Women are very intuitive and those powers are very strong when it comes to “whispering” insights out of data. There is an art and a science to AI, and for this reason, women are perfectly suited for a career in this field.
As part of my series about the women leading the Artificial Intelligence industry, I had the pleasure of interviewing Julie Schmidt. Julie is the Senior Vice President of Analytics & Insights at Allant Group — a “model driven” marketing service provider that uses data to generate insights (e.g., customer likes/interests/behavior) to execute personalized campaigns that acquire, retain or win-back customers. She joined Allant in 1998 as the founding of member of the data science team and currently leads its Analytics & Insights practice. Her focus is on building solutions that drive value through closed-loop marketing optimization; from planning and targeting through execution and measurement — driving value at all points in the customer lifecycle. Julie speaks frequently at events nationwide and is an active member and President-Elect of the Chicago Chapter of the American Statistical Association (CCASA).
Thank you so much for doing this with us! Can you share with us the ‘backstory” of how you decided to pursue this career path?
I always found math as an easy subject in school. My older siblings would teach me how to do their math homework and then have me do it. So, I started college as a math major, thinking it would be an easier route to get accepted in the school and then I would change. That change never happened.
NCR [a global leader in developing transformational transaction technologies] was a large contributor to University of Dayton. As we were playing more and more with personal computers, I had this thought that they would generate a lot of data/information and statistics might become an interesting field. This was in 1988 mind you, so it was probably one of the best thoughts I have ever had — it obviously became truer than I ever thought it would. So, I started my career at IRI, in a data quality role, and then moved into a data warehousing role as a consultant. These two jobs laid a firm foundation for understanding data, data structures and business intelligence. As I finished my masters in statistics, an analyst role arose and I’ve been running with it even since.
What lessons can others learn from your story?
Follow your heart, your gut and do what comes naturally for you. Find something you love to do, that you’re also good at — and figure out how to get paid to do it. Find super smart people in your field and learn as much as you can from them. But, don’t do your siblings homework!
Can you tell our readers about the most interesting projects you are working on now?
We are working on a new approach to understand customer journeys and how to identify the unique parts of the customer journey that drive consumers to make decisions with their wallets. A journey is the way a customer continually uses all the available channels (physical store, email, calls, website, social media) to interact with a company. What’s most interesting is that every customer journey is so unique. So, we are looking for patterns or very small differences that might drive consumers to better experiences — and marketers to get more from their marketing budgets as a result. We are in the early stages, but the results are extremely promising.
None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful towards who helped get you to where you are? Can you share a story about that?
Early on in my career I worked for two people who were extremely smart mathematicians/statisticians. They were also great strategists and huge influences in shaping my career. Both worked at Price Waterhouse prior to joining Allant and are extremely smart (but understood the business side as well) and hard-working. I joke that I made my career out of understanding what they were envisioning and figuring out if it were actually feasible or not. Then, I would serve as the “translator” to everyone else (the non-mathematicians), bringing it all down to the layman’s terms to build something or operationalize a process. As I said, I tell people to surround themselves with the smartest people you can and be a sponge — that’s what I did.
What are the 5 things that most excite you about the AI industry? Why?
New Use Cases:
- While I’m not in the medical field, the AI applications that are in use to support advancements in medical diagnostics and patient care are inspiring and the most impactful to our society.
- IoT [Internet of Things]: There are a lot of interesting use cases where AI is being put on the edge for decisioning.
- Just the sheer exposure of this field — people actually are beginning to understand what I do. I don’t get “you’re an actuary” as much anymore.
- The opportunities to leverage past experiences to shape new use cases. At the end of the day, AI starts with data, and a very deep knowledge of data continues to be required.
- The idea of “explainable” AI, which will likely remove a lot more barriers to operationalizing some of these solutions.
What are the 5 things that concern you about the AI industry? Why?
- My top and most important concern is: “just because we can, doesn’t mean we should.” Essentially, there are some use cases that might be interesting from an academic perspective, but don’t really serve a business or “healthy” need. For example, facial recognition to determine emotion. I think we need to ask ourselves “what purpose does this serve?”
- I’m sure many people would say they are concerned with the idea that AI will eliminate jobs, but I don’t believe this to be true. I do think skills will change and a potential concern out of that is that the U.S. will not be able to fulfill these jobs — even globally, we may not be able to fulfill the need.
- Intelligence (which is what AI is meant to generate in the first place) in the wrong hands will be dangerous.
