My Career in Data Season 4 Episode 04: Ron Zionpour, Chief Technology Officer, Healthee

Hello and welcome to My Career in Data, a podcast where we discuss with industry leaders and experts how they have built their careers. I'm your host, Shannon Kemp, and today we're talking to Ron Zionfore from Healthy. With a robust catalog of courses offered on demand and industry-leading live online sessions throughout the year, the Dataversity Training Center is your launch pad for career success. Browse the complete catalog at training.

dataversity.net and use code DBTalks for 20% off your purchase. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer at Dataversity, and this is my career in data, a Dataversity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to talk with people who help make those careers a little bit easier.

To keep up to date in the latest in data management education, go to dataversity.net forward slash subscribe. Today we are joined by Ron Zionfore, Chief Technology Officer at Healthy. And normally this is where a podcast host would read a short bio of the guest, but in this podcast, your bio is what we're here to talk about.

Ron, hello and welcome. Hey Sharon, how are you? Good, good. Thank you so much for doing this today.

I'm excited to hear your story. So tell me, you're the CTO, the chief technology officer at Healthy. Um so for those who may not know, what type of business is Healthy? All right.

So yeah, Healthy is uh is a SARS B2B company uh with a great and and pretty simple mission. Uh we want to give people access to better healthcare without any effort. Uh you use AI-based technology and data to turn something that is usually confusing into something uh clear and easy. Um you can think about Healthy as kind of your personal guide to your um health benefits.

We help people understand their coverage, what things cost, which doctors are in network or out of network, how to make smart choices and you know save money for themselves and their employer. And one thing I love saying is that, you know, people do not, at least most of the time, do not think about health insurance the way they think about other products, right? On average, people spend like 10k a year on health plan, obviously themselves and their employer all together. Uh, but most of them, you know, they don't know what they get in it or how to use it properly.

And I sometimes ask people when I introduce healthy, is you know, would you spend $10,000 on a boxing, you know, from Amazon without knowing what's in it or how to use it? And obviously the answer is of course not. And that that is how we see people actually feel about their you know benefits today. And that is exactly the problem we're solving at healthy because the health insurance is not an option for anyone.

Everyone has to pick one, and it's not just their medical, their dental or vision uh plans, it's also their voluntary benefits like life insurance, for example, or disability. And then you have all your point solutions and extra programs from your employer. And on top of that, it's actually not just for yourself, it's for yourself and your family. And it gets complex and expensive very quickly, uh, both for the employee and the employer.

And our job here at Healthy is actually to make all of that simple and clear uh using AI and data so people can actually understand, you know, what they have and how to use it. Oh, I love that. Because I I am one of those who have struggled. What do I have?

I was just looking it up the other day. What do I have? Um so that's great. So as a CTO, what is it you do?

What's your day look like? Yeah. Yeah, I think like I can probably split it into, you know, um, probably two or three or four kind of big pillars, the way that I see my role, at least. It's obviously the first and foremost is is our business and and you know, our goals as a company, you know, focus on understanding our business needs and and the company goals, and obviously translate that into our product and technology, um, you know, helping, you know, kind of turning or or or kind of achieving the business opportunities and goals using the technology and the product that that we have and kind of deliver.

Uh the second pillar I would say probably is the leadership collaboration. So I'm working obviously, you know, uh with the rest of the leadership, the head of departments. So we collaborate to stay aligned and to kind of move in the same direction as the company. Um the third one would be probably the product itself.

You know, I lead what we call here at Healthy the makers team. Those are you know the people who build and make and make the product that we deliver here at Healthy. Um, you know, the product management team, the product designers, the RD all together are working on obviously turning business goals into the features and the real solutions that we have for our customers. And obviously the rest is, you know, the fourth pillar probably, and the rest of my job is obviously the people themselves, like the team itself, you know, working with the people, make sure I spend a lot of time to make sure that you know they are kind of you know, they have what they need to grow, to succeed, to keep building uh our product and kind of move blockers along the way.

