Would you please talk about your cognitive science background and your transition to business? Was this a leap?
I was part of the first attempt at cognitive science in the 1980’s, as the discipline was inventing itself. We researched psychologists of all types, computer science researchers but also linguists, philosophers and even a few math/stat folks. This afforded me the opportunity to learn about anything and not be overly focused on one approach.
At the time, I was trying to use Artificial Intelligence models (Minsky’s Frames) to understand the relationship between learning, memory and attitude change. I had social, memory and experimental psychologists as well as cognitive scientists supervising my work, which proved to be challenging as no one faculty member particularly cared about defending my work as a whole. But I picked up a little bit in a lot of areas.
These skills, when put together, helped me enter work on projects that involved things like machine learning and intelligent computer-assisted instruction with Aerospace and Defense Research and Development at Honeywell. After that, I had the opportunity to work in a data science group at Merck & Co., where I could apply my understanding of behavior change and do things like price elasticity, promotion response, and marketing experimental design.
We are using machine learning techniques to predict the likelihood of trauma-care products needed in a given region based on weather, holidays and, other predictors.
I ultimately found my way into IT where the data is created (or not), and the capability to bring advanced analytics to major problem solving and the development of people-centered solutions is enabled.
Does cognitive science have a relation to what you are doing today at Johnson & Johnson?
I would like to say that I was smart enough to know that the methods and tools I learned in graduate school would be relevant to me as Johnson & Johnson’s CIO. This is not at all the case!
There are two primary aspects of my cognitive science background that are extremely useful today.
- The first is the statistical methods that we use to try to comprehend human cognitive behavior, which advances our understanding of consumers’ and patients’ behavior. We have long applied tools like predictive modeling to be able to identify what the next best action is to take.
- Now, with machine learning, the system continuously and manually updates the model without human intervention. This means that the productivity of our data scientists can be greatly enhanced. With the ability to create machine learning models that extend the reach and power of each data scientist, we can leverage the creative work our professionals produce and extend it more broadly throughout the enterprise.
To lead a data science group, it is helpful if you are an “outgoing introvert”…who enjoys sharing that work with people, creating teams that do this valuable work, and persuading business leaders that this is an area well worth the investment.
How did you get into the pharma industry?
My father had a long career in marketing, which resulted in my being born in Cincinnati, Ohio, while he was working at Procter & Gamble. Because of his career, I had always been intrigued by the idea of applying cognitive science techniques to marketing. So, once I decided it was time to get serious about establishing my career, I interviewed for a variety of positions, including a marketing job at Merck & Co.
Within its U.S. business, Merck & Co. had created a small group of what we now call data scientists, where we worked on things like promotion response assessment, hospital purchase probability modeling, conjoint analysis, and extrapolative methods for product forecasting. I don’t think there was any other time in the pharmaceutical industry until now, where a company like Merck & Co. would have hired someone like me. This gave me an opportunity to join a marketing development program where I rotated through roles like managing marketing research and product management. This business foundation has been enormously useful in my career leading IT organizations.
What are the specific applications of data analytics at Johnson & Johnson?
Data science is being applied to almost every business and function within Johnson & Johnson. It is being used to help us better understand and target consumer micro-segments in order to meet our people where they are – from the grocery store to the home. It is being used in the supply chain organization to understand how to create an agile response to emerging consumer demand locally, while planning on a global basis. We are using machine learning techniques to predict the likelihood of trauma-care products needed in a given region based on weather, holidays and, other predictors. Ultimately, we are using data science to identify and create people-centered solutions that help meet our consumers’ needs.
At Johnson & Johnson, I have been involved in all areas of data analytics. We have a group of about 30 data scientists in our IT organization supporting Janssen pharmaceuticals. I have played a role in establishing a group of supply chain professionals to model the supply chain flow, end to end. And most significantly, Johnson & Johnson created technology environments that allow us to break down the complexities of modeling, and support the massive amounts of data and power required for today’s techniques.
Is there a type of person that data science appeals to?
I don’t know if there is one type of data scientist. Data science work typically requires someone who is interested in math and in understanding the relationship of metrics to the real world. It requires the type of person who gets excited about finding and quantifying relationships that no one understood before. To lead a data science group, it is helpful if you are an “outgoing introvert”. An introvert can be defined as someone who gets their energy from quiet time working alone, while an “outgoing introvert” is someone who enjoys sharing that work with people, creating teams that do this valuable work, and persuading business leaders that this is an area well worth the investment.
One of the most important soft skills required for a top-performing data scientist is the ability to understand what your business partner is trying to do… If you take the time to ask your business partners, you may be surprised at how happy they are to share their work with you.
For people who consider themselves data scientists first, what hard and soft skills do they most need in the business world?
One of the most important soft skills required for a top-performing data scientist is the ability to understand what your business partner is trying to do. If you take the time to ask your business partners, you may be surprised at how happy they are to share their work with you. You can learn a lot in a short period of time about their business strategy, about the capabilities that are needed to accomplish business objectives, and the barriers that your business faces in accomplishing those goals. Once you have this knowledge, you can aim data science techniques at overcoming the most important obstacles. If you have helpful solutions to answer your business’ most critical problems, you will find a ready audience.
Lastly, what one thing would you like people to know about your field or your work at J&J?
Many individuals know about Johnson & Johnson from our Band-Aids® and baby products. But in reality, our two largest businesses are pharmaceuticals and medical devices. We are a health technology company fueled by empathy, and we ultimately attract people who are interested in our core mission which is helping people live longer, healthier and happier lives. If you’re interested in the opportunity to contribute to humanity in this way, and would rather develop people-centered solutions then say, tune search engines or adjust hedge fund algorithms, then we have a place for you!