1 00:00:07,119 --> 00:00:10,030 Gordon Wong: Welcome to Hard Problems, Smart Solutions, the Newfire Podcast, 2 00:00:10,079 --> 00:00:12,640 where we explore the most complex challenges and groundbreaking 3 00:00:12,640 --> 00:00:14,099 solutions with industry leaders. 4 00:00:14,370 --> 00:00:17,750 I'm Gordon Wong, VP of Data AI at Newfire Global Partners, 5 00:00:18,140 --> 00:00:19,159 and your host for this episode. 6 00:00:21,823 --> 00:00:24,423 Today, I'm thrilled to welcome Sandeep Dhamale, Director of 7 00:00:24,443 --> 00:00:27,554 Engineering, Data and Intelligence at the American Medical Association. 8 00:00:28,143 --> 00:00:31,233 Sandeep has built his career leading data and platform engineering teams. 9 00:00:31,653 --> 00:00:36,044 At the AMA, he has spearheaded transformative projects like Datalabs 10 00:00:36,074 --> 00:00:38,593 GPT, a private LLM infrastructure. 11 00:00:39,178 --> 00:00:42,848 and the AMA Intelligent Platform, which brings a modern, reusable technical 12 00:00:42,848 --> 00:00:45,898 infrastructure to AMA's data initiatives. 13 00:00:46,158 --> 00:00:50,759 Beyond his roles at AMA, Sandeep has also served as an advisor with 14 00:00:50,808 --> 00:00:53,578 Newfire, bringing his expertise to help drive innovation and 15 00:00:53,578 --> 00:00:54,968 scalability for our clients. 16 00:00:55,368 --> 00:00:58,498 Today, we'll explore how healthcare organizations can adopt scalable 17 00:00:58,498 --> 00:01:01,718 data solutions to improve patient and operational outcomes. 18 00:01:01,938 --> 00:01:03,548 Sandeep, welcome to the podcast. 19 00:01:04,809 --> 00:01:05,488 Sandeep Dhamale: Thank you, Gordon. 20 00:01:05,548 --> 00:01:08,969 Really excited to be here and I look forward to a fun conversation. 21 00:01:09,538 --> 00:01:10,058 Gordon Wong: Yeah, me too. 22 00:01:10,058 --> 00:01:12,698 This is, these are typically a lot of fun and I've been looking forward 23 00:01:12,698 --> 00:01:13,768 to speaking to you for a while. 24 00:01:14,488 --> 00:01:16,719 One of the things I wanted to ask you about really is how 25 00:01:16,719 --> 00:01:18,339 you got into this space, right? 26 00:01:18,339 --> 00:01:19,578 You were originally in fintech. 27 00:01:19,579 --> 00:01:22,868 Can you talk a bit about how your career journey took you from 28 00:01:22,868 --> 00:01:26,489 fintech to healthcare and how you can, how your fintech experience 29 00:01:26,519 --> 00:01:29,609 shaped your strategies for tackling healthcare's unique data challenges? 30 00:01:30,547 --> 00:01:31,517 Sandeep Dhamale: Yeah, great. 31 00:01:32,257 --> 00:01:37,177 I mean, I consider myself very lucky to have had a career journey that I had. 32 00:01:37,637 --> 00:01:41,847 Basically, after I finished my master's in computer science, right, I landed this job 33 00:01:41,877 --> 00:01:44,177 at a global fintech firm called SunGard. 34 00:01:44,687 --> 00:01:46,617 The job was great for my career. 35 00:01:46,617 --> 00:01:51,497 It allowed me to expand my experience way beyond software engineering skills, right? 36 00:01:51,497 --> 00:01:55,357 It was a global development firm with an emphasis on building 37 00:01:55,817 --> 00:01:57,267 global market connectivity. 38 00:01:57,277 --> 00:02:00,547 So I was really talking to different various global exchanges. 39 00:02:00,547 --> 00:02:06,407 So that allowed me to work across multiple business lines and wear different hats, 40 00:02:06,537 --> 00:02:11,157 which involved building large scale platforms from scratch, modernizing legacy 41 00:02:11,157 --> 00:02:13,037 platform onto these platforms, right? 42 00:02:13,037 --> 00:02:16,857 So thinking about opportunities for new product development, 43 00:02:16,857 --> 00:02:19,807 new ways of accessibility that comes from the modernization. 44 00:02:20,327 --> 00:02:24,815 So, the point being, while I was building my engineering skills and building teams 45 00:02:24,815 --> 00:02:30,688 and I was, building departments, I was also keeping track of customer problems, 46 00:02:30,708 --> 00:02:32,398 driving the customer listening sessions. 47 00:02:32,448 --> 00:02:35,478 And I was also lucky because of that I got to travel internationally. 48 00:02:36,048 --> 00:02:39,728 But one thing that really taught me that as an early career engineer there 49 00:02:40,118 --> 00:02:43,918 was the importance of shifting left on the scale of product life cycle, right? 50 00:02:44,448 --> 00:02:46,488 And really develop that product sense. 51 00:02:46,968 --> 00:02:51,298 I think product sense is such a crucial skill in all high performing engineering 52 00:02:51,338 --> 00:02:55,308 teams that I've worked with that it definitely needs to be underscored. 53 00:02:55,692 --> 00:02:58,932 So coming to the experience, the platforms we built there, especially 54 00:02:58,932 --> 00:03:02,627 towards the later part of my career in SunGard, were with derivatives 55 00:03:02,627 --> 00:03:08,007 processing, but the focus was on high throughput, low latency types of systems. 56 00:03:08,357 --> 00:03:11,667 You're now thinking about in memory database, how to avoid context switching 57 00:03:11,667 --> 00:03:15,347 and from processes to processes to get the best performance you can. 58 00:03:15,847 --> 00:03:19,177 While we were doing that, I got to build these platforms from scratch for 59 00:03:19,177 --> 00:03:21,017 data processing and data connectivity. 60 00:03:21,597 --> 00:03:24,577 And then eventually also build an API store for our customers to make 61 00:03:24,577 --> 00:03:26,667 it all available for integration. 62 00:03:27,367 --> 00:03:30,737 So that was a great experience, and now I'm thinking about those concepts 63 00:03:30,737 --> 00:03:31,917 similarly in healthcare, right? 64 00:03:31,967 --> 00:03:35,937 So security, compliance, handling of sensitive data were all critical 65 00:03:36,057 --> 00:03:40,037 in those environments, and those principles translate directly 66 00:03:40,037 --> 00:03:41,117 to healthcare as well, right? 67 00:03:41,507 --> 00:03:45,867 When we think about financial privacy and integrity, it's crucial we think 68 00:03:45,867 --> 00:03:51,007 about those things even more when we think about patient records, right? 69 00:03:51,507 --> 00:03:55,707 I also think about Fintech, and even other industries that I've looked 70 00:03:55,707 --> 00:04:00,437 at in general, the place of pace of innovation feels faster because 71 00:04:00,447 --> 00:04:02,347 stakes are a little bit different. 72 00:04:02,997 --> 00:04:07,687 Even with regulation, there are no patient lives at stake there, right? 73 00:04:07,687 --> 00:04:10,127 So pace definitely feels and looks different. 74 00:04:11,007 --> 00:04:15,247 I do feel that API-driven ecosystem and modern infrastructure were 75 00:04:15,247 --> 00:04:18,097 far more advanced in Fintech before I got to the AMA. 76 00:04:18,647 --> 00:04:23,507 And I kind of think that kind of led me to think about how can I think 77 00:04:23,517 --> 00:04:26,367 about scalability and flexibility into some of the problems I'll be 78 00:04:26,367 --> 00:04:27,977 solving here into healthcare, right? 79 00:04:28,357 --> 00:04:31,207 Think about iterative value creation think about unifying 80 00:04:31,207 --> 00:04:33,077 data assets and accessibility. 81 00:04:33,467 --> 00:04:36,907 Those things translate really well into healthcare too, right? 82 00:04:37,187 --> 00:04:38,927 So that's how I think about those things. 83 00:04:40,541 --> 00:04:42,881 Gordon Wong: You underscored a familiar distinction for me. 84 00:04:43,261 --> 00:04:45,511 Like, frequently as engineers, we talk about things like 85 00:04:46,161 --> 00:04:49,721 velocity or total volume, real time analytics and so on, right? 86 00:04:49,981 --> 00:04:54,371 And so, as engineers, we might think that fintech and healthcare are 87 00:04:54,371 --> 00:04:57,581 different, but what I just heard you say is that when you bubble out 88 00:04:57,581 --> 00:05:01,211 to the problem, the larger problem, they actually have a lot in common. 89 00:05:01,261 --> 00:05:01,751 Is that right? 90 00:05:02,461 --> 00:05:03,401 Sandeep Dhamale: Yep, that is correct. 91 00:05:03,491 --> 00:05:07,251 And that's one thing, a cool thing about being an engineer, you're able to look 92 00:05:07,251 --> 00:05:12,631 at the commonalities across the problem spaces and bring on thinking that really 93 00:05:12,911 --> 00:05:14,341 makes you see the art of possible. 94 00:05:14,691 --> 00:05:17,391 Because you, if you've solved a problem a certain way, why can't 95 00:05:17,391 --> 00:05:18,521 you solve it for this industry? 96 00:05:18,521 --> 00:05:21,115 Those are the kind of things that engineers always love. 97 00:05:21,115 --> 00:05:25,071 And I, I have seen those commonalities first hand for sure, especially when 98 00:05:25,071 --> 00:05:29,151 it comes to, uh, solving for data unifying all of those data assets to 99 00:05:29,151 --> 00:05:30,651 leverage the value from that data. 100 00:05:32,711 --> 00:05:35,831 Gordon Wong: Multiple years in the fintech world, built some really cool 101 00:05:35,831 --> 00:05:37,921 solutions, now you're in healthcare. 102 00:05:38,351 --> 00:05:41,681 Healthcare data comes with unique challenges, such as silos and compliance, 103 00:05:42,101 --> 00:05:45,071 legacy systems, heterogeneous systems. 