Um so if you want to kind of to summarize that in in one sentence, I would say that my role is kind of connecting our company vision and and goals into execution uh using our product and and technology, obviously. Okay, very nice. And I love that you mentioned that a huge part of your uh job is uh managing the people. I think uh so many people I don't think realize what a big piece of uh the C-suite it's that role is.

Yeah, I agree. So so tell me, how do you specifically work with data in your job? What data are you using day-to-day? So I think maybe before I jump into the data itself, you know, I I want to share something I always say.

I think like a lot of people put a lot of focus on technology, mainly in the recent, you know, two years, people focus on AI-based technology, LLM, generative AI, um, and so on and so forth. And there's a huge hype around it. And I want to say that, you know, I'm seeing healthy and and what we do at healthy with, you know, with that with that statement is like, I think like AI and technology is our accelerator, but data is our remote. And I think what we've built here at Healthy is mainly because we have a strong data and data entity and data model behind it.

Um so at Healthy, you know, if if to speak about kind of the data that we use at Healthy, is that we collect and understand everything in person's benefit, right? Uh that includes the medical, the dental, the vision, everything that I stated, kind of mentioned before, all the pieces we talked about, like, you know, things like provider information, their plan designs, you know, facilities data, networks, procedures, prices, you know, people deductibles, everything that is affecting their healthcare and you know, health benefits and can help us, helping them navigate better uh in that world.

And on top of that, what we do is adding into our data layer everything that is around medical conditions, symptoms, CPT codes, uh, you know, everything around pharmacy, for example, and a lot more. Um, and all of that is to create the best data that we can bring into our product and our AI-based uh technology. Every small piece of data, I think, in my opinion, at least matters. I think what makes it so unique and healthy so unique is that over time we just enriched more data into our platform and created the better granularity and more details into our data layer.

Um, in my day-to-day, you know, at healthy at least, I also look at the kind of privacy and security aspects of data, obviously, and how we kind of connect all of them together, uh, all the entities that and all the data sources that we consume and digest into healthy. Um, we are starting with analyzing, you know, unstructured data, which is very complex and and not so easy to digest, uh and kind of combine that with structured data, um, you know, and that is consisting of our data model today.

So I would say that there's a lot about data at healthy and a lot of about my day-to-day, at least with data at healthy. Uh, I think the accuracy uh that you know in what we deliver comes from the data model that we build, um, which really gives us or allowing us to give people answers they can trust and help them make good decisions about their care. I'm so glad that you bring it up that uh it's such an important point. So many companies um rushed into AI because it is an accelerator, right?

There's so many advantages to using it. But if you don't have that mode, if you don't have that foundation um to build on, it we we've seen it go awry. Yeah, I agree. And I always say, by the way, that you know, uh, you know, when we introduce Zoe, for example, which is our AI assistant, people tend to confuse it with ChatGPT.

And I always say we we didn't aim to change to build something that is ChatGPT-like. There's a lot of hallucination, there's a lot of satisfaction uh from talking with ChatGPT, and that's not the idea behind what we built here at Healthy. We started with data because we know that with the ground truth and a solid ground truth of the best data that we can put here and a very controlled AI-based technology, we're leveraging over obviously LLM and generative AI, but we do it in a very controlled and kind of um structured way, uh, so we can deliver the kind of the outcomes that we bring to to those users and our customers.

That's great. Um, you know, we've seen a lot of uh we we do see that you know healthcare, um, financial industry uh have been you know much further advanced because of the uh regulations around their data. They've I think so. Yes.

I think it's it's not about advanced, though, because we are we are using the the most recent technologies out there. I think it enforces us because of the regulations to put the safeguards that you know other markets might not have to deal with it. And and we are facing it today, and it's super important and it's affecting everything that we do on the day-to-day here at Healthy as well. And do you think they give you a motivation to to you know have that data model and and have that your data structured?

