Building Scalable Data Products: Lessons from Tech to Healthcare

[00:00:00] Gordon Wong: Welcome to Hard Problems, Smart Solutions, the Newfire Podcast, where we explore the most complex challenges and groundbreaking solutions with industry leaders. I’m Gordon Wong, VP of Data AI at Newfire Global Partners, and your host for this episode.

[00:00:21] Gordon Wong: Today, I’m thrilled to welcome Sandeep Dhamale, Director of Engineering, Data and Intelligence at the American Medical Association. Sandeep has built his career leading data and platform engineering teams.

[00:00:31] Gordon Wong: At the AMA, he has spearheaded transformative projects like Datalabs GPT, a private LLM infrastructure. and the AMA Intelligent Platform, which brings a modern, reusable technical infrastructure to AMA’s data initiatives. Beyond his roles at AMA, Sandeep has also served as an advisor with Newfire, bringing his expertise to help drive innovation and scalability for our clients.

[00:00:55] Gordon Wong: Today, we’ll explore how healthcare organizations can adopt scalable data solutions to improve patient and operational outcomes. Sandeep, welcome to the podcast.

[00:01:04] Sandeep Dhamale: Thank you, Gordon. Really excited to be here and I look forward to a fun conversation.

[00:01:09] Gordon Wong: Yeah, me too. This is, these are typically a lot of fun and I’ve been looking forward to speaking to you for a while. One of the things I wanted to ask you about really is how you got into this space, right? You were originally in fintech. Can you talk a bit about how your career journey took you from fintech to healthcare and how you can, how your fintech experience shaped your strategies for tackling healthcare’s unique data challenges?

[00:01:29] Sandeep Dhamale: Yeah, great. I mean, I consider myself very lucky to have had a career journey that I had. Basically, after I finished my master’s in computer science, right, I landed this job at a global fintech firm called SunGard. The job was great for my career. It allowed me to expand my experience way beyond software engineering skills, right?

[00:01:51] Sandeep Dhamale: It was a global development firm with an emphasis on building global market connectivity. So I was really talking to different various global exchanges. So that allowed me to work across multiple business lines and wear different hats, which involved building large scale platforms from scratch, modernizing legacy platform onto these platforms, right?

[00:02:13] Sandeep Dhamale: So thinking about opportunities for new product development, new ways of accessibility that comes from the modernization. So, the point being, while I was building my engineering skills and building teams and I was, building departments, I was also keeping track of customer problems, driving the customer listening sessions.

[00:02:32] Sandeep Dhamale: And I was also lucky because of that I got to travel internationally. But one thing that really taught me that as an early career engineer there was the importance of shifting left on the scale of product life cycle, right? And really develop that product sense. I think product sense is such a crucial skill in all high performing engineering teams that I’ve worked with that it definitely needs to be underscored.

[00:02:55] Sandeep Dhamale: So coming to the experience, the platforms we built there, especially towards the later part of my career in SunGard, were with derivatives processing, but the focus was on high throughput, low latency types of systems. You’re now thinking about in memory database, how to avoid context switching and from processes to processes to get the best performance you can.

[00:03:15] Sandeep Dhamale: While we were doing that, I got to build these platforms from scratch for data processing and data connectivity. And then eventually also build an API store for our customers to make it all available for integration. So that was a great experience, and now I’m thinking about those concepts similarly in healthcare, right?

[00:03:31] Sandeep Dhamale: So security, compliance, handling of sensitive data were all critical in those environments, and those principles translate directly to healthcare as well, right? When we think about financial privacy and integrity, it’s crucial we think about those things even more when we think about patient records, right?

[00:03:51] Sandeep Dhamale: I also think about Fintech, and even other industries that I’ve looked at in general, the place of pace of innovation feels faster because stakes are a little bit different. Even with regulation, there are no patient lives at stake there, right? So pace definitely feels and looks different. I do feel that API-driven ecosystem and modern infrastructure were far more advanced in Fintech before I got to the AMA.

[00:04:18] Sandeep Dhamale: And I kind of think that kind of led me to think about how can I think about scalability and flexibility into some of the problems I’ll be solving here into healthcare, right? Think about iterative value creation think about unifying data assets and accessibility. Those things translate really well into healthcare too, right?

[00:04:37] Sandeep Dhamale: So that’s how I think about those things.

[00:04:40] Gordon Wong: You underscored a familiar distinction for me. Like, frequently as engineers, we talk about things like velocity or total volume, real time analytics and so on, right? And so, as engineers, we might think that fintech and healthcare are different, but what I just heard you say is that when you bubble out to the problem, the larger problem, they actually have a lot in common.

[00:05:01] Gordon Wong: Is that right?

[00:05:02] Sandeep Dhamale: Yep, that is correct. And that’s one thing, a cool thing about being an engineer, you’re able to look at the commonalities across the problem spaces and bring on thinking that really makes you see the art of possible. Because you, if you’ve solved a problem a certain way, why can’t you solve it for this industry?

[00:05:18] Sandeep Dhamale: Those are the kind of things that engineers always love. And I, I have seen those commonalities first hand for sure, especially when it comes to, uh, solving for data unifying all of those data assets to leverage the value from that data.