104 00:05:45,121 --> 00:05:47,481 What are the foundational hurdles you've encountered, and 105 00:05:47,481 --> 00:05:48,361 how have you addressed them? 106 00:05:49,621 --> 00:05:50,131 Sandeep Dhamale: Right. 107 00:05:50,391 --> 00:05:55,381 So foundational hurdles, like some of the things that you've mentioned is 108 00:05:55,701 --> 00:06:01,401 healthcare is in a state what it is because of various different reasons. 109 00:06:01,461 --> 00:06:05,141 Of course compliance and speed being one of the reasons, but 110 00:06:05,141 --> 00:06:09,031 because of that the problem spaces of legacy infrastructure exist 111 00:06:09,341 --> 00:06:09,941 a lot more. 112 00:06:09,941 --> 00:06:14,501 So data fragmentation has creeped in a lot in my opinion because there are so 113 00:06:14,501 --> 00:06:18,321 many different systems, they're siloed, the data doesn't talk to each other. 114 00:06:18,641 --> 00:06:22,421 That has been one common theme that I've noticed across healthcare. 115 00:06:22,751 --> 00:06:27,431 It's also a lot of build versus buy challenges that come into 116 00:06:27,431 --> 00:06:31,391 places and decision making that is happening in a siloed manner. 117 00:06:31,401 --> 00:06:36,821 So there's duplication of platforms data being replicated across multiple places. 118 00:06:36,831 --> 00:06:41,011 So there's that redundancy that's happening which actually some of the 119 00:06:41,021 --> 00:06:46,321 recent regulation like HIPAA and GDPR are only putting a more spotlight on 120 00:06:46,851 --> 00:06:51,191 like if you look at where is your right to forget or if you go to look for those 121 00:06:51,201 --> 00:06:55,661 records, it's not just in one place, but it's in like 26 different places 122 00:06:55,661 --> 00:06:57,361 across the enterprise that you learn. 123 00:06:57,391 --> 00:07:02,191 And then, then Joel on some days emailing spreadsheets of the same 124 00:07:02,191 --> 00:07:05,671 data to somewhere and that only exacerbates the problem, right? 125 00:07:05,671 --> 00:07:09,091 So I think the hurdle was the fragmentation and trying to think 126 00:07:09,091 --> 00:07:14,021 about how can we organize this data in a centralized place so that we 127 00:07:14,031 --> 00:07:18,021 have a better control and better data, unified data management in place. 128 00:07:18,031 --> 00:07:21,281 So that was one of the first ones that I think about. 129 00:07:21,681 --> 00:07:25,671 I really also think about governance frameworks and compliance for designs 130 00:07:25,701 --> 00:07:27,711 is becoming very, very crucial. 131 00:07:28,081 --> 00:07:33,001 But while I emphasize those, I also want to emphasize flexibility for 132 00:07:33,001 --> 00:07:38,231 growth and thinking about scalability in mind are as important as well. 133 00:07:38,231 --> 00:07:42,161 So yeah, I, I think the hurdles are really data fragmentation and 134 00:07:42,161 --> 00:07:45,251 interoperability and how do we really get those all together. 135 00:07:47,221 --> 00:07:50,701 Gordon Wong: You know, at Newfire, one of the things I do with our clients 136 00:07:50,711 --> 00:07:54,801 is I advise them to think about the ROI of their data efforts, right? 137 00:07:55,061 --> 00:07:58,811 You have to consider what are you going to get out of this and what are the costs. 138 00:07:59,111 --> 00:08:02,371 Now, in fintech, it's probably a little bit easier to measure that, right, because 139 00:08:02,371 --> 00:08:03,871 you're looking at financial returns. 140 00:08:04,671 --> 00:08:07,481 Do you have any suggestions on how to measure the ROI of 141 00:08:08,171 --> 00:08:09,631 data efforts in healthcare? 142 00:08:10,836 --> 00:08:11,336 Sandeep Dhamale: Sure. 143 00:08:11,386 --> 00:08:13,117 You can look at it two different ways, right? 144 00:08:13,167 --> 00:08:17,137 Definitely start with your use case in mind and what you're really what 145 00:08:17,157 --> 00:08:21,557 personas you're trying to serve, because that's going to determine your ROI 146 00:08:21,707 --> 00:08:25,177 case, because in some cases it's a data product that you're building, which is 147 00:08:25,187 --> 00:08:27,147 going to have a real financial incentive. 148 00:08:27,207 --> 00:08:28,397 And that's why you're building it. 149 00:08:28,747 --> 00:08:31,787 In some cases, you're really thinking about administrative use cases. 150 00:08:31,787 --> 00:08:34,207 And you're really trying to think about how. 151 00:08:34,387 --> 00:08:38,217 How this problem will make everybody's life easier and get the type of 152 00:08:38,217 --> 00:08:40,197 compliance and processes that we need. 153 00:08:40,257 --> 00:08:45,687 And ROI in, when I was building infrastructure, we've done it both ways. 154 00:08:45,847 --> 00:08:50,077 Uh, one is thinking about what is our current total cost of ownership 155 00:08:50,077 --> 00:08:53,137 of these siloed data systems and how much are we really spending. 156 00:08:53,767 --> 00:08:58,402 And two, thinking about when we get to our target vision of a unified data 157 00:08:58,402 --> 00:09:02,172 infrastructure— what is the cost of running the business going to look like, 158 00:09:02,432 --> 00:09:04,302 and how do we transition from A to B. 159 00:09:04,302 --> 00:09:06,482 And there are always so many wins to be had. 160 00:09:06,492 --> 00:09:10,932 You're, you almost always certainly decrease your cost and reduce your risk. 161 00:09:11,252 --> 00:09:14,122 So that's that's a win with cloud based architectures. 162 00:09:14,122 --> 00:09:16,952 You're always guaranteed more and more flexibility that kind of 163 00:09:17,422 --> 00:09:19,082 prepares you for the future evolution. 164 00:09:19,572 --> 00:09:19,952 And. 165 00:09:20,582 --> 00:09:24,592 And then you realize that while you're building this infrastructure, if you 166 00:09:24,672 --> 00:09:29,762 attach a couple of data product or real revenue generating use cases to back 167 00:09:29,762 --> 00:09:34,702 it up, you really bake in the cost of maintaining this infrastructure that 168 00:09:34,702 --> 00:09:38,972 kind of gets offset by the, by those revenue generating products that you've 169 00:09:39,022 --> 00:09:43,822 included in the first cut of the draft that you're thinking about the vision. 170 00:09:44,382 --> 00:09:48,482 And, and it has now unlocked value because you can build the product 3, 4, 171 00:09:48,552 --> 00:09:53,527 5, 6 fairly quickly with reusing all of the infrastructure that you've really 172 00:09:53,527 --> 00:09:55,207 built in for first couple of things. 173 00:09:55,547 --> 00:09:57,377 And then the ROI really scales from there. 174 00:09:57,617 --> 00:10:01,767 So definitely when you're thinking about it, have those principles in mind is what 175 00:10:01,767 --> 00:10:06,637 I is what has benefited me and I think should benefit our listeners as well. 176 00:10:07,224 --> 00:10:09,104 Gordon Wong: We use a lot of metaphors in our industry, right? 177 00:10:09,104 --> 00:10:13,744 We talk about DAGs, directed graphs, we talk about pipelines, we talk 178 00:10:13,754 --> 00:10:17,704 about manufacturing moving data into knowledge, but more and more I'm 179 00:10:17,704 --> 00:10:21,584 thinking that the correct metaphor is that this is actually a marketplace. 180 00:10:22,514 --> 00:10:25,744 We have sources on one side and we have consumers on the other side and 181 00:10:25,754 --> 00:10:31,014 we're trying to enable transactions and any good marketplace should take 182 00:10:31,014 --> 00:10:32,744 as little of a cut as possible, right? 183 00:10:33,069 --> 00:10:33,619 Sandeep Dhamale: Yes. 184 00:10:33,974 --> 00:10:34,284 Gordon Wong: Right? 185 00:10:34,464 --> 00:10:36,594 So does that, does that metaphor hold in healthcare? 186 00:10:36,594 --> 00:10:37,844 Do you think, do you find that valuable? 187 00:10:38,969 --> 00:10:39,909 Sandeep Dhamale: I do actually. 188 00:10:39,969 --> 00:10:45,299 The more and more I think about how to bring value to the data, it's really to 189 00:10:45,329 --> 00:10:49,984 try to think about how you're really going to cater this data and what packaging 190 00:10:50,274 --> 00:10:54,214 and what formats and how little or how big of that package needs to be. 191 00:10:54,214 --> 00:10:56,094 So you really have to think about the personas. 192 00:10:56,524 --> 00:10:59,864 One of the other thing that's where data marketplaces are 193 00:10:59,874 --> 00:11:02,764 becoming so useful in my mind. 194 00:11:03,074 --> 00:11:05,724 And it also shifts to the healthcare because I think the 195 00:11:05,734 --> 00:11:07,014 problem is, not very different. 196 00:11:07,014 --> 00:11:08,444 You're thinking about interoperability. 197 00:11:08,444 --> 00:11:13,184 You're thinking about making it more accessible and accessible is the is key 198 00:11:13,224 --> 00:11:16,954 when you think about marketplace like concepts is like you're coming to a place. 199 00:11:17,164 --> 00:11:19,524 I always think of it like a grocery store, right? 200 00:11:19,824 --> 00:11:23,754 You go to a grocery store aisle you, you can pick up a you, you know exactly 201 00:11:23,754 --> 00:11:24,944 what you're doing in the grocery store. 