Over the course of her career, Marianne has held a variety of positions that unite her background in chemistry with applied statistics. It is this combination of skills to which she attributes much of her success working to bridge the gap between scientists and statistics.
Since joining Novozymes nearly 13 years ago, Marianne has played an active role in the organization’s analytics transformation and the democratization of statistical approaches to domain experts. Her work touches on a wide range of statistical applications including design of experiments, ANOVA, linear regression, mixed models, nonlinear modeling, time series analysis, chemometrics and machine learning in support of the entire global business from R&D to supply chain.
Meg: One of the most common objections we hear from newcomers to JMP is that some are concerned statistical approaches will supplant domain expertise. How has having JMP changed the way statisticians collaborate with domain experts at Novozymes?
Marianne: At Novozymes, statisticians teach the scientists to do statistical analyses themselves using JMP and continuously support them in using the correct approach. We statisticians don’t teach statistics as “giving the one and only conclusion,” but rather as a tool that – together with your subject matter expertise – helps you make better decisions.
Meg: And as scientists become increasingly adept at incorporating statistics into their work, you’re probably freed up to work on more and more complicated challenges across Novozymes’ global organization! Where do you personally turn for inspiration to continue learning new ways to use JMP?
Marianne: I have attended eight Discovery Summits in the last 10 years! They are in my opinion THE BEST way to learn about what JMP can do, get inspired by what other companies are doing, and learn the newest features when new versions are released. Add to that the possibility of talking to the developers about software issues or wishes, my trip has always been worthwhile.
Meg: People often talk about Discovery Summit as both a source of inspiration and an opportunity to add value by deepening the relationship with JMP. Tell me more about your relationship to the software developers. Do you feel that your feedback helps influence new JMP releases?
Marianne: I have had good interactions with JMP software developers during Discovery Summits and have also succeeded in influencing the development of functionality in JMP. This is definitely a valuable thing, and the fact that JMP developers and even executives listen to “ordinary users” surprises most people.
Meg: What advice would you give to someone looking to cultivate a more mature analytics culture in their organization?
Marianne: I think there are two ways, and which is the best depends on the organization: Either top-down, which I believe is harder, but faster if you succeed, or bottom-up, which is slower but an easier way to get started.
Top-down would mean getting upper management to understand the value of analytics and drive the analytics culture. Bottom-up would mean starting to offer training internally to scientists and engineers. As scientists and engineers start to use analytics and solve their problems faster and better and share their stories, it will spread naturally.
Meg: What advice would you give to someone who is just starting out with JMP?
Marianne: Join the next Discovery Summit, watch webcasts on jmp.com, do the STIPS training, ask questions on the JMP Community, and find some like-minded colleagues to discuss statistics with.