[00:05:32] Gordon Wong: Multiple years in the fintech world, built some really cool solutions, now you’re in healthcare. Healthcare data comes with unique challenges, such as silos and compliance, legacy systems, heterogeneous systems. What are the foundational hurdles you’ve encountered, and how have you addressed them?

[00:05:49] Sandeep Dhamale: Right. So foundational hurdles, like some of the things that you’ve mentioned is healthcare is in a state what it is because of various different reasons. Of course compliance and speed being one of the reasons, but because of that the problem spaces of legacy infrastructure exist

00:06:09] Sandeep Dhamale: a lot more. So data fragmentation has creeped in a lot in my opinion because there are so many different systems, they’re siloed, the data doesn’t talk to each other. That has been one common theme that I’ve noticed across healthcare. It’s also a lot of build versus buy challenges that come into places and decision making that is happening in a siloed manner.

[00:06:31] Sandeep Dhamale: So there’s duplication of platforms data being replicated across multiple places. So there’s that redundancy that’s happening which actually some of the recent regulation like HIPAA and GDPR are only putting a more spotlight on like if you look at where is your right to forget or if you go to look for those records, it’s not just in one place, but it’s in like 26 different places across the enterprise that you learn.

[00:06:57] Sandeep Dhamale: And then, then Joel on some days emailing spreadsheets of the same data to somewhere and that only exacerbates the problem, right? So I think the hurdle was the fragmentation and trying to think about how can we organize this data in a centralized place so that we have a better control and better data, unified data management in place.

[00:07:18] Sandeep Dhamale: So that was one of the first ones that I think about. I really also think about governance frameworks and compliance for designs is becoming very, very crucial. But while I emphasize those, I also want to emphasize flexibility for growth and thinking about scalability in mind are as important as well.

[00:07:38] Sandeep Dhamale: So yeah, I, I think the hurdles are really data fragmentation and interoperability and how do we really get those all together.

[00:07:47] Gordon Wong: You know, at Newfire, one of the things I do with our clients is I advise them to think about the ROI of their data efforts, right? You have to consider what are you going to get out of this and what are the costs. Now, in fintech, it’s probably a little bit easier to measure that, right, because you’re looking at financial returns.

[00:08:04] Gordon Wong: Do you have any suggestions on how to measure the ROI of data efforts in healthcare?

[00:08:10] Sandeep Dhamale: Sure. You can look at it two different ways, right? Definitely start with your use case in mind and what you’re really what personas you’re trying to serve, because that’s going to determine your ROI case, because in some cases it’s a data product that you’re building, which is going to have a real financial incentive.

[00:08:27] Sandeep Dhamale: And that’s why you’re building it. In some cases, you’re really thinking about administrative use cases. And you’re really trying to think about how. How this problem will make everybody’s life easier and get the type of compliance and processes that we need. And ROI in, when I was building infrastructure, we’ve done it both ways.

[00:08:45] Sandeep Dhamale: Uh, one is thinking about what is our current total cost of ownership of these siloed data systems and how much are we really spending. And two, thinking about when we get to our target vision of a unified data infrastructure— what is the cost of running the business going to look like, and how do we transition from A to B.

[00:09:04] Sandeep Dhamale: And there are always so many wins to be had. You’re, you almost always certainly decrease your cost and reduce your risk. So that’s that’s a win with cloud based architectures. You’re always guaranteed more and more flexibility that kind of prepares you for the future evolution. And. And then you realize that while you’re building this infrastructure, if you attach a couple of data product or real revenue generating use cases to back it up, you really bake in the cost of maintaining this infrastructure that kind of gets offset by the, by those revenue generating products that you’ve included in the first cut of the draft that you’re thinking about the vision.

[00:09:44] Sandeep Dhamale: And, and it has now unlocked value because you can build the product 3, 4, 5, 6 fairly quickly with reusing all of the infrastructure that you’ve really built in for first couple of things. And then the ROI really scales from there. So definitely when you’re thinking about it, have those principles in mind is what I is what has benefited me and I think should benefit our listeners as well.

[00:10:07] Gordon Wong: We use a lot of metaphors in our industry, right? We talk about DAGs, directed graphs, we talk about pipelines, we talk about manufacturing moving data into knowledge, but more and more I’m thinking that the correct metaphor is that this is actually a marketplace.

[00:10:22] Gordon Wong: We have sources on one side and we have consumers on the other side and we’re trying to enable transactions and any good marketplace should take as little of a cut as possible, right?

[00:10:33] Sandeep Dhamale: Yes.

[00:10:33] Gordon Wong: Right? So does that, does that metaphor hold in healthcare? Do you think, do you find that valuable?

[00:10:38] Sandeep Dhamale: I do actually. The more and more I think about how to bring value to the data, it’s really to try to think about how you’re really going to cater this data and what packaging and what formats and how little or how big of that package needs to be. So you really have to think about the personas.

[00:10:56] Sandeep Dhamale: One of the other thing that’s where data marketplaces are becoming so useful in my mind. And it also shifts to the healthcare because I think the problem is, not very different. You’re thinking about interoperability. You’re thinking about making it more accessible and accessible is the is key when you think about marketplace like concepts is like you’re coming to a place.