- Transparency — but this is being recognized in the field and hopefully will be addressed soon. We need to be able to explain the decisions we are making and why. There are several use cases in regulated industries, so we are starting to see progress here.
- The idea that AI will replace humans, and the fear that is driven from that concept. I like the idea of AI standing for “augmented intelligence” and not “artificial intelligence” — because there is nothing artificial about what comes out of these solutions and what AI can do for businesses.
As you know, there is an ongoing debate between prominent scientists, (personified as a debate between Elon Musk and Mark Zuckerberg,) about whether advanced AI has the future potential to pose a danger to humanity. What is your position about this?
I don’t usually take strong positions on things that require someone to “take a side.” There is good and bad in all things.
Rather, I go back to: just because we can, doesn’t mean we should. In our lives we have to decide what technologies we adopt and those we don’t — it will be personal, and people will be guided by their goodness. When you go to the auto show, not all the “cars of the future” end up being built — but there are interesting learnings along the way that help shape our society in ways people don’t realize — like the Tang example from NASA. We all need to keep our moral compass and people will decide what they adopt & what they don’t. For everyday people like you and me, direction of society is not driven by Elon and Mark. They are driving technology in many ways, but that’s not everything.
What can be done to prevent such concerns from materializing? And what can be done to assure the public that there is nothing to be concerned about?
Much of the issues will come to bear with consumers’ privacy about the data that they generate — especially when it comes to image recognition. Mark understands that now because consumers have reacted to the “Cambridge Analytica” issues. There NEEDS to be more transparency. So, in general, I go back to “just because we can, doesn’t mean we should.” If you can identify whether a customer is smiling at a kiosk, or mad, are you really going to treat them differently? And, do we really need AI to consider whether someone is happy or sad?
How have you used your success to bring goodness to the world? Can you share a story?
I tried to leave the business world to become a high school math teacher — this was how I was going to “make a difference.” I realized through the journey that working in business as a statistician can have an impact as well. I discovered two main ways. First: through being a positive mentor & influence on the people that work on my team and that work for my company. We spend more than half our waking hours with the people we work with, so we should treat them the way we want to be treated. Second: if I help a company improve their bottom line, I’m helping the employees of that company as well as the investors.
As you know, there are not that many women in your industry. Can you share 3 things that you would you advise to other women in the AI space to thrive?
- First: don’t let the fact that you are a woman stop you from doing anything.
- Second: don’t even notice that you are the only woman in the room. If it doesn’t matter to you (hopefully) it won’t matter to them.
- Third: women are very intuitive and those powers are very strong when it comes to “whispering” insights out of data. There is an art and a science to AI, and for this reason, women are perfectly suited for a career in this field.
Can you advise what is needed to engage more women into the AI industry?
We need to encourage more girls to become engaged in mathematics at the high school level and even before then. There are many studies regarding the lack of women in technology fields and the best thing we can do is encourage girls to take advanced topics in math at a young age (high school). The odds of her moving on to a STEM (Science, Tech, Engineering and Math) career are much higher if she takes an AP class in high school. There are also a lot more programs in Data Science at the universities. This will help in general, but we need to get more women in businesses engaged with these programs through hack-a-thons and other similar competitions to show other women that we are out there.
What is your favorite “Life Lesson Quote”? Can you share a story of how that had relevance to your own life?
“Honesty is the best policy.” It’s so simple but true. I have a great “lesson learned” story from early on in my career. I accidentally sent an email to one of my analysts with a remark about how the client data was “not perfect” (not the words I used). I didn’t realize that the client was CC’ed on that email. When I realized it, I immediately picked up the phone to apologize. He hadn’t read the email. He actually had to go into his trash folder to pull it up and read it. He laughed and agreed with my comment about the status of his data. The lesson learned: from that point on, he trusted me completely because he knew that I was an honest & sincere person. We had a great relationship from that point on. These aren’t the typical lessons you’re taught in business school, but it was one of my best lessons early in my career that has stuck with me. Building trust goes beyond WHAT you do and is more often about HOW you go about doing it.
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. 🙂
My movement would involve getting more business experience into the high schools (maybe into the colleges too) as practitioners that can evoke some passion into the age-old question that students ask: “when am I ever going to use this?” My personal experience is that many educators can’t answer this question, in math and likely other subjects. There should be quotas on the percent of teachers in every subject, that have 10 or more years of practical business experience in that subject.
How can our readers follow you on social media?
This was very inspiring. Thank you so much for joining us!