In this Q&A, Matthias Hofmann—a member of our Customer Success team—discusses his unique path to Albert, provides insights on the critical importance of a data ecosystem and “one source of truth in the lab,” and highlights what he loves most about Albert.
There are multiple aspects of Albert that I like very much and wish I had when I was on the “other side” as a chemist in the lab.
A self-described “unconventional chemist,” Matthias Hofmann, Ph.D., has spent years blending his love for managing a chemistry lab with his passion for data analytics. After honing his lab management skills at BASF for several years, Matthias published Data Management for Natural Scientists, imagining a tool that would empower chemists to harness experimental data, making their daily lives easier and improving the quality of science. Months later, he learned that tool existed in Albert.
Matthias’ perspective and approach are quite rare in the Chemistry and Materials Science space, which is why we are so excited to have him on our team at Albert. In his role as Technical Success Specialist, he guides our customers on how to leverage Albert’s end-to-end R&D platform to invent faster and better.
Q: You describe yourself as an “unconventional chemist.” Tell us more!
I am a chemist by trade with a particular passion for using data to improve how we work. I often hear from other chemists that I’m “too little of a chemist” because I focus so much on the process and data side of things. While I love bench work, it’s hard for me to ignore the aspects of our job that can be done faster and better. That makes me somewhat unconventional. But I don’t fit neatly in the data science bucket either. I’m in this sweet spot between the two, which is really cool because it empowers me to leverage data science to find new ways of improving our work in the lab, while also giving me the chemistry perspective that I need to discern what data science approaches will work in the Chemistry and Materials Science industry—which is highly nuanced.
For example, a traditional data scientist may not be aware that some of the cool features and new techniques used in data science won’t be the best fit in a chemistry R&D lab. Many of the most popular data science models used in business require hundreds of thousands of data points to work. This is great for certain applications like Sales and Marketing, which have extensive data on thousands of customers. But in Chemistry and Materials Science R&D, we don’t have that volume on a regular basis. In the most common setting, we as scientists often need to draw conclusions from 15-25 experiments and extrapolate from that data.
Q: What made you develop this unconventional approach in the lab?
With my background in physical chemistry, I started out in a similar manner to other chemists, using typical methods for making formulations and performing characterizations. When I worked as a lab manager, I found myself increasingly frustrated with all of the time my colleagues and I lost trying to understand and analyze the data coming from the available characterization tools at the time. The output was not structured in a way that was easy for us to analyze, requiring chemists to endure a lot of repetitive, manual, and time-consuming work that, in the end, was highly subjective depending on who was reading the result. That’s when I broke from tradition and started to look for new ways to improve this often painful and unsatisfying process.
Fortunately, while completing my Ph.D. thesis years before that, I had the opportunity to physically build several material characterization devices. This required me to think through how the results coming from a device should be structured so they could be easily readable and understandable. I then dove into programming, scripting, and data science, quickly learning that it’s not that difficult to improve the data coming out of these tools. So, at BASF, I continued scripting and started making the resulting data analysis tools more readily available to my colleagues. In doing so, we also improved the quality of our research by at least partially removing the subjectivity and inconsistencies in how we read the results.
Q: Is it true that you wrote a book about that experience?
I did. It’s called Data Management for Natural Scientists and is intended to be a practical guide on how to process scientific data for other chemists or material scientists like myself. When I looked for ways to more easily gain access to experiments and analyze the results, there was nothing out there. So, I taught myself and pulled many of my learnings into this book, which I wrote with a lot of valuable input from Torben Gädt, a close colleague of mine at BASF who is now a Professor at Technical University of Munich. (We are actually working on another project focused on open-source data analysis in the natural scientific R&D space right now.)
Little did I know, most of what I was describing in the book was already being done halfway around the world at Albert.
Q: How did you discover Albert?
My first interaction with Albert was an email that said, “Ready to be the first to join this rocket ship in EMEA?” It definitely caught my attention! And so far, the rocket ship analogy has been pretty accurate.
After speaking with Albert’s co-founders, it didn’t take long to see that this is a company with ambition and the ability to change the way industrial research is performed—and I wanted to be a part of that. As I mentioned, being close to natural sciences is equally close to my heart, considering my education in the field. However, I also knew that the way industrial research is typically carried out didn’t feel quite right.
Albert was the answer for me. It gives me the opportunity to harness my “unconventional chemist” role to help chemists and R&D organizations get the most out of their experiments by effectively using their precious and thoughtfully collected data.
Q: As a chemist by trade, what features of Albert do you love the most?
There are multiple aspects of Albert that I like very much and wish I had when I was on the “other side” as a chemist in the lab. First, I love Albert’s highly intuitive user interface, which allows for a familiar spreadsheet-like experience. I think many chemists will appreciate how comfortable the user interface feels.
At the same time, I like that Albert has the flexibility to support multiple levels of user proficiency. If you prefer working by using the highly intuitive user interface, you can. But if you are more into scripting and coding, you can also interact with Albert in that way. There are multiple avenues into the tool.
Finally, and perhaps most importantly, I love that Albert truly offers “one source of truth” across the entire R&D landscape. Every piece of data that you could possibly need throughout the R&D process is centralized in the Albert R&D Platform. A lot of other R&D software only incorporate data from certain parts of the R&D process, meaning a lot more manual work to access, integrate, and analyze that data. With Albert, a chemist can be sure their data, and their colleagues’ data, will always be readily accessible across projects—no matter what. The time-saving benefits of this are far reaching, but so are the incredible insights to be gained with access to all historical data in one place.
Albert is an unconventional (and conventional) chemist’s dream!
A big thank you to Matthias for sharing his unique insights into Albert’s end-to-end R&D platform and its value for chemists in diverse lab settings. Explore how Albert can revolutionize your team’s approach to chemistry R&D with a few helpful links below!