think of it today, we have to teach this data interpretation mindset. Teach them how to interact with the software and how to ask the right questions. At SCIEX, we constantly strive to pair intuitive, ease-of-use tools with superior customer support. We know every edge a scientist gets can lead to the next discovery.
What role do you think sustainability will play in shaping hardware and software design in this field over the next decade?
As citizens of the world, we have a commitment to sustainability. Part of our role as a solutions provider is to incorporate it in smart ways. As a Danaher Corporation operating company, we strive to be conscious in our manufacturing and logistics practices, as evident by Danaher’s commitment to “set science-based greenhouse gas (GHG) emission reduction targets in line with the Science Based Targets initiative (SBTi), including a long-term target to reach net-zero value chain emissions by no later than 2050.”
From a hardware and software perspective, sustainability will force our industry to be better at innovating. We have to discover new advances as we work to make systems eco-friendly and have smaller footprints, for example.
What’s one question you think the industry isn’t asking but should be?
As a newcomer to the industry, I find it incredibly impressive how analytical science keeps getting more precise, more sensitive, more robust. As a next step, the question I think the industry should ask is:
“What new discoveries are missed because I didn’t take full advantage of something?”
What matters at the end of the day is if our customers can get their jobs done. We see our field service engineers and scientific teams work day-in and day-out to help customers develop new methods, optimize their studies, and get to meaningful results. As we ask ourselves this question, I know we will keep finding ways to bring the full capability of our technology to fruition.
