ADVOCATING FOR ANALYTICS
An interview with Alicia Garcia-Tunon of Johnson & Johnson Vision
On how advocating for statistical approaches has shaped her career as a process engineer and leader in analytics enablement
Process Engineer, Johnson & Johnson Vision
User Reference Manager, JMP
Process engineer Alicia Garcia-Tunon has worked for nearly 10 years at Johnson & Johnson Vision, a leading manufacturer of eye health, vision correction, LASIK refractive and cataract products. A longtime advocate of statistical approaches, Alicia is now co-leading efforts to develop a new data analytics unit within the company’s process engineering organization.
“For any new role I have taken on throughout my career,” she says, “I have relied heavily on data-based problem solving and focused a lot of energy making data easily available and visual to drive improvement.”
Prior to joining the J&J Vision’s process engineering organization, Alicia worked in the pulp and paper industry. She holds a BS in chemical engineering from Florida State University.
Meg: As someone coming from a chemical engineering background, I’m wondering how your advocacy for analytics transformation has impacted your career trajectory. Have you encountered opportunities that might not have been available to you before?
Alicia: The approach I take towards data analytics in each of my roles has contributed to most of the successful endeavors throughout my career.
I have always leaned towards using available data to make decisions and drive improvement, and the first thing I do when I take on a new role is finding the data that is applicable to the function. I then make sure that any pertinent data is easy to access and readily available. I create visualizations through various mediums to translate the data for business units to take appropriate action.
Through data analytics I have become a subject matter expert for the processes that I am responsible for, and by advocating the use of data analytics, I have helped my businesses grow in that area as well. I have recently been given the opportunity to co-develop a new unit within our process engineering organization that focuses on data analytics to drive continuous improvement projects and improve product quality.
Meg: How have you overcome skepticism within your organization that analytics enablement really can drive the improvements you’re talking about?
Alicia: The biggest hurdle I typically encounter in my advocacy for analytics is overcoming technical issues in getting reliable data that business units will trust. And challenging the status quo regarding improvements that might change how decisions are being made.
When people are used to doing something because it has always been done that way, it is difficult to implement change. If the data isn’t reliable or doesn’t add value right away, it makes change even more difficult. The resolution to these challenges has always been to first ensure that the data is available and reliable, and then to be clear and consistent in communicating the information. Consistently utilizing data to drive improvement will help minimize emotion-based decisions and demonstrate its value almost immediately, in my experience.
Meg: Data-driven decision making is what JMP is all about! People often talk about the value of JMP outside of just the software itself. Is that something you’ve encountered in terms of relationships?
Alicia: Since I took over the management of my group’s JMP software, I have had a great relationship with my user representatives. They are always keeping in touch to offer new training opportunities and have worked with me on several occasions to develop the site-specific training requested by my users.
They are always quick to answer any questions I have regarding the licensing and any software-related inquires. As JMP is a relatively new software for our organization, the assistance has been essential to building our proficiency.
Meg: What advice would you give to someone who is just starting out with JMP?
Alicia: JMP offers very good online training resources to help all users, beginners to advanced, and I recommend that new users explore them.
I would recommend taking the Statistical Thinking for Industrial Problem Solving course that is free online. This course teaches the fundamentals on how to navigate JMP, but also lays out a process to approach and solve problems using the software. Problem solving quickly and efficiently using a data-driven approach will add value to any organization.