100%. Yes, I think it does that, and I think it's also enforcing us in a way to kind of um rethink or thinking twice before we deliver something to consumers and users, right? It's not again, it's not about just bringing the value, it's about bringing an accurate value to our users. Uh yeah, we've seen a lot of uh industries that aren't so regulated be resistant to to data governance um and thinking it's a dirty word, and but it really the industries who've had to implement it, uh I think are have been set up to mobilize quickly on on these new technologies.

Um well a lot of it. So okay, so let's talk about how you got into this role. Um so so tell me, Ron, when you were very young, say six years old, was this a dream? Did you say I'm gonna be a CTO um at healthy, I'm gonna be a CTO at a at a health company?

I I don't think if if you were you know mentioning the the three letters CTO, you know, at the age of six, I wouldn't know what it what that is. So obviously, you know, just to say that it I I haven't dreamed about obviously being a CTO. I think like, you know, even programming wasn't at my kind of earliest hobbies. Actually, I was more into electronics uh back then.

So I grew up in the 1990s. Obviously, there were obviously computers out there, and I know like a lot of friends were dealing with programming, but my father was actually dealing with electronics, and it kind of you know picked me as well. So I started with, and I always saw myself, by the way, as kind of a problem solver or a creator. And I started with, you know, uh kind of you know, uh take things apart, like old electronics, things like old radios and and flashlights, and and actually put them uh back together in in you know in a different way.

I always wanted to turn something that is very old into something that is more useful uh for me and more exciting. Um and I had a lot of crazy ideas back then at the age of you know, maybe later, a bit later than six, but obviously when I was young. Um and I think it's you know, maybe probably the strongest memory that I have, at least uh, you know, from my childhood. So although, you know, it wasn't really what I wanted to do, you know, as is kind of you know continue with electronics, uh, I knew that I want to at least do something like that, bring values and create something that is better and and new for myself and later on, obviously, for for others as well.

And I think like today, the funny thing is that I'm doing something that is very different today, right? Uh, but still it's kind of probably the same thing. So, you know, my answer would be like a yes and a no. Uh no in the in the fashion that I wouldn't know what CDO is and not even dealing with programming.

But today, actually, I'm lucky to reassemble a lot of different parts, different data sources, and using best uh, you know, kind of cutting-edge technology, reassemble that into something that is, you know, creating a better world, uh, not just obviously for people like us, for our customers and our users. Um, so it's kind of the same thing and it's kind of a great closure the way that I see it, uh, but in a different market, different landscape, different role, different position, uh, different technology, obviously, but I'm still like taking a lot of pieces and connect them together uh to bring something that is way better than you know what you could have without that without healthy in general.

Visit dataversity.net and expand your knowledge with thousands of articles and blogs written by industry experts, plus free live and on-demand webinars covering the complete data management spectrum. While you're there, subscribe to the weekly newsletter so you'll never miss a beat. That's a great initial passion that you that you were born with.

Um, you know, and and that innate curiosity that we found and talk about on this podcast a lot that uh that data people kind of is very common amongst data people. That curious, how did how does this work? How do I put it, like you say, how do I put it back together? Um But so tell me, you know, as you start studying, you know, as you're getting older, you know, where did your passions start leading you towards?

What do you start studying? What do you start, where did you start? Yeah, I think like probably at, you know, when I uh kind of reached the college age, I started to see like, you know, how can I turn kind of you know myself as being a problem solver and and you know, want to want to make an impact on the world and kind of take like or solve challenging real world problems, you know, I started to think, okay, which way should I pick, obviously, and what should I do uh to kind of accomplish that?

Uh and I, you know, it took me to technology, to to programming, because I was really good at math and and science, and it was kind of uh kind of my path on direct path on making impact and and take something that I I feel like I'm gonna be good at. Uh so I went to study uh my degree at computer science. Even before finishing it, actually I started as you know in a student job, kind of in a large corporation. Um and and it was it felt nice actually, because I really, you know, as a student, it's important for you to start actually do something that that is real and not just you know um study all day.

