“The software you use not only shapes what you learn from your data; it shapes the questions you ask!”
-- Tom Treynor, Director, Zymergen
The Beyond Spreadsheets blog series shows how JMP customers are augmenting their tools and processes for exploratory data analysis to make breakthrough discoveries. The series features Q&As with JMP users to learn more about how and why their organizations bring Excel data into JMP to create graphics and explore what-if scenarios. This week's Q&A is with Tom Treynor, Director at Zymergen, and an experienced scientist, chemist and quality engineer.
Tell us a little bit about the function of your department and how it contributes to your organization’s mission.
Zymergen is harnessing the power of biology to make chemical products that are good for business, people and the environment. Every quarter my department, Test Operations, is making hundreds of thousands of measurements and distilling them into a handful of critical decisions about which engineered microbes should deliver the best process economics at industrial fermentation scales. However, the high throughput and low volumetric scales with which we operate at Zymergen pose significant false positive and false negative risks, respectively, so we also distill our data into critical decisions about how to improve the processes we use to improve the microbes.
What do you like most about the type of work you do?
Every week at work, I learn new things about biology, chemistry and physics, because every week my team is deriving new mechanistic insights through testing and refining our statistical models. Even now, when there are hundreds of things already tracked in our databases, we are still innovating better ways to measure them and discovering new things worth measuring.
What do you like most about using JMP?
The folks who built JMP recognize that any problem worth solving has both multiple factors and multiple competing responses (e.g., cost, quality, speed). Although JMP has many capabilities that derive from this insight (for example, the ability to decorate columns with information such as Specification Limits), they all come together so nicely in JMP’s Profiler platform. Another of JMP’s greatest capabilities is the way its user interface has been so well designed for training scientists and engineers to become applied statisticians themselves.
What is a professional accomplishment of which you are most proud?
It was pretty cool the first time we reduced the unexplained variation in a fermentation process from over 10% to under 2%, yet it gets cooler every time we do it! Although it is theoretically possible to achieve the same increase in decision quality by buying and operating more than 25 times as many bioreactors, I think it is more satisfying to increase the productivity of my team by an order of magnitude than to grow it by that amount. I can't wait to tackle some of the most challenging fermentation processes in my industry!
How is using JMP different from using spreadsheets to conduct statistical analysis?
The greatest power of spreadsheet programs is also their greatest weakness: They let you put anything anywhere. As my colleagues make the transition to JMP, they eventually realize the flexibility they have lost is not a bug, but a feature of their new statistical software. For example, no longer do they need to copy and paste their equations to other cells in their spreadsheet, since a JMP formula is applied automatically to every row in its column. Although it takes some practice to write formulae that explicitly incorporate all the conditional statements they had previously implied by selectively aggregating or copying and pasting in a spreadsheet, developing this skill makes it so much easier for them to read the many, many stories that each measurement has to tell.
What advice or best practices would you give to other companies that are currently relying on spreadsheet tools to conduct statistical analyses?
The software you use not only shapes what you learn from your data; it shapes the questions you ask! For example, we all learned how to make histograms in grade school, and yet so few of our colleagues have ever made a histogram in a spreadsheet. Although it can be done, and it doesn’t even take long once you know how to do it, making histograms in a spreadsheet is simply not as easy as making a scatterplot. As a result, our colleagues almost always default to making scatterplots without even considering other kinds of analysis. In fact, I bet few of you reading this even thought to make a histogram in your workplace until you started using JMP – and now you make histograms all the time. Why? Because with JMP you can make countless histograms, visualize outliers, test for normality and fit each distribution in under 10 seconds. JMP makes it so easy to ask the right questions and to perform the right analyses once you know how to use it.
Ready to go beyond spreadsheets, too? Visit www.jmp.com/beyond to gain key insights on visualization, including how to determine what graphs should be used in certain situations for “what-if” analysis.
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