Dialogue: A Future of Data-Driven Leadership
With expertise in data science, Dow’s Amanda Ahrens has a unique understanding into what the power of data can mean for the future of manufacturing and its role within R&D, sustainability, asset optimization and more.
Last June, Amanda Ahrens of Dow was honored at the Manufacturing Leadership Awards as the winner of the Next-Generation Leadership category, which is given to a high-performing emerging leader aged 30 or younger. Her background in mathematics and data science is emblematic of the emerging role of data analytics in manufacturing – a true embodiment of the industry’s future.
Ahrens has experience with both corporate IT and R&D during her time at Dow, an unusual and challenging career path but one that gives her unique insight into different areas of the business. She is also a strong advocate for promoting STEM career for women and minorities, serving as a volunteer for the Michigan Council of Women in Technology, computer science coordinator for Dow’s Girls in STEM program, and a coordinator and lecturer for an introductory data science programs for HBCU undergraduates. In this interview, she discusses data’s growing role in manufacturing, how it can provide fuel for ESG initiatives, and what she thinks are the most exciting use cases for AI.
Q: Data science has a growing and important role in manufacturing. How do you see that role evolving in the next 3-5 years?
A: It is becoming easier to access data to build models, deploy models, and maintain models. Data quality, however, has always been and continues to be a barrier to building models that provide useful results. As manufacturers see a return on investment regarding data science, if reinvested to maintain high-quality data (by sharing the results with data owners, investing in soft sensors and AI enhanced sensors, investing in systems fit for purpose that enable fast and easy data cleansing and analysis), other use cases will be unlocked. AI and data science is best when people don’t even realize it is there and will need to be embedded into modern processes and tools to realize the full value cases.
Q: You started at Dow as part of the corporate IT team before making the switch to R&D. What was that transition like for you, and how does data analytics play a role in R&D?
A: My internal clients were completely different in corporate IT, R&D, and Manufacturing regarding what data they regularly worked with, how they used data, and how they wanted to receive data and data analysis. During my transition, I learned about my new client personas. In R&D, data analysis is used to design experiments, analyze lab test results, identify product portfolio gaps for new product opportunities, predict formulations to deliver desired properties, and many other use cases. The data in R&D is very wide, meaning that there are fewer rows (or samples) than columns (chemical structure, properties, applications testing results). Wide data requires data analysis techniques that are meant for this type of data.
Q: Your role has evolved to include ESG initiatives, working toward the goal of reducing Dow’s carbon emissions. How can manufacturers use data to further their sustainability goals?
EXECUTIVE PROFILE: Amanda Ahrens
Title: Sustainability Data Architect – Enterprise Architecture (Houston, TX), 2021 to present
Education: Michigan State University, B.S., Mathematics; Texas A&M University, M.S., Analytics
Previous Roles:
– Data Visualization Specialist – Advanced Analytics (Midland, MI), Dow
– Research Informatics Analyst – R&D Information Research (Midland, MI), Dow
– Data Scientist – Digital Operations Center (Houston, TX), Dow
About the author:
Penelope Brown is Senior Content Director of the NAM’s Manufacturing Leadership Council.