202 00:11:24,944 --> 00:11:28,674 You know where to find, Your vegetables, where you want to find packaged 203 00:11:28,674 --> 00:11:30,574 foods or where you want to find milk. 204 00:11:30,574 --> 00:11:33,874 So there, it's all well organized, where the dairy section is and all that. 205 00:11:34,214 --> 00:11:37,144 You can go to that section, you pick up a product of the shelf, you can 206 00:11:37,144 --> 00:11:41,024 read all the labels, you know what you're getting and how it was sourced, 207 00:11:41,024 --> 00:11:44,514 etc, etc, what the ingredients are, or the nutrition information, right? 208 00:11:44,564 --> 00:11:47,874 It's readily available for you to make your decisions right there and there. 209 00:11:47,894 --> 00:11:52,014 So you don't feel very confused as to what you're going to walk out with. 210 00:11:52,034 --> 00:11:55,314 You're going to walk out with a whole milk or a 2 percent milk, 211 00:11:55,514 --> 00:11:56,344 those are the kind of things. 212 00:11:56,624 --> 00:11:58,784 So I also think about data marketplaces the same way. 213 00:11:58,784 --> 00:12:02,549 If you have your data products very well listed and what you're 214 00:12:02,549 --> 00:12:04,309 getting is clear for the consumer 215 00:12:04,339 --> 00:12:05,119 that's a win. 216 00:12:05,129 --> 00:12:09,049 That's how you should think about packaging your data products in a way that 217 00:12:09,089 --> 00:12:13,519 a consumer comes to a data marketplace that they're getting what they're needing. 218 00:12:14,229 --> 00:12:15,639 And that's one metaphor. 219 00:12:15,699 --> 00:12:19,429 And then the second metaphor also that really resonated with me once 220 00:12:19,429 --> 00:12:21,669 was especially thinking about privacy. 221 00:12:22,029 --> 00:12:25,939 You take that grocery store and add a pharmacy section to it, and now 222 00:12:25,939 --> 00:12:29,264 you need to have a prescription to get a particular product, right? 223 00:12:29,734 --> 00:12:32,334 You're walking up to a pharmacy and you're telling, I have a prescription. 224 00:12:32,334 --> 00:12:36,494 So that's to me most, some of the data assets in that marketplace could be 225 00:12:36,494 --> 00:12:38,764 going that way because they're protected. 226 00:12:38,774 --> 00:12:41,494 They are important and with sensitive information. 227 00:12:41,494 --> 00:12:43,784 So personas need to be going that way. 228 00:12:43,784 --> 00:12:44,874 So I agree with you. 229 00:12:45,214 --> 00:12:49,064 I think data products and the marketplace's vision translates well, 230 00:12:49,154 --> 00:12:53,339 even into the healthcare ecosystem because we're trying to really bring 231 00:12:53,339 --> 00:12:56,689 the same type of consumer experience to healthcare that we've seen elsewhere. 232 00:12:58,119 --> 00:12:59,989 Gordon Wong: This is turning into an economics podcast. 233 00:13:00,269 --> 00:13:02,940 Some of the concepts you brought up earlier were definitely, you talked about, 234 00:13:03,399 --> 00:13:05,029 basically fixed and variable costs, right? 235 00:13:05,029 --> 00:13:10,189 And if costs are lower, you can cover, if you have a baseline products that cover 236 00:13:10,579 --> 00:13:13,679 most of the costs of the platform, you can afford to bring additional products. 237 00:13:13,829 --> 00:13:17,649 So that seems pretty key to scalability. 238 00:13:18,049 --> 00:13:18,299 All right. 239 00:13:18,359 --> 00:13:20,429 So I want to hear a little bit more about some of the things 240 00:13:20,429 --> 00:13:21,659 you specifically built, right? 241 00:13:21,709 --> 00:13:24,199 Particularly, I to hear about the AMA Intelligent Platform. 242 00:13:24,689 --> 00:13:25,609 Can you tell me about that? 243 00:13:26,888 --> 00:13:29,868 Sandeep Dhamale: it's going to be easy to explain AMA Intelligent Platform because 244 00:13:29,868 --> 00:13:31,688 of some of the constructs we talked about. 245 00:13:32,028 --> 00:13:36,618 When I started at AMA in 2019 and when we were looking at our future 246 00:13:36,618 --> 00:13:41,418 of data products, one of the ways we were thinking about how can we bake 247 00:13:41,428 --> 00:13:46,133 in flexibility into an architecture that can allow us to scale for future. 248 00:13:46,133 --> 00:13:49,183 So some of the exact same principles that I described previously 249 00:13:49,513 --> 00:13:50,993 were were in consideration. 250 00:13:50,993 --> 00:13:55,593 We wanted to build a technical reusable infrastructure on, cloud basically 251 00:13:55,593 --> 00:13:59,713 so that we can scale the way we want to and add, keep adding capabilities. 252 00:13:59,973 --> 00:14:04,593 So think of it as, as more like a modern data platform initiative where you're 253 00:14:04,633 --> 00:14:08,173 thinking about what is your unified data strategy is going to be, where 254 00:14:08,173 --> 00:14:11,513 you're going to land the data, what kind of tools you're going to bring in to 255 00:14:11,793 --> 00:14:16,493 process the data and create your gold slash product repository, if you will 256 00:14:16,843 --> 00:14:18,563 what that infrastructure looks like. 257 00:14:18,888 --> 00:14:23,668 And then build capabilities on top of it of creating different API endpoints, 258 00:14:23,698 --> 00:14:28,988 creating different type of integration technologies for external facing whether 259 00:14:28,988 --> 00:14:33,538 they're external facing product consumers or internal users even, but thinking 260 00:14:33,538 --> 00:14:35,518 about data accessibility from the get go. 261 00:14:36,333 --> 00:14:38,063 So it was a culmination of all of this. 262 00:14:38,063 --> 00:14:42,343 So it's baked with technical reusable infrastructure in the back end with 263 00:14:42,343 --> 00:14:45,573 a digital front door on the front end that allows our customers or 264 00:14:45,583 --> 00:14:49,853 even internal audiences to interact with a developer portal built in. 265 00:14:49,873 --> 00:14:52,853 So you can you can look at our API stack directly. 266 00:14:52,853 --> 00:14:57,573 So sort of, gives you a marketplace, like a construct, but makes makes it easy 267 00:14:57,573 --> 00:15:01,513 for our customers to really interact with our data and make it more accessible. 268 00:15:01,533 --> 00:15:04,153 That's, that, that was the vision that we've been going in with, 269 00:15:04,343 --> 00:15:09,703 as we do more with this data, it also creates a framework so for 270 00:15:09,733 --> 00:15:13,673 customers to predict what, how it's going to look like and feel like. 271 00:15:13,993 --> 00:15:18,013 It's bringing some of the standardization to access patterns. 272 00:15:18,013 --> 00:15:22,483 So that way, when we launch a new data product, it's just going to be a walk in 273 00:15:22,483 --> 00:15:25,903 the park because they've done it already once and it's going to look very similar. 274 00:15:25,903 --> 00:15:29,363 And the, all of the constructs that they did with the first integration 275 00:15:29,553 --> 00:15:30,563 are going to just stand in there. 276 00:15:30,563 --> 00:15:30,803 Right. 277 00:15:30,803 --> 00:15:33,543 So that's how we think about AMA Intelligent Platform. 278 00:15:33,893 --> 00:15:37,743 So for what it's worth, for a lot of our newer data initiatives, 279 00:15:37,793 --> 00:15:40,973 they're all being facilitated by this platform infrastructure now. 280 00:15:41,303 --> 00:15:44,213 And that has given us flexibility for thinking about 281 00:15:44,213 --> 00:15:45,363 our data assets in the future. 282 00:15:46,323 --> 00:15:46,833 Gordon Wong: Incredible. 283 00:15:47,563 --> 00:15:50,828 So when you first, when the platform was first conceived What were 284 00:15:50,828 --> 00:15:53,748 some of the real world problems that you were hoping to solve? 285 00:15:53,798 --> 00:15:55,308 What, what kind of pain were people facing? 286 00:15:56,493 --> 00:15:58,733 Sandeep Dhamale: I will categorize into two sections, if you will. 287 00:15:58,733 --> 00:16:03,998 Uh, One is pains of how can we get our infrastructure ready for innovation? 288 00:16:04,358 --> 00:16:06,688 Because we've had a lot of legacy systems in place. 289 00:16:07,363 --> 00:16:11,153 And then the second is, of course, the real customer voice, right? 290 00:16:11,153 --> 00:16:14,563 Like we've been hearing you, you ship in certain formats. 291 00:16:14,563 --> 00:16:18,423 In a lot of cases, there were flat files, you, we don't know how to 292 00:16:18,453 --> 00:16:23,223 track change history of these data assets or in some cases, like it's not 293 00:16:23,223 --> 00:16:25,243 intuitive, the documentation around it. 294 00:16:25,583 --> 00:16:29,653 And it takes me a long time to understand your data once it's delivered. 295 00:16:29,653 --> 00:16:31,963 So those were some of the key challenges. 296 00:16:31,963 --> 00:16:36,153 When we were thinking about architecture, of course, we wanted to make sure we 297 00:16:36,153 --> 00:16:42,333 take a simpler approach of not trying to replace everything at once, but 298 00:16:42,343 --> 00:16:46,243 think about how can we bring a data platform that can be fed with some 299 00:16:46,243 --> 00:16:52,333 of the existing data assets that we have to create an infrastructure to 300 00:16:52,343 --> 00:16:53,743 think about how to solve problems. 301 00:16:53,823 --> 00:16:56,763 And then from a customer listening perspective, we were applying lenses 302 00:16:56,783 --> 00:17:01,093 as to, OK, you need more metadata along with our existing data assets. 303 00:17:01,103 --> 00:17:03,013 So how can we generate that metadata for you? 304 00:17:03,583 --> 00:17:07,013 OK, you need modern delivery mechanisms. 