Uh but as soon as I finished college, actually I you know looked at my position, my full-time position now in the same corporate. And I felt like I'm I'm not doing the the kind of the impact that I promised myself that that I would make. Um so I left and and joined actually an early stage startup, which is since then it's it's my passion to do something you know from scratch and build things uh from you know bottom up. Um and I started actually in a startup that is in the ed tech industry, so completely different industry than today.

Um and I stayed there for probably more than eight years, I would say, I would say eight to eight and a half years. Um I started as a junior developer and actually ended up as VP of Engineering. Uh, throughout that year, I kind of grew with the company. I started when we were like probably 60 people and ended when we were about 500 uh people at the company.

And I learned a lot about myself. So I know I knew like I'm starting to love working with people more than I love writing my the code myself, but also at the same time being very strategic. I love leading others. Um, you know, I also care about kind of the details, and I, you know, I immediately thought about okay, if I'm not going to be like hands-on and write code, then at least I'm gonna stick to you know the details that we have, the technicals and and data decisions that we're making as a company.

And I always made sure that I know the platform better than anyone else. Um, after those eight years, I decided to move on to another market and a different product, actually. So I started to, you know, at the company, a SAS product company called Monday.com uh for a while.

Um, I joined there to build a new product also from scratch and kind of build team around it, um, which is, as I said, always my passion. Um and that is the first time that I actually saw not just technology and data, but also how product consumerism and user experience working together with it. And I fell in love with that combination. And actually, since then, I just moved to healthy about three and a half years ago.

Um, I think it's the best professional decision that I've made. Um, you know, it it brought together everything that I care about. I think people, technology, data, um, and and also the chance. I think I'm a very lucky person to have that chance of helping others in a very real way.

Um, and so something that is, I feel at least, it's a necessity in the US healthcare system. Uh yeah, I would agree. That's that's quite the journey. So, w what's been your biggest lesson so far in your career?

I think the biggest lesson is I wouldn't say it's a one case, probably. It's kind of I learned it throughout the years in many cases or many events that I saw. And it's what I called, and actually being called Mount Stupid from the Dunning Kruger effect. I don't know if that is something that you're familiar with, but for everyone that is anyone that is listening, obviously I'll share that, you know.

Mount Stupid, like by Dunning Kruger, you know, they they claim that as people learn a small amount of knowledge or or uh anything about a specific subject, their confidence actually grows very fast, right? It's it's not linear, it's being exponentially growing. Um and and they feel like they understand everything. But as you know, us people obviously learn a little more, you know, we suddenly realize how much we don't know yet.

And then our confidence obviously drops dramatically. And after a while, when we get more knowledge around that specific area or subject, then obviously it's starting to grow, but this time naturally and and obviously in a healthy manner. And I think that is a very great example and very good lesson learned, you know, to be able to even understand that, you know, mouth stupid, so you can avoid that uh from time to time. And if I need to give some examples, like if you take even healthy as a whole and the platform and what we solved in the domain, the benefits navigation domain looks you know simple from the outside sometimes.

I know that people understand how complex benefits can be, but they can see that solution is something that is might be very easy to solve, but it requires a huge amount of knowledge and expertise. And there is, you know, the platform itself, the relationships that we build in our data entities, the challenges that you know, of bringing a lot of data sources into one data model, kind of connect everything all together. And obviously, on top of that, to create the technology and the product and the features that we deliver and think about the level of knowledge that you need to gain.

And I always say to people that are joining us, like that example with Mount Stupid, is that don't, you know, don't be too hairy to make some assumptions. Make sure that that you learn all the things that you don't know yet. And by the way, a great example around that, if if I need to even take like one theme or one example, like of now, we're working squads at Healthy. So each squad is responsible for a different domain or a product.