[00:11:17] Sandeep Dhamale: I always think of it like a grocery store, right? You go to a grocery store aisle you, you can pick up a you, you know exactly what you’re doing in the grocery store. You know where to find, Your vegetables, where you want to find packaged foods or where you want to find milk. So there, it’s all well organized, where the dairy section is and all that.

[00:11:34] Sandeep Dhamale: You can go to that section, you pick up a product of the shelf, you can read all the labels, you know what you’re getting and how it was sourced, etc, etc, what the ingredients are, or the nutrition information, right? It’s readily available for you to make your decisions right there and there.

[00:11:47] Sandeep Dhamale: So you don’t feel very confused as to what you’re going to walk out with. You’re going to walk out with a whole milk or a 2 percent milk, those are the kind of things. So I also think about data marketplaces the same way. If you have your data products very well listed and what you’re getting is clear for the consumer

[00:12:04] Sandeep Dhamale: that’s a win. That’s how you should think about packaging your data products in a way that a consumer comes to a data marketplace that they’re getting what they’re needing. And that’s one metaphor. And then the second metaphor also that really resonated with me once was especially thinking about privacy.

[00:12:22] Sandeep Dhamale: You take that grocery store and add a pharmacy section to it, and now you need to have a prescription to get a particular product, right? You’re walking up to a pharmacy and you’re telling, I have a prescription. So that’s to me most, some of the data assets in that marketplace could be going that way because they’re protected.

[00:12:38] Sandeep Dhamale: They are important and with sensitive information. So personas need to be going that way. So I agree with you. I think data products and the marketplace’s vision translates well, even into the healthcare ecosystem because we’re trying to really bring the same type of consumer experience to healthcare that we’ve seen elsewhere.

[00:12:58] Gordon Wong: This is turning into an economics podcast. Some of the concepts you brought up earlier were definitely, you talked about, basically fixed and variable costs, right? And if costs are lower, you can cover, if you have a baseline products that cover most of the costs of the platform, you can afford to bring additional products.

[00:13:13] Gordon Wong: So that seems pretty key to scalability. All right. So I want to hear a little bit more about some of the things you specifically built, right? Particularly, I to hear about the AMA Intelligent Platform. Can you tell me about that?

[00:13:26] Sandeep Dhamale: it’s going to be easy to explain AMA Intelligent Platform because of some of the constructs we talked about. When I started at AMA in 2019 and when we were looking at our future of data products, one of the ways we were thinking about how can we bake in flexibility into an architecture that can allow us to scale for future.

[00:13:46] Sandeep Dhamale: So some of the exact same principles that I described previously were were in consideration. We wanted to build a technical reusable infrastructure on, cloud basically so that we can scale the way we want to and add, keep adding capabilities. So think of it as, as more like a modern data platform initiative where you’re thinking about what is your unified data strategy is going to be, where you’re going to land the data, what kind of tools you’re going to bring in to process the data and create your gold slash product repository, if you will what that infrastructure looks like.

[00:14:18] Sandeep Dhamale: And then build capabilities on top of it of creating different API endpoints, creating different type of integration technologies for external facing whether they’re external facing product consumers or internal users even, but thinking about data accessibility from the get go.

[00:14:36] Sandeep Dhamale: So it was a culmination of all of this. So it’s baked with technical reusable infrastructure in the back end with a digital front door on the front end that allows our customers or even internal audiences to interact with a developer portal built in. So you can you can look at our API stack directly.

[00:14:52] Sandeep Dhamale: So sort of, gives you a marketplace, like a construct, but makes makes it easy for our customers to really interact with our data and make it more accessible. That’s, that, that was the vision that we’ve been going in with, as we do more with this data, it also creates a framework so for customers to predict what, how it’s going to look like and feel like.

[00:15:13] Sandeep Dhamale: It’s bringing some of the standardization to access patterns. So that way, when we launch a new data product, it’s just going to be a walk in the park because they’ve done it already once and it’s going to look very similar. And the, all of the constructs that they did with the first integration are going to just stand in there.

[00:15:30] Sandeep Dhamale: Right. So that’s how we think about AMA Intelligent Platform. So for what it’s worth, for a lot of our newer data initiatives, they’re all being facilitated by this platform infrastructure now. And that has given us flexibility for thinking about our data assets in the future.

[00:15:46] Gordon Wong: Incredible. So when you first, when the platform was first conceived What were some of the real world problems that you were hoping to solve? What, what kind of pain were people facing?

[00:15:56] Sandeep Dhamale: I will categorize into two sections, if you will. Uh, One is pains of how can we get our infrastructure ready for innovation? Because we’ve had a lot of legacy systems in place. And then the second is, of course, the real customer voice, right? Like we’ve been hearing you, you ship in certain formats.

[00:16:14] Sandeep Dhamale: In a lot of cases, there were flat files, you, we don’t know how to track change history of these data assets or in some cases, like it’s not intuitive, the documentation around it. And it takes me a long time to understand your data once it’s delivered. So those were some of the key challenges.

[00:16:31] Sandeep Dhamale: When we were thinking about architecture, of course, we wanted to make sure we take a simpler approach of not trying to replace everything at once, but think about how can we bring a data platform that can be fed with some of the existing data assets that we have to create an infrastructure to think about how to solve problems.