305 00:17:07,013 --> 00:17:10,823 In some cases, that just meant making sure we have API delivery formats. 306 00:17:11,623 --> 00:17:13,213 Some were really low hanging fruit. 307 00:17:13,213 --> 00:17:16,573 Some were really thinking about where this is going to go 308 00:17:16,583 --> 00:17:19,763 from , from our perspective, as well as our customers perspective. 309 00:17:19,763 --> 00:17:23,593 One of the good things is people really rely on AMS data. 310 00:17:23,633 --> 00:17:26,653 It's been powering healthcare ecosystem for 311 00:17:29,233 --> 00:17:33,113 decades now, and we just want to make sure we are good stewards of these data assets 312 00:17:33,463 --> 00:17:40,073 and continue to give it to our healthcare partners and customers in the right 313 00:17:40,623 --> 00:17:45,833 time, right, format and, and make sure that we continue to solve those challenges 314 00:17:45,833 --> 00:17:48,143 that we've solved in previous era as well. 315 00:17:49,613 --> 00:17:52,293 Gordon Wong: Now, I'm sure that every project planned along the way was no 316 00:17:52,293 --> 00:17:53,783 problem, and you hit every deadline. 317 00:17:53,793 --> 00:17:55,393 But were there any challenges? 318 00:17:56,703 --> 00:18:00,103 Sandeep Dhamale: Of course, there's no software project that ever 319 00:18:00,153 --> 00:18:02,023 throws a curveball at you, right? 320 00:18:02,033 --> 00:18:03,363 I think there were, right? 321 00:18:03,423 --> 00:18:08,053 But I think key to success in any of these initiatives is making 322 00:18:08,053 --> 00:18:09,913 sure you have an end in mind. 323 00:18:10,113 --> 00:18:14,663 I always say be stubborn on your vision, but be flexible on details and take, 324 00:18:14,813 --> 00:18:18,673 think about how your iterations could look different based on your priorities 325 00:18:19,038 --> 00:18:23,138 and what personas so your scope can be changed as you're 326 00:18:23,138 --> 00:18:24,478 learning about the marketplace. 327 00:18:24,478 --> 00:18:27,428 So think about what your milestones and their definitions can be 328 00:18:27,748 --> 00:18:29,928 can be and still add a value. 329 00:18:29,948 --> 00:18:34,378 So definitely go incremental smaller iterations and think about how can 330 00:18:34,378 --> 00:18:36,308 you ship a value every iteration. 331 00:18:36,743 --> 00:18:40,473 And then for your bigger milestones, be flexible as you're 332 00:18:40,473 --> 00:18:41,893 learning from the marketplace. 333 00:18:42,203 --> 00:18:47,843 I can tell you, for example, there were certain use cases that were not, that 334 00:18:47,853 --> 00:18:50,843 we thought were high value early on. 335 00:18:51,173 --> 00:18:55,203 And then to, to some of the market research and even internal hurdles 336 00:18:55,503 --> 00:18:58,303 that we thought, okay, it's going to take us longer to launch this. 337 00:18:58,648 --> 00:19:02,018 But we were able to pivot that into a different type of package 338 00:19:02,278 --> 00:19:05,408 and make sure make sure we still deliver value to our customers. 339 00:19:05,738 --> 00:19:09,118 But also our infrastructure initiative doesn't get stalled 340 00:19:09,418 --> 00:19:12,328 because you always need to make sure your customers are happy. 341 00:19:12,358 --> 00:19:16,488 And that kind of gives us the impetus and funding, in fact, also 342 00:19:16,488 --> 00:19:20,118 to make sure that you can self sustain and self fund the platform 343 00:19:20,118 --> 00:19:21,488 initiative that you've started here. 344 00:19:23,398 --> 00:19:25,568 Gordon Wong: Would you be willing to share some of your wins with us? 345 00:19:25,718 --> 00:19:26,648 Feel free to brag. 346 00:19:28,593 --> 00:19:33,223 Sandeep Dhamale: I really think the biggest one is is organizational one 347 00:19:33,513 --> 00:19:37,843 is taking everybody along on this, and I think some of the builders 348 00:19:37,843 --> 00:19:41,913 will relate to it is it's one thing to make, know what is right. 349 00:19:42,213 --> 00:19:46,363 And then you having a vision and being able to also evangelize that 350 00:19:46,363 --> 00:19:48,513 across it takes a village, right? 351 00:19:48,523 --> 00:19:52,473 So you have to really be able to talk to all the departments 352 00:19:52,583 --> 00:19:53,773 and all the stakeholders. 353 00:19:54,193 --> 00:19:59,093 And making sure that this this resonates with everyone, and this is the most 354 00:19:59,093 --> 00:20:02,823 important problem that you're trying to, so the framing of this initiative 355 00:20:03,193 --> 00:20:07,633 it took us a little bit I'm going to say a lot of attritions to get it right, 356 00:20:07,673 --> 00:20:11,953 but I think I feel very good about it right now that we are on a right path. 357 00:20:12,043 --> 00:20:13,543 Uh, so that's one. 358 00:20:13,983 --> 00:20:19,154 From an infrastructure and technical perspective, I think getting our 359 00:20:19,234 --> 00:20:24,094 developer program stood up was one of the highlights of this initiative. 360 00:20:24,454 --> 00:20:26,084 So I'll give you a back story, right? 361 00:20:26,084 --> 00:20:28,284 Like, we were building this infrastructure for all the 362 00:20:28,284 --> 00:20:29,504 reasons that I mentioned to you. 363 00:20:29,874 --> 00:20:36,544 And then we had an initiative where we wanted to make our CPT asset available 364 00:20:36,544 --> 00:20:43,124 to early stage builders with an open license kind of a thing, where they can 365 00:20:43,124 --> 00:20:47,444 get access to our CPT content when they're building their use cases and they've 366 00:20:47,444 --> 00:20:50,624 not figured out their go to market, for example, and they want to have access. 367 00:20:51,074 --> 00:20:54,394 It was a perfect marriage because we had an infrastructure in place. 368 00:20:54,464 --> 00:20:58,384 All we had to do was to think about what a CPT developer program could look 369 00:20:58,384 --> 00:21:02,804 like, invite these builders onto the AMA intelligent platform, and reuse the 370 00:21:02,804 --> 00:21:04,624 same infrastructure that we have, right? 371 00:21:04,914 --> 00:21:09,354 We have a developer portal, we have an API store, and that we had built 372 00:21:09,374 --> 00:21:12,724 for our and curated for our customers, but we were able to repurpose 373 00:21:13,224 --> 00:21:16,194 and launch a CPT developer program in matter of weeks. 374 00:21:16,224 --> 00:21:20,274 Like I, I think from conception to reality, it was like four to 375 00:21:20,274 --> 00:21:24,484 six weeks of project and boom, it was launched and it's one of the 376 00:21:24,484 --> 00:21:30,169 programs that has given us a community that actually participates with us. 377 00:21:30,289 --> 00:21:32,659 We were able to take those connections forward, 378 00:21:32,659 --> 00:21:36,109 we interacted with our conferences, we have quarterly Dev Chats. 379 00:21:36,109 --> 00:21:40,619 And they've been actually, uh, some of the people who are working on cool use 380 00:21:40,639 --> 00:21:42,399 cases with our content and data products. 381 00:21:42,399 --> 00:21:46,799 So it's always useful to hear perspective because industry is like you have matured. 382 00:21:47,649 --> 00:21:50,119 Participants that are using your data assets and there are some 383 00:21:50,129 --> 00:21:52,559 new use cases you're seeing, like how people are thinking about 384 00:21:52,559 --> 00:21:53,769 what they can do with your data. 385 00:21:54,119 --> 00:21:57,129 And having that voice baked in has really helped us. 386 00:21:57,129 --> 00:22:01,559 So I think that was one of the coolest achievement of AMA Intelligent 387 00:22:01,559 --> 00:22:04,989 Platform was to be able to launch a CPD developer program, for example. 388 00:22:05,029 --> 00:22:05,229 Yeah. 389 00:22:05,279 --> 00:22:12,089 I think what's next is, is definitely AI and generative AI on our minds. 390 00:22:12,509 --> 00:22:18,589 I also think it's, it was a good test when AI wave came along is some of the 391 00:22:18,589 --> 00:22:23,259 investments that we'd started making in this infrastructure stands true or not. 392 00:22:23,369 --> 00:22:24,689 And I think it has, right? 393 00:22:24,739 --> 00:22:31,034 If you think about, doing your AI or, more importantly, generative AI now, right? 394 00:22:31,704 --> 00:22:36,094 You need to have the infrastructure that backs it and you need to be ready with 395 00:22:36,404 --> 00:22:41,864 your data in a unified store and the right formats and the right point of delivery. 396 00:22:42,874 --> 00:22:47,004 And I think this initiative has started to push us in that direction. 397 00:22:47,434 --> 00:22:52,074 Some of the interesting things that we're thinking about there has been 398 00:22:52,794 --> 00:22:58,979 more about unstructured content that have been sitting in PDFs and different 399 00:22:58,979 --> 00:23:03,439 because a lot of our data assets were also, one of our business, I'm going 400 00:23:03,439 --> 00:23:07,819 to say, was really a book business or a human oriented business, which 401 00:23:07,819 --> 00:23:09,289 is now turning into a data business. 402 00:23:09,299 --> 00:23:10,009 So that's a shift. 403 00:23:10,339 --> 00:23:13,609 So we're taking some of those assets and trying to trying to 404 00:23:13,699 --> 00:23:18,809 really create RAG architectures or graph databases and vector stores. 