Sterling Snead, a principal in the Broken Arrow, Oklahoma-based Snead & Stoffel Global Family Office, is the founder and CEO of the Self Research Institute, an organization building a protocol that lets humans decide what personal data is shared with health and banking services, cellphone providers and on social media. Snead shared his story with Crain Currency.
Talk about your background and what inspired you on this journey into harnessing the power of data science?
I come from a single-family office where the family business is data science. It started in the energy sector and expanded into the agriculture sector. We're a young family office, started by my father. He grew up very poor, and he got a full scholarship to be a doctor. And he turned it down to go do volunteer work and fell into the energy business in Oklahoma. We’re kind of unusual in that we never exited from the legacy business. It's still going on, and it averages a 3-to-1 return every two years. Basically, it was [the] data science of finding people that the oil companies owed money to.
I also saw my family using their data to improve their own quality of life. My sister found her own cancer. And my oldest son is special-needs—when he was 1½, they told us, “You’ll be lucky if he ever throws a ball back and forth.” And now he’s 10 and doing great.
It wasn’t the data, but the data helps find the solution.
About five years ago, I was having organ failure, and the doctors didn't know why. My sister said: “Look, you have all this data on yourself. You've been experimenting with your data. You need to go to a new physician.” So I did and showed him everything. I said: “Look, I think I have celiac [disease]. Can you test me for it?” He did, telling me: “That's exactly what you have. And if you hadn't found it, you wouldn't have lasted another year.”
I’ve been in waiting rooms with all these parents who didn't have the resources I had or the supportive family that I have. I thought, “I’ve got to use data somehow to help people like my son and help their families.” I originally started two entities, a benefit corporation to build software and a scientific research institute — because it became, “OK, how do we use the data?” I had to hire a bunch of Ph.D.s to figure out how to do this.
I realized that you needed to basically standardize or map an entire human being in data in order to actually get any benefit for this type of application. So we’re releasing our first version of the Self Data Atlas this year. Then we built a custom platform and solution to store and analyze and process the information using graph databases, a custom-made blockchain. We’re building a custom LLM [large language model] for the processing and information. Everything is self-sovereign, which means that the individual owns and manages their own data.
What is the Self Data Atlas? How does that work?
What’s interesting is you say, “How does my data affect me, and how can it improve my quality of life?” And if you go back to the study of human categories or [American psychologist Abraham] Maslow's hierarchy, and you say, “What are these different categories, and how can I store the information?” then you start understanding. There's small ontologies in different fields that people haven't really integrated together. So if you think about it, there's not really a personal finance data model that is widely accepted. How we calculate credit scores in the United States is a different equation than how they calculate a creditworthiness score in Europe. In India, 99% of the population only uses debit cards. So you can't really build an app that recommends personal finance in certain ways unless you build in that differential, right?
How would a user interact with the Atlas?
Two ways: We're building the GitHub of human data. You can connect to professionals, you can connect to whatever you need. And we're building a gamified front-end interface that's highly 3D visual. So we're focusing on data visualization in ways that people haven't before. Long gone are the days of just relational sheets or a bar chart or line graph of what your health looks like. We can use new and innovative features like spatial computing from Apple and all these different things in order for people to look at their data in more innovative and creative ways that are naturally understandable.
In terms of user experience, what’s the next step? How do you use that information?
There are basically two main products — the Atlas, the map, the data map, the ontology. And then there's the whole technical framework that uses that information. So what we did is we built a front end to that framework for users to utilize.
I’ve found that family offices want to lead this charge because they're the most flexible and unusual and nonconforming entities out there, right? They have that long-term patience for a project that might not return a profit within six months.
We built a gamified, highly interactive app to interface with the data. And then what it allows you, the individual, to do is to set their goals. Then the AI will work on the person's data to help them achieve those goals and say: “OK, this is your data. This is where you need to go from here.” There will be a marketplace where individuals can participate in research studies just like Apple has for research studies. We've built the first royalty structure where a person can sell their data to somebody else. That person then again sells it to somebody else. And the individuals is getting royalties on the data.
And where do you see this sort of developing — where it will be five years from now, 10 years from now?
Somebody asked me about longevity investing the other day. One of the interesting things about it is that longevity investing is good, but it's kind of a wealthy man's game at the moment. What really needs to be focused on is quality-of-life measurements. The World Health Organization actually has quality-of-life metrics that we are comparing the data against. A colleague in Europe wrote a book that's amazing. It's about measuring the quality of life and how it's adaptive per individual. So some people want to be minimalists and go do van life. Some people want to be wealthy and have a large house. So, you know, the quality of life is subjective, but you can build the data and the AI around some objectivity to achieve those subjective goals.