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.
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.
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.
Today, we'll explore how healthcare organizations can adopt scalable
data solutions to improve patient and operational outcomes.
Sandeep, welcome to the podcast.
Sandeep Dhamale: Thank you, Gordon.
Really excited to be here and I look forward to a fun conversation.
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?
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?
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?
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.
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.
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.
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?
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?
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.
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?
So that's how I think about those things.
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.
Is that right?
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?
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.
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?
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
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.
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.
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.
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.
So yeah, I, I think the hurdles are really data fragmentation and
interoperability and how do we really get those all together.
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.
Do you have any suggestions on how to measure the ROI of
data efforts in healthcare?
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.
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.
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.
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.
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.
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.
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?
Sandeep Dhamale: Yes.
Gordon Wong: Right?
So does that, does that metaphor hold in healthcare?
Do you think, do you find that valuable?
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.
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.
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.
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.
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
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.
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.
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.
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.
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?
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.
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.
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.
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.
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.
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.
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.
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?
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.
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.
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.
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.
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
time, right, format and, and make sure that we continue to solve those challenges
that we've solved in previous era as well.
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?
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
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.
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.
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.
Gordon Wong: Would you be willing to share some of your wins with us?
Feel free to brag.
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?
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.
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.
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
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,
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.
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.
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.
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.
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
fairly a quick amount of time because we had started landing all of this
into our data platform already.
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,
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.
Gordon Wong: Yeah, well, I'd love to hear those.
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.
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?
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.
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.
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?
So that's how that's an initiative that we've been thinking about really.
Gordon Wong: Yeah, so keep leading into the marketplace, right?
That's what it sounds like.
Sandeep Dhamale: That is Correct
Gordon Wong: One of the conversations I have with our clients at
Newfire with I say, you know, encourage 'em to think about ROI.
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.
What are some.
What are some areas of high friction you see in delivering data products
that you think AI can help with?
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.
And then, you have to look for common keywords, common tags,
like create metadata on the fly.
So,
giving this to humans, to curate a new data
asset, reading through books, and doing it like that, it's gonna be a
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.
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.
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.
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.
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
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.
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.
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.
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?
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.
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.
Sandeep Dhamale: Absolutely.
Gordon Wong: So how do you see AI helping us address the privacy concerns in
healthcare and protecting our patients?
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.
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.
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.
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.
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.
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?
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
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.
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.
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.
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.
Now, have you seen the same thing?
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.
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.
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.
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,
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.
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.
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.
And so, yeah, it's always a balance, I think.
Gordon Wong: 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.
So
Sandeep Dhamale: That's
true.
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?
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.
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,
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.
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.
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.
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.
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?
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.
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.
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?
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.
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.
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?
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?
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.
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?
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.
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.
Sandeep Dhamale: Yeah.
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?
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
it.
now it's getting easier.
Sandeep Dhamale: Yeah, no.
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?
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
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.
Gordon Wong: IMe 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.
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.
We should continue talking more.
It's always fun to chat with you.
Thank you for having me here.
Gordon Wong: Of course, it's a pleasure.
Thank you so much.
Have a good day.
Thanks for tuning in.
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