So two and a half years ago, actually, we started something new, kind of a complementary product or navigation tool around benefit decision support. And we call that tool the plan comparison tool. It helps people choose the best benefits for themselves during the open enrollment, right? We all need to pick a new health uh kind of medical, dental, vision plans, all the voluntary benefits all over again once a year.

And because of that, we developed obviously the plan comparison tool uh to kind of lay out all the different options for their employees so they can pick the best, you know, best options for for themselves and their families. And and think about like a team that we formed around this specific product and domain. Think about the complexity of understanding, you know, how users see all the comparisons of different plans, how we are going to kind of translate that language, that heavy language of health insurance into something that is digestible for them and they can make decisions around that.

So there is a lot of knowledge to gain here. And the trick here is, and and you can see throughout the time that you always climb into that Mount Stupid and realize what you don't know yet. You just realize that there's like 10 other layers that you just didn't, you know, discover yet. And and the idea around it, and I think that's the lesson learned here for everyone, is be or be aware of that Mount Stupid.

You can only sometimes only see it when you you do kind of retrospective with yourself. Uh, but if you are aware of that and you're kind of focused on understanding what you don't know yet, um, then it puts you in a situation that you can, of course, avoid Mount Stupid. What we usually do here at Healthy is that we work very closely with customers, uh, with users, obviously, to get feedbacks and to always seek of the things that we do not know yet and how we can improve our product and platform overall.

And I think that by itself just avoiding us from getting into that situation that we think that we cracked something, but there's a lot of things that we need to improve still. Uh so yeah, that that is my kind of lesson learned. Great lesson. I, you know, I I hadn't heard that term before, and and but it's you know, kind of sounds like the wave of uh our just our own human development, right?

You know, as teenagers, we think we know everything, or at least I did. We do it as a as an adult as well. So yeah, yeah. And then you you grew out of that and realized that there's you know, you don't suddenly reach a point where you know everything that there's just always gonna be things to learn.

It took me way too long to figure that out, but uh it's uh such a great lesson when you do it and just open your mind to always be learning, never you know, having to say, I know everything because it's impossible. Yeah. And I was just talking to somebody recently who was constantly serving their surveying their customers uh and said mentioned that you know that they didn't like when they got back uh all positive feedback because that prevented them from knowing how to grow and how to be better.

But um yeah, so um so tell me Ron, you know, uh with this mode of data um that you work in, what is your definition of data? Yeah, I think um, you know, just you know, even for myself, like there are many definitions for data because you know, I'm an engineer, I'm uh as you said, like a C level, right? I'm in the leadership, I'm thinking about the business, we have a company to run, we have a product to build, right? And I think, you know, when each time I'm wearing like a different hat and different, you know, position um in that aspect, it's like I'm seeing data differently.

So I'll start with with something that I strongly connect with. You know, I think data is is kind of the ground truth of everything that we do. And I actually I want to start with kind of the um interpersonal aspects of data. I you know, I run into and I work with a lot of stakeholders, and I always have that thing that I'm saying, like we can sit here and argue for weeks on opinions, we'll never agree probably on that, or we can just bring data to table and kind of you know discuss that and look at facts.

So, you know, I start with that because I think like that is a great explanation, the way that I see it, at least, and talk about data, you know, that you know, the fact that data is kind of the ground truth of everything that we do. Data are facts, are um, you know, things that we lay out that we can look at, and without any opinions, sometimes we can interpret that obviously differently, but at least it's something that you you cannot argue with numbers, right? On the technical side, I would say that especially in company like healthy, data is kind of the fuel for everything that we do.

I think it's not only the ground truth, but also powers the technology that we build, obviously, here and the product that we that we are offering to our users and customers, our AI technology and features that we bring, obviously, to them. Um and as I said before, data is kind of our remote. It is what gives us our edge and what makes us different. Uh, we know a lot of companies that try to um kind of crack something within any market.