[00:16:53] Sandeep Dhamale: And then from a customer listening perspective, we were applying lenses as to, OK, you need more metadata along with our existing data assets. So how can we generate that metadata for you? OK, you need modern delivery mechanisms. In some cases, that just meant making sure we have API delivery formats. Some were really low hanging fruit.

[00:17:13] Sandeep Dhamale: Some were really thinking about where this is going to go from , from our perspective, as well as our customers perspective. One of the good things is people really rely on AMS data. It’s been powering healthcare ecosystem for decades now, and we just want to make sure we are good stewards of these data assets and continue to give it to our healthcare partners and customers in the right

[00:17:40] Sandeep Dhamale: time, right, format and, and make sure that we continue to solve those challenges that we’ve solved in previous era as well.

[00:17:49] Gordon Wong: Now, I’m sure that every project planned along the way was no problem, and you hit every deadline. But were there any challenges?

[00:17:56] Sandeep Dhamale: Of course, there’s no software project that ever throws a curveball at you, right? I think there were, right? But I think key to success in any of these initiatives is making sure you have an end in mind. I always say be stubborn on your vision, but be flexible on details and take, think about how your iterations could look different based on your priorities

[00:18:19] Sandeep Dhamale: and what personas so your scope can be changed as you’re learning about the marketplace. So think about what your milestones and their definitions can be can be and still add a value. So definitely go incremental smaller iterations and think about how can you ship a value every iteration. And then for your bigger milestones, be flexible as you’re learning from the marketplace.

[00:18:42] Sandeep Dhamale: I can tell you, for example, there were certain use cases that were not, that we thought were high value early on. And then to, to some of the market research and even internal hurdles that we thought, okay, it’s going to take us longer to launch this. But we were able to pivot that into a different type of package and make sure make sure we still deliver value to our customers.

[00:19:05] Sandeep Dhamale: But also our infrastructure initiative doesn’t get stalled because you always need to make sure your customers are happy. And that kind of gives us the impetus and funding, in fact, also to make sure that you can self sustain and self fund the platform initiative that you’ve started here.

[00:19:23] Gordon Wong: Would you be willing to share some of your wins with us? Feel free to brag.

[00:19:28] Sandeep Dhamale: I really think the biggest one is is organizational one is taking everybody along on this, and I think some of the builders will relate to it is it’s one thing to make, know what is right. And then you having a vision and being able to also evangelize that across it takes a village, right?

[00:19:48] Sandeep Dhamale: So you have to really be able to talk to all the departments and all the stakeholders. And making sure that this this resonates with everyone, and this is the most important problem that you’re trying to, so the framing of this initiative it took us a little bit I’m going to say a lot of attritions to get it right, but I think I feel very good about it right now that we are on a right path.

[00:20:12] Sandeep Dhamale: Uh, so that’s one. From an infrastructure and technical perspective, I think getting our developer program stood up was one of the highlights of this initiative. So I’ll give you a back story, right? Like, we were building this infrastructure for all the reasons that I mentioned to you. And then we had an initiative where we wanted to make our CPT asset available to early stage builders with an open license kind of a thing, where they can get access to our CPT content when they’re building their use cases and they’ve not figured out their go to market, for example, and they want to have access.

[00:20:51] Sandeep Dhamale: It was a perfect marriage because we had an infrastructure in place. All we had to do was to think about what a CPT developer program could look like, invite these builders onto the AMA intelligent platform, and reuse the same infrastructure that we have, right? We have a developer portal, we have an API store, and that we had built for our and curated for our customers, but we were able to repurpose

[00:21:13] Sandeep Dhamale: and launch a CPT developer program in matter of weeks. Like I, I think from conception to reality, it was like four to six weeks of project and boom, it was launched and it’s one of the programs that has given us a community that actually participates with us. We were able to take those connections forward,

[00:21:32] Sandeep Dhamale: we interacted with our conferences, we have quarterly Dev Chats. And they’ve been actually, uh, some of the people who are working on cool use cases with our content and data products. So it’s always useful to hear perspective because industry is like you have matured. Participants that are using your data assets and there are some new use cases you’re seeing, like how people are thinking about what they can do with your data.

[00:21:54] Sandeep Dhamale: And having that voice baked in has really helped us. So I think that was one of the coolest achievement of AMA Intelligent Platform was to be able to launch a CPD developer program, for example. Yeah. I think what’s next is, is definitely AI and generative AI on our minds. I also think it’s, it was a good test when AI wave came along is some of the investments that we’d started making in this infrastructure stands true or not.

[00:22:23] Sandeep Dhamale: And I think it has, right? If you think about, doing your AI or, more importantly, generative AI now, right? You need to have the infrastructure that backs it and you need to be ready with your data in a unified store and the right formats and the right point of delivery. And I think this initiative has started to push us in that direction.

[00:22:47] Sandeep Dhamale: Some of the interesting things that we’re thinking about there has been more about unstructured content that have been sitting in PDFs and different because a lot of our data assets were also, one of our business, I’m going to say, was really a book business or a human oriented business, which is now turning into a data business.

