Data Driven Chat: “It was something that really mattered.”
Ganna Pogrebna in conversation with Briana Brownell — The chapters of her data science journey, impactful projects and current work in health care
Academic, Educator, Consultant, and Blogger Ganna Pogrebna started Data Driven Chat to explore the many interesting aspects of behavioural data science including human behaviour, data science and AI. Ganna Pogrebna is Professor of Behavioral Analytics and Data Science and a Lead of Behavioral Data Science strand at the Alan Turing Institute — the national centre for AI and Data Science in London (UK). The Data Driven Chat is the official podcast of the Behavioral Data Science Special Interest Group at Turing.
Data scientist turned tech entrepreneur Briana Brownell is the founder and CEO of Pure Strategy Inc. Using the leading edge technology in natural language processing, machine learning and neural networks Pure Strategy’s Automated Neural Intelligence Engine (ANIE) gives organizations the insights needed to make decisions confidently.
The conversation originally appeared on Data Driven Chat. Click here to listen.
This is Part 1 of a three-part series.
Ganna: Hello everyone, this is Data Driven Chat and today we have Briana Brownell with us! Briana is a founder and CEO at Pure Strategy Incorporated. Hello Briana! It’s really nice to see you and to meet you.
Briana: Wonderful to see you and meet you as well!
Ganna: Thanks for doing this. I know you’re very busy and it’s challenging to find the time right now, but thank you so much for coming on this podcast and for sharing your ideas with us.
Briana: You’re very welcome.
Ganna: So to kick off our discussion, it’s my understanding is that you work between decision science and data science and I’m really excited about talking to you because that’s what I do as well. I do the academic side and I very rarely meet people like you on the business side, so I’m really excited. How did you become interested in this area? Can you tell us a little bit about your personal journey?
Briana: When I went to university I actually wanted to be a theoretical physicist. I was really interested in physics all throughout high school. I loved Stephen Hawking and I was so fascinated by quarks. I just really loved particle physics. But when I got into university I found it was the mathematical courses that really pulled me in and made me excited about some of the possibilities around mathematics. At that time mathematics was extremely unpopular — when I graduated, I was the only person who graduated with a degree in math in my year. Nobody could understand why I wanted to major in something that was so impractical and so bizarre.
My first job right after my undergraduate degree was in finance. I got a job as a prop trader on the NYSE in 2006 and I absolutely loved it. It was such a fascinating experience to be working in finance at that time. But of course, you probably know what happened next. The Global Financial Crisis happened next! So everything started to fall apart about 2007 and so I left the finance industry at that time.
“Finally after so many years of people not understanding why I wanted to work with mathematical models and work with data, they started coming around to it. I love that now there’s so much interest in data science.”
But what really stuck with me was that I could see how human behaviour was really an important part, a crucial part of some of the mathematical models that I had been creating. I started to get really interested in that human behaviour side and I started working as a data analyst. We didn’t call ourselves data scientists then because back then the term hadn’t been created yet. I was working with different companies on some of their challenges to do with data — doing things like prediction, understanding consumer behaviour, and understanding some of the deep aspects of why people make the decisions that they do.
After a few years, companies started being really really interested in using their data more effectively and so I couldn’t have found myself in a better place. Data science was growing in popularity and I felt a little bit vindicated that finally after so many years of people not understanding why I wanted to work with mathematical models and work with data, they started coming around to it. I love that now there’s so much interest in data science.
Ganna: Yeah, now it is the hottest field today. For those people who do not know your work, if you had to pick the one most important thing you have achieved during your career what would you pick? What would it be?
Briana: One of the things that I’m most proud of is the work that I did in Australia with the Australian government to understand decision making and technology adoption by primary producers.
In 2007 there was a very severe drought in Australia and it was absolutely devastating. We looked at why people were adopting technology in order to mitigate their risks due to climate variation. This research lasted for probably about eight years. I started in 2007 and we were still publishing in 2015.
It was just absolutely fascinating because it really linked the work that I was doing with data to people’s lives. Instead of it being just a bunch of numbers in a spreadsheet or an algorithm that I ran, it was something that really mattered. It was really impactful and I loved working with that. We had a great research team and I was so lucky to work with them. It really taught me a lot about how data could be used for improving the world.
Ganna: Yeah and I can see a lot of similarities with myself. I also was a lab scientist and then I finally got a job in an engineering department just because I wanted to see my models actually work in the real world so I think this is very important.
Briana: It’s so satisfying, isn’t it?
Ganna: It is, it is. So can you tell us a little bit more what about what are you currently working on? What keeps you awake at night? What excites you?
Briana: I’d mentioned the work that I did in Australia. One of the big components to that was creating typologies to understand how different individuals make decisions in similar ways. We found different groupings of individuals had the same worldview, had the same attitudes and beliefs and by understanding those attitudes and beliefs we could predict behaviour. We could intervene and encourage behaviour that we wanted to encourage.
So for example, mitigating risk as a result of climate variation was something that we wanted to encourage because we wanted people to have their livelihood be protected. Now we’re using that methodology to understand decision-making with physicians and patients in the healthcare industry.
“We found different groupings of individuals had the same worldview, had the same attitudes and beliefs and by understanding those attitudes and beliefs we could predict behaviour. We could intervene and encourage behaviour that we wanted to encourage.
So for example, mitigating risk as a result of climate variation was something that we wanted to encourage because we wanted people to have their livelihood be protected. Now we’re using that methodology to understand decision-making with physicians and patients in the healthcare industry.”
I find it really fascinating because rather than treating patients or physicians like some monolithic group that does the same thing, you respect the variation and the different world views that people have.
We’ve done this in multiple areas within healthcare to understand things like why is it that patients decide not to seek care for symptoms that they’re experiencing? Sometimes they feel embarrassed, sometimes they feel resigned to some of the symptoms that they’re experiencing. We want to encourage people to lead the healthiest lives that they can, so understanding why they may not choose to seek care is extremely important.
Then looking down the line, we also see individuals, when they have a treatment plan for a condition, they may not maintain their treatment on that plan. They don’t take their medication at the right time or they don’t schedule follow-up appointments or appointments with other health care professionals that they need to see.
By understanding the behaviour and the core attitudes and emotions around decision-making within the patient group, we can plan interventions. We’re finding out how challenging healthcare can be around the world with COVID-19 and all of the different factors that feed into people’s decision-making on how to keep their health to the top shape that it can be. I think it’s just really really important to be able to understand those behaviours and those motivations.
This is Part 1 of a three-part series. Click here to listen to the whole conversation.
Part 2: Data Driven Chat: “The potential of data is not realized in most organizations”
Ganna Pogrebna in conversation with Briana Brownell — Underrated skills in data science, getting executive buy-in, and how we can all shape the future
Part 3: Data Driven Chat: “We didn’t do a very good job of building resiliency into really important industries.”
Ganna Pogrebna in conversation with Briana Brownell — What COVID-19 showed us about data science, how life after COVID-19 will be different, resilient systems and recommended reading and watching on AI.