405 00:23:19,149 --> 00:23:23,279 Those technologies, we were able to onboard onto our data platform in 406 00:23:23,279 --> 00:23:26,709 fairly a quick amount of time because we had started landing all of this 407 00:23:26,989 --> 00:23:28,509 into our data platform already. 408 00:23:28,799 --> 00:23:32,419 This was just, this is the good part about modern data platform that I 409 00:23:32,419 --> 00:23:37,349 think is you can bring another piece of technology into your stack fairly easily 410 00:23:37,679 --> 00:23:41,249 and it's able to, like you're thinking about your architecture all the time 411 00:23:41,249 --> 00:23:43,629 is volume, velocity and variety, right? 412 00:23:43,629 --> 00:23:47,789 So this variety of, the new variety of data and how do you really bring 413 00:23:47,789 --> 00:23:52,139 it to bring it to take advantage of vector storage, for example, what was, 414 00:23:52,139 --> 00:23:54,629 is something that we're working on, right? 415 00:23:54,629 --> 00:23:55,679 Like you asked me what's next. 416 00:23:55,689 --> 00:23:57,929 So this is definitely on our minds. 417 00:23:58,279 --> 00:24:01,259 And there are some couple of use cases that we can maybe talk about that 418 00:24:01,259 --> 00:24:03,139 we're thinking in that space as well. 419 00:24:03,189 --> 00:24:03,349 Yeah. 420 00:24:04,849 --> 00:24:06,289 Gordon Wong: Yeah, well, I'd love to hear those. 421 00:24:07,269 --> 00:24:07,709 Sandeep Dhamale: Sure. 422 00:24:07,749 --> 00:24:12,229 I mean, yeah with AI, when a couple of years ago, I think it's coming up on 423 00:24:12,229 --> 00:24:18,504 the second anniversary now when Chat GPT launched we, I think early on, we 424 00:24:18,504 --> 00:24:22,014 pretty much jumped in with thinking about what use cases and what are the 425 00:24:22,014 --> 00:24:24,534 framing that we want to put in place. 426 00:24:24,904 --> 00:24:28,334 I think we were bucketing our use cases into a everyday AI and a 427 00:24:28,334 --> 00:24:30,364 transformative AI type of category. 428 00:24:30,884 --> 00:24:35,714 With everyday I mean, can AI help us automate some of our tasks, make 429 00:24:35,714 --> 00:24:39,854 lives easy for our people give them hours back in the day and solve some 430 00:24:39,854 --> 00:24:43,714 of the search problems even that we had with these assets, right? 431 00:24:43,994 --> 00:24:49,004 So that's where we focused first is is trying to train a model and create a 432 00:24:49,004 --> 00:24:53,624 RAG infrastructure on our book business oriented data asset to see whether we 433 00:24:53,624 --> 00:24:58,084 can unlock some real value and create an assistant for our internal staff 434 00:24:58,484 --> 00:25:03,584 to ask questions and see if they can find right documents and references. 435 00:25:03,594 --> 00:25:08,264 So it's a combination of a search problem and a a really easy chat based 436 00:25:08,274 --> 00:25:13,314 interface to think if this intelligence layer can give you the answers 437 00:25:13,314 --> 00:25:16,954 right off the bat, because even this internal team that I'm talking about 438 00:25:17,324 --> 00:25:21,084 gets questions from different teams and even external people on guidance. 439 00:25:21,204 --> 00:25:24,184 So it is, the goal is to accelerate their workflow. 440 00:25:24,214 --> 00:25:28,599 So that's a pilot that we're running right now with the hope that this is the 441 00:25:28,599 --> 00:25:32,959 same infrastructure that we can actually package intelligence into future and think 442 00:25:32,959 --> 00:25:38,009 about what kind of intelligence offerings we can make into future products, right? 443 00:25:38,009 --> 00:25:41,309 So that's how that's an initiative that we've been thinking about really. 444 00:25:41,419 --> 00:25:43,569 Gordon Wong: Yeah, so keep leading into the marketplace, right? 445 00:25:43,569 --> 00:25:45,559 That's what it sounds like. 446 00:25:46,274 --> 00:25:46,959 Sandeep Dhamale: That is Correct 447 00:25:48,623 --> 00:25:50,513 Gordon Wong: One of the conversations I have with our clients at 448 00:25:50,513 --> 00:25:53,693 Newfire with I say, you know, encourage 'em to think about ROI. 449 00:25:53,783 --> 00:25:56,663 They get very tired of hearing me say ROI over and over again. 450 00:25:56,663 --> 00:25:58,223 But that's, that's really kind of everything. 451 00:25:58,253 --> 00:26:01,343 We have both the, the numerator, the denominator, the value 452 00:26:01,343 --> 00:26:02,633 we generate and the costs. 453 00:26:02,663 --> 00:26:07,093 And I think personally I see a tremendous amount of value in 454 00:26:07,093 --> 00:26:09,073 using AI to lower those costs. 455 00:26:09,163 --> 00:26:11,053 Lifting constraints, removing blockers. 456 00:26:11,473 --> 00:26:11,983 What are some. 457 00:26:12,308 --> 00:26:15,868 What are some areas of high friction you see in delivering data products 458 00:26:15,868 --> 00:26:17,088 that you think AI can help with? 459 00:26:18,598 --> 00:26:21,898 Sandeep Dhamale: I think I've got a good example for the question you're asking 460 00:26:21,948 --> 00:26:25,308 I mentioned we, we were a book business and we're trying to get into a data 461 00:26:25,308 --> 00:26:27,168 business with that particular data asset. 462 00:26:27,798 --> 00:26:32,178 First challenge is making sure you can segment the data the right way. 463 00:26:32,533 --> 00:26:32,763 Right? 464 00:26:32,763 --> 00:26:36,153 You have to break the content down into a different format altogether. 465 00:26:37,213 --> 00:26:41,823 And then, you have to look for common keywords, common tags, 466 00:26:41,873 --> 00:26:43,753 like create metadata on the fly. 467 00:26:44,103 --> 00:26:44,663 So, 468 00:26:44,884 --> 00:26:48,264 giving this to humans, to curate a new data 469 00:26:48,474 --> 00:26:54,824 asset, reading through books, and doing it like that, it's gonna be a 470 00:26:54,962 --> 00:26:56,522 It's going to take you forever to do it. 471 00:26:56,922 --> 00:27:01,662 I think what we're thinking about it is, and we've done already some pilots with 472 00:27:01,662 --> 00:27:06,732 this is, can you use large language model infrastructure to prompt it properly to 473 00:27:06,732 --> 00:27:12,642 create segments out of a big book pdf and see what kind of output it generates. 474 00:27:13,032 --> 00:27:16,982 Then take those sections and create like a preview summary that you can 475 00:27:16,982 --> 00:27:22,317 attach as a metadata tag into to that, create keywords for that sections 476 00:27:22,317 --> 00:27:26,527 and generate like 10 keywords each for that section and create that. 477 00:27:26,947 --> 00:27:32,297 And again, the goal is to accelerate the stewardship of this new 478 00:27:32,297 --> 00:27:33,457 data asset that we're creating. 479 00:27:33,457 --> 00:27:37,257 So these are tools that we're providing to our data teams or 480 00:27:37,257 --> 00:27:39,777 our content teams specifically. 481 00:27:40,242 --> 00:27:45,412 That who will be in charge of reviewing this content and essentially stamping it. 482 00:27:45,412 --> 00:27:49,542 So these are tools that are going to assist these data owners. 483 00:27:49,992 --> 00:27:54,572 To really look at what tags got generated, how it's really creating sections of it. 484 00:27:54,572 --> 00:27:58,062 So it's not like you're taking and fully automating it, but you're really 485 00:27:58,342 --> 00:28:02,232 making their lives easy and they are now reviewers of this process rather than 486 00:28:02,262 --> 00:28:06,307 taking a book content and try to break it down themselves one by one and reading 487 00:28:06,307 --> 00:28:07,717 through that's such a daunting task. 488 00:28:08,047 --> 00:28:12,647 I think AI is going to make that much more simpler and thinking about curation and 489 00:28:12,647 --> 00:28:17,767 metadata creation process, that's one area where you're gonna see your costs go down. 490 00:28:17,797 --> 00:28:22,217 And especially these initiatives now are generating value, right? 491 00:28:22,217 --> 00:28:26,697 Like this was a value that was sitting somewhere on a PDF 492 00:28:26,737 --> 00:28:29,027 document that was not getting 493 00:28:29,382 --> 00:28:34,212 uh, solved for now you have really created a data offering out of it. 494 00:28:34,222 --> 00:28:37,642 So you have an opportunity to go to market with a new offering. 495 00:28:38,092 --> 00:28:40,802 That's what we are seeing with this kind of initiative. 496 00:28:40,852 --> 00:28:44,372 So whatever total addressable market space you're looking at with that kind 497 00:28:44,372 --> 00:28:48,712 of content is definitely needs to be factored into your ROI calculation. 498 00:28:48,712 --> 00:28:49,262 That's one. 499 00:28:49,612 --> 00:28:54,564 And then the second thing I'm going to say is, because these data products are 500 00:28:54,574 --> 00:28:58,224 giving are going to the new markets, you're going to get more feedback. 501 00:28:58,254 --> 00:29:04,264 For example for, in our case, a real example, this was going into only 502 00:29:04,644 --> 00:29:08,994 into the hands of a certain section of people who actually read this content. 503 00:29:09,334 --> 00:29:13,669 Now we're able to take these micro Intelligence, if you will, and 504 00:29:13,669 --> 00:29:16,349 we're thinking about whether we can inject into newer workflows. 