[00:23:09] Sandeep Dhamale: So that’s a shift. So we’re taking some of those assets and trying to trying to really create RAG architectures or graph databases and vector stores. Those technologies, we were able to onboard onto our data platform in a fairly quick amount of time because we had started landing all of this into our data platform already.

[00:23:28] Sandeep Dhamale: This was just, this is the good part about modern data platform that I think is you can bring another piece of technology into your stack fairly easily and it’s able to, like you’re thinking about your architecture all the time is volume, velocity and variety, right? So this variety of, the new variety of data and how do you really bring it to bring it to take advantage of vector storage, for example, what was,

[00:23:52] Sandeep Dhamale: is something that we’re working on, right? Like you asked me what’s next. So this is definitely on our minds. And there are some couple of use cases that we can maybe talk about that we’re thinking in that space as well. Yeah.

[00:24:04] Gordon Wong: Yeah, well, I’d love to hear those.

[00:24:06] Sandeep Dhamale: Sure. I mean, yeah with AI, when a couple of years ago, I think it’s coming up on the second anniversary now when Chat GPT launched we, I think early on, we pretty much jumped in with thinking about what use cases and what are the framing that we want to put in place.

[00:24:24] Sandeep Dhamale: I think we were bucketing our use cases into a everyday AI and a transformative AI type of category. With everyday I mean, can AI help us automate some of our tasks, make lives easy for our people give them hours back in the day and solve some of the search problems even that we had with these assets, right?

[00:24:43] Sandeep Dhamale: So that’s where we focused first is is trying to train a model and create a RAG infrastructure on our book business oriented data asset to see whether we can unlock some real value and create an assistant for our internal staff to ask questions and see if they can find right documents and references.

[00:25:03] Sandeep Dhamale: So it’s a combination of a search problem and a a really easy chat based interface to think if this intelligence layer can give you the answers right off the bat, because even this internal team that I’m talking about gets questions from different teams and even external people on guidance.

[00:25:21] Sandeep Dhamale: So it is, the goal is to accelerate their workflow. So that’s a pilot that we’re running right now with the hope that this is the same infrastructure that we can actually package intelligence into future and think about what kind of intelligence offerings we can make into future products, right?

[00:25:38] Sandeep Dhamale: So that’s how that’s an initiative that we’ve been thinking about really.

[00:25:41] Gordon Wong: Yeah, so keep leading into the marketplace, right? That’s what it sounds like.

[00:25:46] Sandeep Dhamale: That is correct.

[00:25:48] Gordon Wong: One of the conversations I have with our clients at Newfire with I say, you know, encourage ’em to think about ROI.

[00:25:53] Gordon Wong: They get very tired of hearing me say ROI over and over again. But that’s, that’s really kind of everything. We have both the, the numerator, the denominator, the value we generate and the costs. And I think personally I see a tremendous amount of value in using AI to lower those costs. Lifting constraints, removing blockers.

[00:26:11] Gordon Wong: What are some. What are some areas of high friction you see in delivering data products that you think AI can help with?

[00:26:18] Sandeep Dhamale: I think I’ve got a good example for the question you’re asking I mentioned we, we were a book business and we’re trying to get into a data business with that particular data asset. First challenge is making sure you can segment the data the right way. Right? You have to break the content down into a different format altogether.

[00:26:37] Sandeep Dhamale: And then, you have to look for common keywords, common tags, like create metadata on the fly. So,

[00:26:44] Sandeep Dhamale: giving this to humans, to curate a new data asset, reading through books, and doing it like that, it’s gonna be a

[00:26:54] Sandeep Dhamale: It’s going to take you forever to do it. I think what we’re thinking about it is, and we’ve done already some pilots with this is, can you use large language model infrastructure to prompt it properly to create segments out of a big book pdf and see what kind of output it generates.

[00:27:13] Sandeep Dhamale: Then take those sections and create like a preview summary that you can attach as a metadata tag into to that, create keywords for that sections and generate like 10 keywords each for that section and create that. And again, the goal is to accelerate the stewardship of this new data asset that we’re creating.

[00:27:33] Sandeep Dhamale: So these are tools that we’re providing to our data teams or our content teams specifically. That who will be in charge of reviewing this content and essentially stamping it. So these are tools that are going to assist these data owners. To really look at what tags got generated, how it’s really creating sections of it.

[00:27:54] Sandeep Dhamale: So it’s not like you’re taking and fully automating it, but you’re really making their lives easy and they are now reviewers of this process rather than taking a book content and try to break it down themselves one by one and reading through that’s such a daunting task.

[00:28:08] Sandeep Dhamale: I think AI is going to make that much more simpler and thinking about curation and metadata creation process, that’s one area where you’re gonna see your costs go down. And especially these initiatives now are generating value, right? Like this was a value that was sitting somewhere on a PDF document that was not getting

[00:28:29] Sandeep Dhamale: uh, solved for now you have really created a data offering out of it. So you have an opportunity to go to market with a new offering. That’s what we are seeing with this kind of initiative. So whatever total addressable market space you’re looking at with that kind of content is definitely needs to be factored into your ROI calculation.

[00:28:48] Sandeep Dhamale: That’s one. And then the second thing I’m going to say is, because these data products are giving are going to the new markets, you’re going to get more feedback. For example for, in our case, a real example, this was going into only into the hands of a certain section of people who actually read this content.

