Craig DePorter is Head of Advanced Mineral Development at Omya, a leading global producer of industrial calcium carbonate and dolomite derivatives. A polymer chemist with more than 20 years of industry experience, Craig has throughout his career promoted data-driven approaches in the pursuit of both business and scientific objectives.
A Lean Six Sigma Black Belt, he has long applied industrial statistics to challenges spanning the entire product pipeline from new product development to commercialization to quality assurance in Europe, the Middle East and North America. Craig is also a prolific inventor – he holds 19 US patents – and is deeply engaged with talent recruitment and forging industry-academia partnerships.
I asked .@CraigD about his evangelism for analytics, not just at Omya but throughout his career.
Craig: I am an analytics evangelist, specifically because the technique (which is not software-specific) has been proven countless times to allow more efficient research. People are able to learn more by doing less, even though they get scared of design sizes when they start. For example, I’ve seen very, very few people make a real effort to use design of experiments (DOE) and decide to go back to “the old way.”
Have I been offered any new job opportunities because of it? Not explicitly, but perhaps indirectly since I’ve increased the efficiency of the organizations I worked for.
Meg: And surely that efficiency can only be scaled up as you bring colleagues on board with data-driven approaches. That being said, introducing statistical methods to a previously statistics naïve organization can be disruptive. What, in your experience, has been the best way to grow support for data initiatives? Were there setbacks?
Craig: It’s all about learning more with less effort – and being able to back up those discoveries with sound statistics instead of techno-jargon and scientific supposition. My efforts at my current company, Omya, have been very successful in engaging a growing community of users who see the benefits of analytics. Though I am on the heels of at least two (if not three) prior attempts to do so that were not really as successful.
Meg: One of the most common objections we hear from newcomers to JMP is that some fear statistical approaches will supplant domain expertise. From your perspective as a polymer chemist, how has having JMP changed the way you apply domain knowledge?
Craig: This is something that I make sure I cement into my user group: that domain expertise is even more important when using DOE, since the planning aspects are the most critical steps. What combinations won’t work? Can you expand the ranges? Does this encompass what you “think” will be the best setting? And so on.
Meg: Tell me about your relationship with JMP as an organization. What are the benefits to connecting with users outside of Omya, for example at a Discovery Summit?
Craig: The JMP organization is top notch, even back to the 1990s when I started using the software and did training at SAS headquarters in Cary. And expanding JMP to other companies that I have worked for has always been with great support from the commercial and technical teams.
Discovery Summits are superb at growing the community of statistically-minded people. No hard-sell of JMP. It certainly does not hurt that JMP is the best overall tool I have found for the sorts of things that I do, but the feel of the Summit is about espousing the concept of using statistics in your day-to-day work. Creation of this mindset prevails ... and it just so happens that everyone uses JMP. It’s just a tool.
Meg: What advice would you give to someone who is just starting out with JMP?
Craig: Try it, and have a mentor help you get going. Start small.
You must be a registered user to add a comment. If you've already registered, sign in. Otherwise, register and sign in.