Meg: As a Six Sigma Black Belt and someone whose work and research have incorporated applied statistical methods for many years, you’re at a position in Siltronic where you can act as change agent and resource for those who are new to statistics. But introducing statistical methods can be disruptive at first. What helped you to garner support for data initiatives with both your leadership and colleagues?
Georg: Siltronic AG has always been strong in statistics, as this is really a must in the semiconductor industry. But JMP has made it a lot easier and attractive for users to deploy standard statistics as well as advanced methods.
At Siltronic, our management was aware of this quite early on. Soon it became evident that with JMP, more comprehensive analysis and better decisions were possible, and that gave us the drive to implement it widely.
Meg: What were some of the hurdles you had to overcome in that initial implementation phase?
Georg: At first glance, JMP looks quite complex. And it is different from Excel – which is also its strength. But, for sure, users need to change how they think and act. However, there is more than one measure users can take to overcome this hurdle.
I’ve found that it’s quite important – albeit not entirely sufficient – to motivate users by giving them a vision of what is possible. A small share of users did learn JMP independently, but for the others, I had to deploy a good communication strategy and provide opportunities for support and personal training. That is what really helped us to gain speed.
Meg: One of the most common objections we hear from newcomers to JMP is that some fear statistical approaches will supplant domain expertise. Is that something you’ve had to address at Siltronic?
Georg: To me personally, this argument does not sound convincing. Domain expertise is always necessary to solve problems and is only improved and accelerated by statistical approaches.
In my opinion, this is a communication challenge: Statistical analysis should be done together with domain experts, not against. JMP’s ease of use makes it easier for domain experts to utilize statistical methods on their own.
Meg: You mentioned support and training resources, and I’m wondering how you’ve leveraged your relationship with the JMP organization – via things like Discovery Summits, STIPS and users groups – to build analytics capability.
Georg: I’ve used all of these resources from JMP and found them to be great and effective. In fact, it has really helped us to overcome the learning curve. It’s so important to be at the leading edge of a dynamically improving software.
Of course, the personal connection with the JMP team is also helpful and much appreciated, particularly when solving more advanced problems.
Meg: How has advocating for analytics transformation impacted the course of your career?
Georg: Using analytics and advocating for analytics approaches are a part of my work, so I can’t say that it has had any direct impact on my career trajectory. But it has enriched both a lot of my work and my network.
Meg: What advice would you give to someone looking to cultivate a more mature analytics culture in their organization?
Georg: Get management support on using data analytics – and communicate that support. Establish visible leadership in using statistical methods and clearly demonstrate to colleagues how statistics is supporting their work and improving decision making.
Meg: What advice would you give to someone who is just starting out with JMP?
Georg: Think about and define what you need JMP for. Then try to find a small first task that can be done regularly with JMP and concentrate on that small part of JMP until you feel more comfortable with it. Also, I recommend seeking out an experienced user and investing in building a network so that users within your company can support each other.
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