505 00:29:16,649 --> 00:29:19,679 So that's like a newer market you've you're thinking about now, 506 00:29:19,679 --> 00:29:22,559 not just the previous market that you were working with, right? 507 00:29:22,929 --> 00:29:28,219 So I think you really have to be able to re-imagine your domain and 508 00:29:28,219 --> 00:29:30,379 where it's heading in the age of AI. 509 00:29:30,619 --> 00:29:33,619 And then then think about what possibilities exist and 510 00:29:33,629 --> 00:29:34,679 which bets you want to take. 511 00:29:34,699 --> 00:29:38,439 But yeah, there are going to be bets that are going to be available to 512 00:29:38,439 --> 00:29:43,089 our, to like the listeners of this podcast, because I think once you 513 00:29:43,089 --> 00:29:46,319 have data, you, the next layer you're thinking about is intelligence, right? 514 00:29:46,319 --> 00:29:52,509 So I think the AI plays the role in simplifying your data processing as well 515 00:29:52,509 --> 00:29:56,409 as thinking about new use cases where your intelligence can be delivered. 516 00:29:56,529 --> 00:29:56,799 Yeah. 517 00:29:57,889 --> 00:30:00,789 Gordon Wong: As responsible stewards of the platforms we build, I want 518 00:30:00,789 --> 00:30:03,759 to take this opportunity to remind us of two expressions, right? 519 00:30:04,009 --> 00:30:05,399 One is knowledge is power. 520 00:30:06,059 --> 00:30:09,009 And then the other one is with great power comes great responsibility. 521 00:30:09,289 --> 00:30:10,109 Sandeep Dhamale: Absolutely. 522 00:30:10,149 --> 00:30:15,919 Gordon Wong: So how do you see AI helping us address the privacy concerns in 523 00:30:15,919 --> 00:30:17,239 healthcare and protecting our patients? 524 00:30:19,185 --> 00:30:20,735 Sandeep Dhamale: Great question again, right? 525 00:30:20,795 --> 00:30:22,495 I I think two ways. 526 00:30:22,545 --> 00:30:26,815 One, and especially, I think we mentioned this in somewhere in the podcast, right? 527 00:30:27,335 --> 00:30:30,195 When you're thinking about building your data products, think with 528 00:30:30,195 --> 00:30:32,785 your end users in mind and the personas you're building with. 529 00:30:33,315 --> 00:30:38,315 And they're going to be, once you do your market research, you can 530 00:30:38,325 --> 00:30:42,775 actually work with large language models to frame those personas right. 531 00:30:42,775 --> 00:30:45,355 And what kind of controls you need in place, like defining 532 00:30:45,355 --> 00:30:48,755 them itself can be done with the help of large language models. 533 00:30:48,755 --> 00:30:53,455 But I think, The daunting task of automating the processes that you need to 534 00:30:53,465 --> 00:30:58,305 have in place for each of these personas, like what are those processes going to 535 00:30:58,795 --> 00:31:03,955 be and then be able to create standard operating procedures according around it. 536 00:31:04,305 --> 00:31:05,835 It's always a time problem, right? 537 00:31:05,875 --> 00:31:09,455 You don't have enough people to think about think about these controls. 538 00:31:09,515 --> 00:31:12,905 You can automate a lot of these controls with with help of AI. 539 00:31:12,945 --> 00:31:15,195 I think it's a problem. 540 00:31:15,555 --> 00:31:21,285 And a balanced space because AI is going to make accessibility more prevalent. 541 00:31:21,875 --> 00:31:26,285 And then how can you also leverage AI with security first mindset 542 00:31:26,635 --> 00:31:29,655 to think about automating the processes and the governance 543 00:31:29,665 --> 00:31:31,645 framework around it is important. 544 00:31:31,675 --> 00:31:36,525 I think some of the things that you talked about data marketplaces and how 545 00:31:36,545 --> 00:31:40,185 you can get your access streamlined because of a data marketplace or that 546 00:31:40,185 --> 00:31:43,505 kind of an access pattern where people are coming to the same place to get 547 00:31:43,505 --> 00:31:47,385 their data I think it still remains. 548 00:31:47,545 --> 00:31:52,155 And these are the kind of platform thinking and processes will continue 549 00:31:52,155 --> 00:31:57,105 to make sure you have a governed platform and not just an accessibility 550 00:31:57,155 --> 00:32:01,215 that has now created a newer type of problem in security space. 551 00:32:02,645 --> 00:32:08,490 Gordon Wong: I like to find the security people in whatever organization 552 00:32:08,490 --> 00:32:12,180 I'm in and ask them to teach me how to be their best customer. 553 00:32:12,805 --> 00:32:15,375 Because otherwise, I'm just a walk, I'm just a walking problem, right? 554 00:32:17,205 --> 00:32:17,515 Sandeep Dhamale: Yeah. 555 00:32:17,515 --> 00:32:22,505 Well, one thing, one thing that I think especially in recent times, I've had a 556 00:32:22,585 --> 00:32:28,405 lot more appreciation is probably, and we did this in our recent all hands within 557 00:32:28,565 --> 00:32:33,035 our engineering team is to start thinking like everybody's a security engineer. 558 00:32:33,245 --> 00:32:34,415 That's part of your job. 559 00:32:34,735 --> 00:32:39,115 We've just tried to ingrain that into our build teams mindset 560 00:32:39,515 --> 00:32:44,580 is our centralized security team is only so much and they are going to evangelize 561 00:32:44,610 --> 00:32:49,270 the principles, but it's everybody's responsibility and really thinking about 562 00:32:49,620 --> 00:32:52,210 that at the design time is very important. 563 00:32:52,700 --> 00:32:55,360 That also brings me to the architecture point, right? 564 00:32:55,360 --> 00:32:59,230 Like evolution, evolvability versus maintainability. 565 00:32:59,640 --> 00:33:01,500 And that's a, that's always a balance. 566 00:33:01,500 --> 00:33:04,390 You want to have an architecture that scales and evolves. 567 00:33:04,805 --> 00:33:08,565 But it is also maintainable because if it's not maintainable, your 568 00:33:08,575 --> 00:33:10,135 security is going to be a nightmare. 569 00:33:10,195 --> 00:33:15,205 And that's why we're trying to bring a balance is it's your responsibility. 570 00:33:15,205 --> 00:33:16,785 It's everybody's responsibility. 571 00:33:16,785 --> 00:33:20,925 And we're trying to think about whether I, I don't we've not done it, but we're now 572 00:33:20,925 --> 00:33:26,705 going to start putting like a line into job descriptions also to start emphasizing 573 00:33:26,775 --> 00:33:30,955 this point more around security as we think about further and further. 574 00:33:31,095 --> 00:33:31,235 Yeah. 575 00:33:32,955 --> 00:33:34,775 Gordon Wong: I'm going to take the opportunity to jump on the soapbox. 576 00:33:34,825 --> 00:33:37,605 I'm going to try to frame my statement as a question. 577 00:33:37,605 --> 00:33:43,885 But I personally have seen that it feels like most organizations underinvest in the 578 00:33:43,885 --> 00:33:45,645 maintainability of their platforms, right? 579 00:33:45,645 --> 00:33:48,765 They focus on development costs, but you don't really deliver value 580 00:33:48,765 --> 00:33:51,185 until the thing, until whatever you're building is in production. 581 00:33:51,485 --> 00:33:52,525 Now, have you seen the same thing? 582 00:33:53,838 --> 00:33:58,488 Sandeep Dhamale: Yes, all the time we see this around and it's also, I, like I 583 00:33:58,488 --> 00:34:03,978 was saying, it's a balance and sometimes engineers have to be that voice of reason 584 00:34:04,378 --> 00:34:08,858 because I know everybody, including our stakeholders are excited to see the value 585 00:34:09,178 --> 00:34:13,778 to market and time to market as the most important thing and which is, which it is 586 00:34:13,778 --> 00:34:16,228 in my mind and you go through phases in. 587 00:34:16,963 --> 00:34:18,513 In my mind, you go through phases. 588 00:34:18,853 --> 00:34:22,593 If you're trying to build an MVP that you want to head to the market 589 00:34:22,593 --> 00:34:27,533 quickly and see what it does, you can think about you can think about 590 00:34:27,553 --> 00:34:29,763 what your maintainability looks like. 591 00:34:29,793 --> 00:34:33,993 But if you're making that decision, you really have to make sure that you're 592 00:34:34,003 --> 00:34:38,923 baking it as a part of consideration as to what what it takes to really get 593 00:34:38,933 --> 00:34:43,333 to the production grade product, if you will and have that on your roadmap. 594 00:34:43,483 --> 00:34:44,813 You don't just discount it. 595 00:34:44,843 --> 00:34:47,123 You really make sure that it's out there. 596 00:34:47,633 --> 00:34:52,233 In my mind, You don't really put maintainability completely out of 597 00:34:52,233 --> 00:34:56,573 the window from the beginning, you actually bake it in, you do trade offs 598 00:34:56,783 --> 00:35:01,253 and try to work with those trade offs based on your time to market, but have 599 00:35:01,253 --> 00:35:05,943 a roadmap, make sure it's out there and everybody has seen it, that your 600 00:35:06,063 --> 00:35:09,753 MVP can hit with X parameters, but to get to the production grade, you have 601 00:35:09,753 --> 00:35:15,193 to have X plus Y in place without that you don't really go live is important. 602 00:35:15,293 --> 00:35:19,703 And it's also allowing them to see why it's important, right. 603 00:35:19,733 --> 00:35:24,383 For ex I, I think that availability and maintainability, I like to take an example 604 00:35:24,383 --> 00:35:26,963 all the time as a bicycle example, right? 