They're trying to use technology, but in my opinion, it's just that's by itself by itself just not just not sufficient enough, right? Um, and and today at Healthy, we have access to a lot of data and the insights we can create from it are amazing. Um just recently, by the way, we started like uh to work on claims insight and and fraud waste abuse for our customers. That's a new domain that we're entering right now.

Uh, and we are using you know, customers' claims data uh with our algorithms kind of to find you know saving opportunities and cashback um, you know, and save millions of dollars back a year for them and using insights from their claims. So think about it that all claims and you know comes with mistakes. Uh that is something that is a fact, not an opinion. Uh, and all our algorithms are just running that through the reclaims, the customers' claims, just to find those mistakes.

Also, another thing that we do is finding like programs they they should adopt, probably, you know, things like weight loss or diabetes or mental health or anything else that is derived from their users, their employees, and their claims. And that is something that, you know, the way that I see it, if I need to kind of think about how we started that new initiative, is that we knew we have the data, we knew that we can use the data, we knew it's again, it's a ground truth of everything that we build.

Now it's just another technology around that and how we build our best technology around that to provide kind of value to our customers. Um we do a lot today at Healthy, but you know, um, if I go back, you know, when we started, we understood the power of data, I think, very early. Uh, even before the Gen AI boom, uh, you know, and the amazing capabilities that we have today with all the features and the products that we added uh throughout the years, um, you know, kind of we build our business on top of the data.

So we saw it right at the beginning that if we build it correctly using data, um, then you know, sky is the limit probably with the features and the products that we can can create above that. Um, and even now, by the way, it's it's like we continue to kind of improve that. So we are entering a new year. I'm running right now with the team a major data project uh that is focusing on improving the existing.

So, kind of going back to what I said before, we we know what we don't know yet. And it's the same goes for our data. We know what we are still missing in our data, and we improve that all the time. We look for more data points, increasing the level of granularity in our data and kind of fitting our system with richer information.

And I'm I'm mentioning it here because you know, people usually think about product, they think about the features that they can bring. But I'm saying because we know data is kind of the base layer of everything that we have, then if we improve it just by itself, we can create way more features and way more accurate um kind of experience for our users and customers as well. Very nice. And and uh let me ask you, you know, as a company whose culture is really based all around data, um, and as a manager who's hiring people who um work with data, do you see the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years and why?

Yeah, of course, I can I can only you know answer from you know the way that I look at the world and the problems and the marketing, the market that I'm in right now. So, you know, I I can you know share my opinion that you know we all see the massive changes happening in tech right now. And I kind of talked about it before is like in the last two years around generative AI and everything. And I think like there are two big directions uh to look at right now.

The first one is actually people using AI technologies or AI tools out there like ChatGPT, Cloud, whatever you're using, uh that alone is already dramatically affecting many markets today and replacing or improving a lot of the tools that we use today. Um, you know, and and like any other revolution, probably new startups will grow from this and many products will need to evolve uh to stay in the competition, obviously. But I think the second direction is actually even more interesting to me.

It is the access of building in new technologies. I think the bar today is much lower than I started, you know, 15 years ago. You know, I took a full degree to learn how to program. And even if I was keeping the kind of the degree and learning it myself, you know, it would take me a while probably to learn how to write code.

And obviously to get to a position that, you know, as is kind of a senior engineer, I would say that I'm not just writing code, I'm following best practices and so on and so forth. And the funny thing is that today, you know, my 10-year-old kid can build a full video game using you know a GPT-based code editor, right? And the access to create software has never been easier the way that I see it. And in my opinion, that's even a dramatic, it's a more dramatic change than just using the technologies out there.

And I think, and again, opinion, and if I need to guess, I think that is exactly why everything I said, you know, before is important around data and why data is kind of the core of everything, you know, that we do. I think data becomes a real differentiator these days even more than before. You can build features faster now, so you know, and you you can build prototypes faster now, but you cannot fake like high-quality data. I think companies will continue to invest more and more in data because it it is one thing that gives them kind of an edge and something that you know no one else can run with any GPT-based um, you know, tool.