[00:29:09] Sandeep Dhamale: Now we’re able to take these micro Intelligence, if you will, and we’re thinking about whether we can inject into newer workflows. So that’s like a newer market you’ve you’re thinking about now, not just the previous market that you were working with, right? So I think you really have to be able to re-imagine your domain and where it’s heading in the age of AI.

[00:29:30] Sandeep Dhamale: And then then think about what possibilities exist and which bets you want to take. But yeah, there are going to be bets that are going to be available to our, to like the listeners of this podcast, because I think once you have data, you, the next layer you’re thinking about is intelligence, right?

[00:29:46] Sandeep Dhamale: So I think the AI plays the role in simplifying your data processing as well as thinking about new use cases where your intelligence can be delivered. Yeah.

[00:29:57] Gordon Wong: As responsible stewards of the platforms we build, I want to take this opportunity to remind us of two expressions, right? One is knowledge is power. And then the other one is with great power comes great responsibility.

[00:30:09] Sandeep Dhamale: Absolutely.

[00:30:10] Gordon Wong: So how do you see AI helping us address the privacy concerns in healthcare and protecting our patients?

[00:30:19] Sandeep Dhamale: Great question again, right? I I think two ways. One, and especially, I think we mentioned this in somewhere in the podcast, right? When you’re thinking about building your data products, think with your end users in mind and the personas you’re building with. And they’re going to be, once you do your market research, you can actually work with large language models to frame those personas right.

[00:30:42] Sandeep Dhamale: And what kind of controls you need in place, like defining them itself can be done with the help of large language models. But I think, The daunting task of automating the processes that you need to have in place for each of these personas, like what are those processes going to be and then be able to create standard operating procedures according around it.

[00:31:04] Sandeep Dhamale: It’s always a time problem, right? You don’t have enough people to think about think about these controls. You can automate a lot of these controls with with help of AI. I think it’s a problem. And a balanced space because AI is going to make accessibility more prevalent.

[00:31:21] Sandeep Dhamale: And then how can you also leverage AI with security first mindset to think about automating the processes and the governance framework around it is important. I think some of the things that you talked about data marketplaces and how you can get your access streamlined because of a data marketplace or that kind of an access pattern where people are coming to the same place to get their data I think it still remains.

[00:31:47] Sandeep Dhamale: And these are the kind of platform thinking and processes will continue to make sure you have a governed platform and not just an accessibility that has now created a newer type of problem in security space.

[00:32:02] Gordon Wong: I like to find the security people in whatever organization I’m in and ask them to teach me how to be their best customer. Because otherwise, I’m just a walk, I’m just a walking problem, right?

[00:32:17] Sandeep Dhamale: Yeah. Well, one thing, one thing that I think especially in recent times, I’ve had a lot more appreciation is probably, and we did this in our recent all hands within our engineering team is to start thinking like everybody’s a security engineer. That’s part of your job. We’ve just tried to ingrain that into our build teams mindset

[00:32:39] Sandeep Dhamale: is our centralized security team is only so much and they are going to evangelize the principles, but it’s everybody’s responsibility and really thinking about that at the design time is very important. That also brings me to the architecture point, right? Like evolution, evolvability versus maintainability.

[00:32:59] Sandeep Dhamale: And that’s a, that’s always a balance. You want to have an architecture that scales and evolves. But it is also maintainable because if it’s not maintainable, your security is going to be a nightmare. And that’s why we’re trying to bring a balance is it’s your responsibility. It’s everybody’s responsibility.

[00:33:16] Sandeep Dhamale: And we’re trying to think about whether I, I don’t we’ve not done it, but we’re now going to start putting like a line into job descriptions also to start emphasizing this point more around security as we think about further and further. Yeah.

[00:33:32] Gordon Wong: I’m going to take the opportunity to jump on the soapbox. I’m going to try to frame my statement as a question. But I personally have seen that it feels like most organizations underinvest in the maintainability of their platforms, right? They focus on development costs, but you don’t really deliver value until the thing, until whatever you’re building is in production.

[00:33:51] Gordon Wong: Now, have you seen the same thing?

[00:33:53] Sandeep Dhamale: Yes, all the time we see this around and it’s also, I, like I was saying, it’s a balance and sometimes engineers have to be that voice of reason because I know everybody, including our stakeholders are excited to see the value to market and time to market as the most important thing and which is, which it is in my mind and you go through phases in.

[00:34:16] Sandeep Dhamale: In my mind, you go through phases. If you’re trying to build an MVP that you want to head to the market quickly and see what it does, you can think about you can think about what your maintainability looks like. But if you’re making that decision, you really have to make sure that you’re baking it as a part of consideration as to what what it takes to really get to the production grade product, if you will and have that on your roadmap.

[00:34:43] Sandeep Dhamale: You don’t just discount it. You really make sure that it’s out there. In my mind, You don’t really put maintainability completely out of the window from the beginning, you actually bake it in, you do trade offs and try to work with those trade offs based on your time to market, but have a roadmap, make sure it’s out there and everybody has seen it, that your MVP can hit with X parameters, but to get to the production grade, you have to have X plus Y in place without that you don’t really go live is important.