605 00:35:27,013 --> 00:35:31,353 If I was asked to design a cycle with a bike, with a requirement 606 00:35:31,353 --> 00:35:32,673 which is the most flexible, 607 00:35:33,068 --> 00:35:35,188 you can build like a monocycle, right? 608 00:35:35,218 --> 00:35:36,798 That one with a one tire. 609 00:35:37,328 --> 00:35:39,068 That one is pretty easy to maneuver. 610 00:35:39,068 --> 00:35:40,728 You can just do 360 on it. 611 00:35:41,128 --> 00:35:45,118 But it's not easy to ride because balancing on it is hard. 612 00:35:45,558 --> 00:35:48,318 And then there's tricycle, which you can build. 613 00:35:48,703 --> 00:35:51,243 Which has three wheels nobody's going to fall off of it. 614 00:35:51,243 --> 00:35:53,773 It's very easy to balance, but it doesn't go as fast. 615 00:35:53,773 --> 00:35:55,003 It's very hard to maneuver. 616 00:35:55,453 --> 00:35:58,113 And bicycle is like a balance with two wheels. 617 00:35:58,113 --> 00:36:02,363 You can do balancing pretty well, but you can also maneuver it. 618 00:36:02,703 --> 00:36:04,183 But if you look at it, it's a spectrum. 619 00:36:04,213 --> 00:36:07,213 Now you're making decision based on what market you're operating 620 00:36:07,293 --> 00:36:08,853 and what situation you're in. 621 00:36:08,903 --> 00:36:12,513 If you're trying to really go fast and you have a skilled guy who can 622 00:36:12,523 --> 00:36:18,243 actually do with one wheel, that's, that can only sustain for a little bit. 623 00:36:18,333 --> 00:36:21,483 I think the bike is the most prevalent architecture, right? 624 00:36:21,483 --> 00:36:25,133 You look for flexibility and it's a balance because you don't want, 625 00:36:25,133 --> 00:36:30,933 also want a tricycle that doesn't go as fast or can only cater to really 626 00:36:30,943 --> 00:36:35,343 specific niche of, in this case, small kids who are really trying to learn 627 00:36:35,803 --> 00:36:37,563 to bike to get to the next level. 628 00:36:37,843 --> 00:36:39,903 And so, yeah, it's always a balance, I think. 629 00:36:40,830 --> 00:36:41,120 Gordon Wong: I love that. 630 00:36:41,130 --> 00:36:41,680 I love that. 631 00:36:41,780 --> 00:36:42,180 I love that. 632 00:36:42,220 --> 00:36:45,940 And that's part of, I think that's all part of the purposeful architecture of 633 00:36:45,960 --> 00:36:49,050 these products and these platforms is that we have to understand the constraints 634 00:36:49,090 --> 00:36:52,600 and we need to make investments, measured investments in the right places, right? 635 00:36:52,630 --> 00:36:54,230 You can't, you don't get anything for free. 636 00:36:54,520 --> 00:36:54,900 So 637 00:36:55,875 --> 00:36:56,035 Sandeep Dhamale: That's 638 00:36:56,625 --> 00:36:56,895 true. 639 00:36:57,120 --> 00:36:58,510 Gordon Wong: What are you excited about next? 640 00:36:58,570 --> 00:36:59,050 What's coming? 641 00:36:59,070 --> 00:37:01,620 What kind of emerging technologies are coming that you can't 642 00:37:01,620 --> 00:37:02,410 wait to get your hands on? 643 00:37:04,069 --> 00:37:06,049 Sandeep Dhamale: I think I kind of covered it with AI. 644 00:37:06,069 --> 00:37:11,689 So I'll tell you one thing that I think it's going to happen is especially in 645 00:37:11,689 --> 00:37:15,019 the space that we're operating, all of the data companies, and especially 646 00:37:15,039 --> 00:37:19,109 companies who have data, like data that either they generate or the data 647 00:37:19,109 --> 00:37:23,214 that they have have been stewards of for a long, long time, have a real 648 00:37:23,214 --> 00:37:27,204 opportunity to think about what additional intelligence layer that they can build. 649 00:37:27,224 --> 00:37:28,634 So that's exciting in my mind. 650 00:37:28,634 --> 00:37:29,874 And we operate in that space. 651 00:37:29,874 --> 00:37:31,294 So very, very excited about that. 652 00:37:31,834 --> 00:37:35,164 The second thing is a lot of these models that are out there have 653 00:37:35,164 --> 00:37:40,464 been trained on internet data but not on enterprise data yet. 654 00:37:40,684 --> 00:37:45,214 So I think that's going to give rise to a lot of domain specific models and domain 655 00:37:45,214 --> 00:37:48,414 specific use cases and healthcare is, 656 00:37:48,464 --> 00:37:52,524 being in healthcare, I think that gives me a lot of hope to solve for problems that 657 00:37:52,534 --> 00:37:54,694 have been out of reach for a while now. 658 00:37:55,024 --> 00:37:57,674 Or just because of time resource constraints. 659 00:37:57,674 --> 00:38:01,834 And when do you really get to it when you have so many other things to solve, right? 660 00:38:01,864 --> 00:38:03,624 So I think that's exciting for me. 661 00:38:03,954 --> 00:38:07,764 And I think the third trend I'm noticing is the private infrastructures. 662 00:38:08,114 --> 00:38:11,524 Because a lot of people who have these data assets are kind of thinking 663 00:38:11,534 --> 00:38:16,634 about how can they not have to throw this data out to these large language 664 00:38:16,644 --> 00:38:21,834 models where they're not sure of the security and whether their data is not 665 00:38:21,854 --> 00:38:24,564 being trained for other purposes, etc. 666 00:38:24,564 --> 00:38:28,379 I think that's where private large language model infrastructures are gonna 667 00:38:28,419 --> 00:38:32,519 play a key role in my mind and there are vendors out there who are now supporting 668 00:38:32,519 --> 00:38:36,369 with with these initiatives but yeah, and that was also one of the things 669 00:38:36,369 --> 00:38:40,819 that we did, just so you're aware, is when when we build our infrastructure of 670 00:38:40,819 --> 00:38:43,749 the large language model that I talked to you about a couple of use cases. 671 00:38:44,539 --> 00:38:49,059 With security first mindset we actually because it was early days we got 672 00:38:49,069 --> 00:38:53,289 the open source model LLAMA 3 back then and LLAMA 2 and LLAMA 3 now. 673 00:38:53,619 --> 00:38:57,129 And we have also looked at other models to kind of bring on prem 674 00:38:57,129 --> 00:39:01,739 we've spun up a GPU infrastructure ourselves and have been training and 675 00:39:01,739 --> 00:39:03,159 creating that infrastructure there. 676 00:39:03,609 --> 00:39:07,109 The with the whole goal, and I think with newer frameworks and newer 677 00:39:07,109 --> 00:39:12,359 technologies, It's getting more easier and easier to control your infrastructure 678 00:39:12,599 --> 00:39:14,199 off these large language models. 679 00:39:14,559 --> 00:39:19,449 It's still niche, but at least it's possible that you are not now training 680 00:39:19,459 --> 00:39:22,459 your data and model elsewhere, right? 681 00:39:22,479 --> 00:39:24,979 You're, you can control the, where that goes. 682 00:39:24,979 --> 00:39:27,459 It can stay within your network or within your preferred 683 00:39:27,459 --> 00:39:28,969 partner network, if you will. 684 00:39:29,299 --> 00:39:31,379 And I think that's exciting to me. 685 00:39:31,379 --> 00:39:35,259 That kind of opens up a lot of use cases because your security teams, if 686 00:39:35,259 --> 00:39:38,469 they're not as worried, at least then you can think about leveraging large 687 00:39:38,469 --> 00:39:42,339 language infrastructure, large language model infrastructure better for your use 688 00:39:42,339 --> 00:39:44,279 cases because we have so many of them. 689 00:39:44,319 --> 00:39:48,699 And I think that, that really was a, you asked me about wins and brags. 690 00:39:48,699 --> 00:39:53,189 I think that was one of the wins and brags that I definitely am proud of that 691 00:39:53,219 --> 00:39:59,364 we were able to tap into a private LLM so early that kind of created possibilities 692 00:39:59,374 --> 00:40:04,224 to think about some of the data use cases or the content to data use cases 693 00:40:04,504 --> 00:40:07,934 that we talked about and that's what we'll be focusing on next year as well. 694 00:40:09,162 --> 00:40:11,402 Gordon Wong: I'm excited about the same things, I find myself 695 00:40:11,472 --> 00:40:12,292 totally aligned with you. 696 00:40:12,532 --> 00:40:15,502 Sandeep I've asked you a lot of questions in this 45 minutes or so. 697 00:40:15,502 --> 00:40:19,072 So I'm going to start a Newfire podcast tradition right now. 698 00:40:19,172 --> 00:40:21,952 I think the guests should get a chance to ask the host a question. 699 00:40:22,352 --> 00:40:23,462 Do you have any questions for me? 700 00:40:25,672 --> 00:40:26,382 Sandeep Dhamale: Sure, Gordon. 701 00:40:26,402 --> 00:40:28,602 First of all, it was fun chatting with you. 702 00:40:28,602 --> 00:40:33,082 I think we talked a lot about data and AI and you bring a very different 703 00:40:33,082 --> 00:40:36,122 perspective because you've been yourself at the helm of so many different 704 00:40:36,122 --> 00:40:37,792 initiatives across your career. 705 00:40:38,272 --> 00:40:42,282 I think in your current role, you're trying to help a lot of customers. 706 00:40:42,292 --> 00:40:46,693 So some of the things that I talked to you about are, are those the same things 707 00:40:46,693 --> 00:40:48,733 that you're seeing across the industry? 