Um and when I speak with people about you know what we do at Healthy, I think they immediately run into kind of, you know, um, you know, I can probably build something similar to that with ChatGPT. And as I said before, we never uh created Healthy to be something like ChatGPT. And because ChatGPT is great, but it's it's very limited and very risky to use in a world like us. Um and because of that, I think like everything that is related to bring all the data, like think about yourself using ChatGPT.

To get to something like Healthy, you will need to tell ChatGPT like all your employer programs, your point solutions, your health benefits, your medical, your dental, your vision, all your voluntary benefits, you will need to connect your claims into ChatGPT, your accumulators, like deductibles, and so on and so forth. And just to kind of explain the complexity, you can just simply not do that. Um, and the reason that I'm mentioning it here is because kind of going back to your question, is that I feel data is something that will never go away and actually will even increase.

Uh and the importance of data will increase with it. So I don't know if the number of jobs will increase, obviously, over time. No one knows, obviously. But I think like if I need to kind of put my bet on the importance of data, it's going to be increased dramatically.

Um, and and my opinion is that data will stay the center of many companies, as well as the quality of data, security, privacy, um, anything around building insights and dashboards and so on and so forth. So, yes, I do think that that you know, data management related positions are going to increase uh at the end of the day or at the end of you know the decade probably. I like it. So, what advice then would you give to people looking to get into a career in data management?

Yeah, so I always tell people, you know, even when they join healthy, regardless of data management position, but same goes here, I think, for data management positions is like be aware of of what the company is trying to achieve. I think like the biggest thing that anyone can do when they enter into a data position, for example, or any company, you know, um, you know, is to start by learning the domain behind the data. Do not look at the data only through a technical lens. I think sometimes people kind of, you know, and I'm speaking obviously as a CTO, but there are other data management related positions, obviously, but people think about what they bring to table in terms of their skill set, right?

But it's not about the skill set. I think it's about the mindset. I think it's about understanding the business, understanding the short-term goals and the long-term goals of the company that you're joining. Many data roles are not just about running queries or building dashboards, right?

They're about improving what already exists. Um, they're about finding insights that move the company forward. Um, and to do that well, you need, you know, strong foundations that can support the company for many years, not just a quick fix uh, you know, for for now and or for tomorrow. Um you know, and and kind of to say that I look you know at how data can can fuel the product, especially here at Healthy and probably in other markets and other solutions as well.

And I think when you have like a strong foundation and when you focus on real value, you become someone who can help the company grow, not just someone who manages the data. So kind of going back to my advice is that focus on what's important, focus on what's important for the company, what goals you are trying to achieve, what goals your company and your manager tries to achieve, and work through through that and not through the data or the schema or the technicalities around the data.

Very nice. Oh Ron, it has been a pleasure chatting with you today. Thank you so much for taking the time. All right, I appreciate you.

Thank you very much. And I'd be remiss if I didn't ask, you know, if somebody wanted to learn more about healthy, where would they go? So obviously, healthy.com, uh available and open for everyone.

You can contact us through there and you can see some live demos and uh yeah, learn about us. And it's spelled H-A-A-L-T-H-E-E, right? Healthy.com.

I love it. Yeah. That's great. Uh and we'll get those links posted to the uh the podcast page too.

Well, again, Ron, thank you so much for sharing your story today. Thank you very much for having me. And to all of our listener out the listeners out there, if you'd like to keep up to date in the latest in data management education, you may go to dativersity.net forward slash subscribe.

Until next time, stay curious, everyone. Thank you for listening to Dataversity Talks, a podcast brought to you by Dataversity. Subscribe to our newsletter for podcast updates and information about our free educational webinars at Dataversity.net forward slash subscribe.