[00:35:15] Sandeep Dhamale: And it’s also allowing them to see why it’s important, right. For ex I, I think that availability and maintainability, I like to take an example all the time as a bicycle example, right? If I was asked to design a cycle with a bike, with a requirement which is the most flexible,

[00:35:33] Sandeep Dhamale: you can build like a monocycle, right? That one with a one tire. That one is pretty easy to maneuver. You can just do 360 on it. But it’s not easy to ride because balancing on it is hard. And then there’s tricycle, which you can build. Which has three wheels nobody’s going to fall off of it.

[00:35:51] Sandeep Dhamale: It’s very easy to balance, but it doesn’t go as fast. It’s very hard to maneuver. And bicycle is like a balance with two wheels. You can do balancing pretty well, but you can also maneuver it. But if you look at it, it’s a spectrum. Now you’re making decision based on what market you’re operating and what situation you’re in.

[00:36:08] Sandeep Dhamale: If you’re trying to really go fast and you have a skilled guy who can actually do with one wheel, that’s, that can only sustain for a little bit. I think the bike is the most prevalent architecture, right? You look for flexibility and it’s a balance because you don’t want, also want a tricycle that doesn’t go as fast or can only cater to really specific niche of, in this case, small kids who are really trying to learn to bike to get to the next level.

[00:36:37] Sandeep Dhamale: And so, yeah, it’s always a balance, I think.

[00:36:40] Gordon Wong: I love that. I love that. I love that. And that’s part of, I think that’s all part of the purposeful architecture of these products and these platforms is that we have to understand the constraints and we need to make investments, measured investments in the right places, right? You can’t, you don’t get anything for free.

[00:36:55] Sandeep Dhamale: That’s true.

[00:36:57] Gordon Wong: What are you excited about next? What’s coming? What kind of emerging technologies are coming that you can’t wait to get your hands on?

[00:37:03] Sandeep Dhamale: I think I kind of covered it with AI. So I’ll tell you one thing that I think it’s going to happen is especially in the space that we’re operating, all of the data companies, and especially companies who have data, like data that either they generate or the data that they have have been stewards of for a long, long time, have a real opportunity to think about what additional intelligence layer that they can build.

[00:37:27] Sandeep Dhamale: So that’s exciting in my mind. And we operate in that space. So very, very excited about that. The second thing is a lot of these models that are out there have been trained on internet data but not on enterprise data yet. So I think that’s going to give rise to a lot of domain specific models and domain specific use cases and healthcare is,

[00:37:48] Sandeep Dhamale: being in healthcare, I think that gives me a lot of hope to solve for problems that have been out of reach for a while now. Or just because of time resource constraints. And when do you really get to it when you have so many other things to solve, right? So I think that’s exciting for me.

[00:38:03] Sandeep Dhamale: And I think the third trend I’m noticing is the private infrastructures. Because a lot of people who have these data assets are kind of thinking about how can they not have to throw this data out to these large language models where they’re not sure of the security and whether their data is not being trained for other purposes, etc.

[00:38:24] Sandeep Dhamale: I think that’s where private large language model infrastructures are gonna play a key role in my mind and there are vendors out there who are now supporting with with these initiatives but yeah, and that was also one of the things that we did, just so you’re aware, is when when we build our infrastructure of the large language model that I talked to you about a couple of use cases.

[00:38:44] Sandeep Dhamale: With security first mindset we actually because it was early days we got the open source model LLAMA 3 back then and LLAMA 2 and LLAMA 3 now. And we have also looked at other models to kind of bring on prem we’ve spun up a GPU infrastructure ourselves and have been training and creating that infrastructure there.

[00:39:03] Sandeep Dhamale: The with the whole goal, and I think with newer frameworks and newer technologies, It’s getting more easier and easier to control your infrastructure off these large language models. It’s still niche, but at least it’s possible that you are not now training your data and model elsewhere, right?

[00:39:22] Sandeep Dhamale: You’re, you can control the, where that goes. It can stay within your network or within your preferred partner network, if you will. And I think that’s exciting to me. That kind of opens up a lot of use cases because your security teams, if they’re not as worried, at least then you can think about leveraging large language infrastructure, large language model infrastructure better for your use cases because we have so many of them.

[00:39:44] Sandeep Dhamale: And I think that, that really was a, you asked me about wins and brags. I think that was one of the wins and brags that I definitely am proud of that we were able to tap into a private LLM so early that kind of created possibilities to think about some of the data use cases or the content to data use cases that we talked about and that’s what we’ll be focusing on next year as well.

[00:40:09] Gordon Wong: I’m excited about the same things, I find myself totally aligned with you. Sandeep I’ve asked you a lot of questions in this 45 minutes or so. So I’m going to start a Newfire podcast tradition right now. I think the guests should get a chance to ask the host a question. Do you have any questions for me?

[00:40:25] Sandeep Dhamale: Sure, Gordon. First of all, it was fun chatting with you. I think we talked a lot about data and AI and you bring a very different perspective because you’ve been yourself at the helm of so many different initiatives across your career. I think in your current role, you’re trying to help a lot of customers.