708 00:40:48,733 --> 00:40:51,893 I was just curious, like your perspective, because you have a 709 00:40:51,963 --> 00:40:55,823 vantage point like no other, and I'm trying to get your perspective as 710 00:40:55,823 --> 00:41:00,603 well on, on the trends that you're seeing across both data and AI space. 711 00:41:02,138 --> 00:41:03,398 Gordon Wong: Yeah, that's a really good question. 712 00:41:03,418 --> 00:41:06,428 So one of the things I think I realized when I think about it is that what's 713 00:41:06,438 --> 00:41:08,118 old is what's new again, right? 714 00:41:08,168 --> 00:41:14,038 So often what I'm finding is that we're building very similar, not that I think 715 00:41:14,038 --> 00:41:18,198 it's exactly the same solution, but we're solving the same problems now, at this 716 00:41:18,198 --> 00:41:20,188 point in my career, as I was 30 years ago. 717 00:41:21,158 --> 00:41:23,898 Because at the end of the day, what we're trying to do is take data, 718 00:41:23,918 --> 00:41:26,378 some kind of measurement, some kind of, and turn it into information and 719 00:41:26,378 --> 00:41:30,108 knowledge that allows better decisions, better actions, and better outcomes. 720 00:41:30,368 --> 00:41:30,738 Right? 721 00:41:31,768 --> 00:41:35,445 And what we're seeing though is that as the world's gotten more sophisticated 722 00:41:35,445 --> 00:41:38,125 and we have more technology and we have more capabilities, we're just 723 00:41:38,135 --> 00:41:39,745 tackling more sophisticated problems. 724 00:41:40,375 --> 00:41:44,090 30 years ago, it might've been trying to measure who my best customer is. 725 00:41:44,690 --> 00:41:49,980 But now we're trying to drive better human health outcomes at scale, right? 726 00:41:50,110 --> 00:41:52,100 We're solving problems that we've never solved before. 727 00:41:52,520 --> 00:41:55,570 And so that's what I think I'm taking away from this and what I'm 728 00:41:55,570 --> 00:41:59,180 reminding my clients at Newfire, and we talked to them is that, that 729 00:41:59,190 --> 00:42:01,900 yes, the technology is new, right? 730 00:42:01,900 --> 00:42:03,800 But the problems are old in some ways. 731 00:42:03,850 --> 00:42:05,110 It's just a different scale. 732 00:42:05,380 --> 00:42:09,270 So since we know how to solve these problems, let's bring that thinking, that 733 00:42:09,270 --> 00:42:10,920 learning we've had in the past there. 734 00:42:11,100 --> 00:42:13,520 You know, ROI, we talked about ROI a second ago, right? 735 00:42:13,660 --> 00:42:14,870 Numerator, denominator. 736 00:42:15,000 --> 00:42:15,370 Okay. 737 00:42:15,590 --> 00:42:18,100 So what are the costs that we are incurring in trying 738 00:42:18,100 --> 00:42:19,180 to drive better outcomes? 739 00:42:19,540 --> 00:42:21,730 Okay, how do we burn those down, right? 740 00:42:22,010 --> 00:42:23,260 What's the value we're driving? 741 00:42:23,300 --> 00:42:24,430 Hey, what's holding back the value? 742 00:42:24,430 --> 00:42:26,730 Where's the friction that is keeping us from delivering? 743 00:42:27,180 --> 00:42:31,910 If I tell my analysts, if you deliver a presentation to your 744 00:42:31,910 --> 00:42:35,280 audience you give them advice on what they should do in this situation. 745 00:42:35,600 --> 00:42:36,950 But they don't take that advice. 746 00:42:37,650 --> 00:42:39,300 You haven't really generated any value yet. 747 00:42:39,890 --> 00:42:44,910 So part of your job as an analyst is to be persuasive, is to communicate. 748 00:42:45,250 --> 00:42:48,850 And part of persuasion and communicating is also proving that our advice or our 749 00:42:48,850 --> 00:42:52,060 data products are safe to use, right? 750 00:42:52,280 --> 00:42:55,840 You used the metaphor earlier about milk and groceries, right? 751 00:42:56,110 --> 00:43:00,250 If I don't know this milk is safe to drink, I'm not going to drink it, right? 752 00:43:00,430 --> 00:43:05,515 And if our customers don't know that or our customers' stakeholders don't 753 00:43:05,515 --> 00:43:08,625 know that the data is safe to use, they're not going to use it, right? 754 00:43:08,825 --> 00:43:11,525 So I encourage our clients to make sure they are investing in 755 00:43:11,525 --> 00:43:15,075 that safety, that maintainability, and make sure it's visible. 756 00:43:15,305 --> 00:43:17,525 And so when someone asks, is your product safe to use? 757 00:43:17,525 --> 00:43:18,715 You say, absolutely, yes. 758 00:43:18,795 --> 00:43:20,045 And here's how I can prove it to you. 759 00:43:20,965 --> 00:43:21,255 Sandeep Dhamale: Yeah. 760 00:43:21,525 --> 00:43:25,075 Gordon Wong: We're seeing these same things across all our company, all 761 00:43:25,075 --> 00:43:28,745 the companies and yes, a lot of use cases in their details are new, a 762 00:43:28,745 --> 00:43:34,825 lot of chatbot stuff, a lot of using, models to predict outcomes, right? 763 00:43:34,825 --> 00:43:37,715 And then we're making those outcomes more deriving those predictions more 764 00:43:37,715 --> 00:43:42,215 quickly, it's greater accuracy further into the future, but also tying all 765 00:43:42,215 --> 00:43:45,635 these assets together to get a more holistic view , right, as opposed to 766 00:43:45,635 --> 00:43:49,285 just these little point views, if I use a simplistic metaphor, if I'm going 767 00:43:49,285 --> 00:43:52,795 to the beach, I want to know how to get there, what's the best clam shack, 768 00:43:52,825 --> 00:43:55,585 what the weather's going to be, what's the tide, I want it all in one place. 769 00:43:55,585 --> 00:43:55,885 And 770 00:43:55,885 --> 00:43:56,055 it. 771 00:43:56,115 --> 00:43:56,995 now it's getting easier. 772 00:43:57,725 --> 00:43:58,315 Sandeep Dhamale: Yeah, no. 773 00:43:58,315 --> 00:43:58,695 Love it. 774 00:43:58,705 --> 00:43:59,005 Love it. 775 00:43:59,075 --> 00:44:00,985 And that totally resonates with me, right? 776 00:44:00,985 --> 00:44:05,415 Like, these are the same problems that have existed, but the reach of 777 00:44:05,505 --> 00:44:10,770 our What the technology has enabled is that it's now actually possible. 778 00:44:10,830 --> 00:44:17,910 I always think in early 2000s, it was really hard to build a data platform 779 00:44:17,910 --> 00:44:21,390 that was, that can unify all of these data assets with the advent of cloud 780 00:44:21,390 --> 00:44:25,260 infrastructure that kind of makes your life much more easier to think about 781 00:44:25,280 --> 00:44:26,780 what it is going to look like, right? 782 00:44:26,780 --> 00:44:30,710 Like you have technologies at your disposal, which can hold variety of 783 00:44:30,710 --> 00:44:35,280 data, which was harder back in 2000s and with NoSQL and other technologies 784 00:44:35,600 --> 00:44:39,070 that have matured over the last few years, it's become possible. 785 00:44:39,160 --> 00:44:40,640 Same story with Vector and AI. 786 00:44:40,660 --> 00:44:42,880 I think it's another tool in your arsenal that's 787 00:44:43,135 --> 00:44:44,645 just gonna increase your reach. 788 00:44:44,695 --> 00:44:46,715 Really excited with those possibilities. 789 00:44:46,715 --> 00:44:50,815 And I totally love the images that you use there, yeah, of the beach. 790 00:44:50,845 --> 00:44:50,985 Yeah. 791 00:44:50,985 --> 00:44:56,145 And let's with, with this, the beach, beach hits home because I'm in Chicago 792 00:44:56,145 --> 00:44:58,915 right now and it's pretty, pretty gloomy. 793 00:44:58,915 --> 00:45:02,385 So I'm already thinking about which beach I can be next. 794 00:45:04,095 --> 00:45:04,515 Yeah. 795 00:45:18,498 --> 00:45:18,655 Gordon Wong: IMe too, me too. 796 00:45:18,655 --> 00:45:18,842 Sandeep, it was fantastic talking to you. 797 00:45:18,842 --> 00:45:18,897 I really enjoyed myself. 798 00:45:18,897 --> 00:45:18,945 I feel like I actually learned something. 799 00:45:18,945 --> 00:45:18,968 I'm looking forward for us to be working together again. 800 00:45:18,968 --> 00:45:18,975 And I hope you enjoyed yourself as well. 801 00:45:18,975 --> 00:45:19,518 Sandeep Dhamale: It was so much fun. 802 00:45:20,303 --> 00:45:22,943 I have always enjoyed interacting with Newfire team. 803 00:45:23,163 --> 00:45:24,613 This is an amazing team. 804 00:45:25,643 --> 00:45:32,113 Everyone I've interacted with this at this team has been super smart and I loved 805 00:45:32,163 --> 00:45:36,123 this conversation also about our data infrastructure and how the AI initiatives 806 00:45:36,123 --> 00:45:39,813 and how to think about data and AI infrastructure in this fast moving world. 807 00:45:40,149 --> 00:45:41,619 We should continue talking more. 808 00:45:41,629 --> 00:45:43,419 It's always fun to chat with you. 809 00:45:43,419 --> 00:45:44,579 Thank you for having me here. 810 00:45:45,254 --> 00:45:46,274 Gordon Wong: Of course, it's a pleasure. 811 00:45:46,524 --> 00:45:47,264 Thank you so much. 812 00:45:47,634 --> 00:45:48,163 Have a good day. 813 00:45:50,153 --> 00:45:50,803 Thanks for tuning in. 814 00:45:51,023 --> 00:45:53,753 Stay tuned for more episodes where we continue to explore the toughest 815 00:45:53,753 --> 00:45:56,003 challenges and smartest solutions in business and technology. 816 00:45:56,523 --> 00:45:57,323 Like and subscribe. 817 00:45:57,863 --> 00:46:00,493 Until next time, keep innovating and solving the hard problems. 818 00:46:00,913 --> 00:46:01,823 This is Hard Problems. 819 00:46:02,013 --> 00:46:02,783 Smart Solutions. 820 00:46:02,953 --> 00:46:04,323 The Newfire Podcast.