[00:40:42] Sandeep Dhamale: So some of the things that I talked to you about are, are those the same things that you’re seeing across the industry? I was just curious, like your perspective, because you have a vantage point like no other, and I’m trying to get your perspective as well on, on the trends that you’re seeing across both data and AI space.

[00:41:02] Gordon Wong: Yeah, that’s a really good question. So one of the things I think I realized when I think about it is that what’s old is what’s new again, right? So often what I’m finding is that we’re building very similar, not that I think it’s exactly the same solution, but we’re solving the same problems now, at this point in my career, as I was 30 years ago. Because at the end of the day, what we’re trying to do is take data, some kind of measurement, some kind of, and turn it into information and knowledge that allows better decisions, better actions, and better outcomes. Right? And what we’re seeing though is that as the world’s gotten more sophisticated and we have more technology and we have more capabilities, we’re just tackling more sophisticated problems. 30 years ago, it might’ve been trying to measure who my best customer is. But now we’re trying to drive better human health outcomes at scale, right? We’re solving problems that we’ve never solved before. And so that’s what I think I’m taking away from this and what I’m reminding my clients at Newfire, and we talked to them is that, that yes, the technology is new, right?

[00:42:01] Gordon Wong: But the problems are old in some ways. It’s just a different scale. So since we know how to solve these problems, let’s bring that thinking, that learning we’ve had in the past there. You know, ROI, we talked about ROI a second ago, right? Numerator, denominator. Okay. So what are the costs that we are incurring in trying to drive better outcomes?

[00:42:19] Gordon Wong: Okay, how do we burn those down, right? What’s the value we’re driving? Hey, what’s holding back the value? Where’s the friction that is keeping us from delivering? If I tell my analysts, if you deliver a presentation to your audience you give them advice on what they should do in this situation.

[00:42:35] Gordon Wong: But they don’t take that advice. You haven’t really generated any value yet. So part of your job as an analyst is to be persuasive, is to communicate. And part of persuasion and communicating is also proving that our advice or our data products are safe to use, right? You used the metaphor earlier about milk and groceries, right?

[00:42:56] Gordon Wong: If I don’t know this milk is safe to drink, I’m not going to drink it, right? And if our customers don’t know that or our customers’ stakeholders don’t know that the data is safe to use, they’re not going to use it, right? So I encourage our clients to make sure they are investing in that safety, that maintainability, and make sure it’s visible.

[00:43:15] Gordon Wong: And so when someone asks, is your product safe to use? You say, absolutely, yes. And here’s how I can prove it to you.

[00:43:20] Sandeep Dhamale: Yeah.

[00:43:21] Gordon Wong: We’re seeing these same things across all our company, all the companies and yes, a lot of use cases in their details are new, a lot of chatbot stuff, a lot of using, models to predict outcomes, right?

[00:43:34] Gordon Wong: And then we’re making those outcomes more deriving those predictions more quickly, it’s greater accuracy further into the future, but also tying all these assets together to get a more holistic view , right, as opposed to just these little point views, if I use a simplistic metaphor, if I’m going to the beach, I want to know how to get there, what’s the best clam shack, what the weather’s going to be, what’s the tide, I want it all in one place. And now it’s getting easier.

[00:43:57] Sandeep Dhamale: Yeah, no. Love it. Love it. And that totally resonates with me, right? Like, these are the same problems that have existed, but the reach of our What the technology has enabled is that it’s now actually possible. I always think in early 2000s, it was really hard to build a data platform that was, that can unify all of these data assets with the advent of cloud infrastructure that kind of makes your life much more easier to think about what it is going to look like, right?

[00:44:26] Sandeep Dhamale: Like you have technologies at your disposal, which can hold variety of data, which was harder back in 2000s and with NoSQL and other technologies that have matured over the last few years, it’s become possible. Same story with Vector and AI. I think it’s another tool in your arsenal that’s

[00:44:43] Sandeep Dhamale: just gonna increase your reach. Really excited with those possibilities. And I totally love the images that you use there, yeah, of the beach. Yeah. And let’s with, with this, the beach, beach hits home because I’m in Chicago right now and it’s pretty, pretty gloomy. So I’m already thinking about which beach I can be next. Yeah.

[00:45:18] Gordon Wong: Me too, me too. Sandeep, it was fantastic talking to you. I really enjoyed myself. I feel like I actually learned something. I’m looking forward for us to be working together again. And I hope you enjoyed yourself as well.

[00:45:18] Sandeep Dhamale: It was so much fun. I have always enjoyed interacting with Newfire team. This is an amazing team. Everyone I’ve interacted with this at this team has been super smart and I loved this conversation also about our data infrastructure and how the AI initiatives and how to think about data and AI infrastructure in this fast-moving world.

[00:45:40] Sandeep Dhamale: We should continue talking more. It’s always fun to chat with you. Thank you for having me here.

[00:45:45] Gordon Wong: Of course, it’s a pleasure. Thank you so much. Have a good day.

[00:45:48] Gordon Wong: Thanks for tuning in. Stay tuned for more episodes where we continue to explore the toughest challenges and smartest solutions in business and technology. Like and subscribe. Until next time, keep innovating and solving the hard problems. This is Hard Problems, Smart Solutions, the Newfire Podcast.