Welcome back to Data Driven, the podcast that peeks into the
rapidly evolving worlds of data science, artificial intelligence,
and the underlying magic of data engineering. Today's guest
is someone who's redefining the rules of the game in AI and data,
Ina Tokarev Saale. She's the CEO and founder of
Illumix, a company pioneering the use of generative semantic
fabric to make organizations AI ready. We'll dig into how
Ina's background as a frustrated data user sparked her innovative
journey, why 80% of enterprise decisions still aren't data
driven, and her bold vision for a future with app free workspaces
where AI copilots handle the heavy lifting. Oh, and we're
tackling the ultimate question. If the future is already here,
why does it still feel so delightfully chaotic? Sit
back, grab your favorite coffee mug, or a Maryland state flag
one if you're feeling fancy, and let's dive in.
Alright. Hello, and welcome back to Data Driven, the podcast where we explore the emergent
fields of data science, artificial intelligence, and, of course, it's all made
possible by data engineering. And with me today is my most favoritest
data engineer in the world, Andy Leonard. How's it going, Andy? It's going well,
Frank. It always warms my heart when you introduce me like that. Well, you are
my most favorite data engineer. Well, that's cool. You're well, you're my
most favoritest. I like, there's so many things. Right? Data
scientist, developer, evangelist.
I mean, there's all sorts of cool things that you do. Super,
certified person. What are you up to in certifies in certification?
12. Wow. Yeah. I'm in I'm in the
New York City area code now. So that's good. Next
up, the Bronx area code 718. So Wow. That's a
big jump. Yeah. Yeah. We're we're working on we're working on it, and I'm at
760 some odd consecutive days. I'm at the point now
where when I post anything on about Pluralsight
or, my number, the search or the number of
days, Pluralsight always sends me a congratulations, Frank. Keep
going. So, like, I'm on their radar now. So which is really
nice. I don't know. It's super cool. Yeah. It is super cool, which reminds me
I still have to do 2 days. But in the
virtual green room, we were talking about coffee mugs. We
were. And, we're we're I don't have a coffee mug with
me today, but, there's an
interesting anecdote from a previous show, which I think the show is live now, about
the Maryland state flag coffee mug, which is, pretty funny.
So today we have with us a very special guest,
Ina Tokarav Sala. She's the CEO and founder
of Illumix, and a pioneer
of generative semantic fabric, which I wanna know more about that, but it
empowers organizations with AI readiness throughout her career
leading data products, monetization, and as a data
stakeholder. Ina recognized the oxymoron of our
domain. Despite huge investments in data and analytics,
most business decisions are still not based on these data or
insights. And when I read that, I felt that one.
So she, she works she founded this company,
Lumix, which is, the the byline says, get your organization
data generative AI ready. So So welcome to the show, Ina.
And, tell us about this. Like, because I think this is a big problem
with generative AI. Well, first off, let's tackle the big
one, which is the idea that despite all this money that's been
thrown at data and analytics for at least 2 decades, probably
longer, a lot of decisions are not data driven.
Yeah. Fine. Can you hear me? Because
I see a little bit Yeah. We can hear you. Okay.
So yeah. Thank you. You're totally right. The the benchmark says
only 20% of decision making in enterprise is based on data.
And to me, I I have been around for a
while. So 25 years in data analytics, and it was
always about cloud, big data. But
what it actually boils down to? Are you able to
pull out whatever analysis of data you need when you have, like, question on
hand? Not really. And this is a situation in majority
of enterprises, right? Even if those huge data
teams and huge investments in infrastructure and all of that.
And to me, the biggest promise
of of LLMs in enterprise setting is to
to bring the contextual and relevant data
to the stakeholders in need.
Right? In this experience which is impromptu which
means it's improvised, it's governed and hallucination free, it's
transparent. So I I would totally love have to
have this experience where I'm in my Slack or Teams, right, and
I've been able to to chat with my data copilot
and ask a question and get the answer I can base decision happen.
Right? Not just an answer. I should be reverse engineering
with, you know, bunch of people.
Interesting. Interesting. But I don't think that I think that
the companies, they
they they they throw a lot of data. They store a lot of data. They
analyze a lot of data. But a lot of at the end of the day,
not all decisions, but a lot of decisions are not based on just the direct
decision of the data. They're based on quite frankly a lot
of it's particularly the higher the, higher the
level. Sometimes it's based on what's good for the person, not
necessarily the organization or the business, let alone the customer.
Do you think what are your thoughts on that? I'm familiar with the saying,
if you touch your data long enough, it will confess. That's
right. It goes exactly to the domain.
So I guess you can you can massage the results
right? But, secondhandly, when an
employee comes to me with suggestion with a business plan with,
you know some project I always ask like what's the ROI like what's
it going to be to spend and what's the impact on on you know
other activities and and what it's going to be on expense of
so having numbers having data to you
know to the basic decision or to bring to your boss is
always has been a struggle and it's still struggle today so I
think it overweights maybe some you know,
reluctance to have open data for all just for the
sake of of being able to to have specific context on it.
Interesting. That that is very interesting. And, you know, that I
think that's been the the purpose of a lot of
data driven activities in in corporations globally
is, you know, and for a very long time is how do you convert
data in its raw natural form into
information? Mhmm. And, you know, and and
defining information as, something I
can glance at and know, you know,
almost instantly how my enterprise is performing.
And that was kind of my opening line 20 years ago when I
started in data warehousing is to go talk
to a decision maker, CIO, CEO,
and, you know, try and do a very small, project,
a phase 0. And just ask them that, how do you know?
And the surprising answer, yeah, even then it was surprising,
was, you know, something along the lines of, well,
people email, information to
a lady out front or a secretary assistant guy out front,
and he or she compiles it and puts it into this summary,
and then they tell me. And so, you know, 1 PM
every day or, you know, Monday on 1 PM. I know how we
did last week. Something like that. It's very
manual processes. So does
does Illumix, address that? The
manual part? Yeah. Yeah. Totally. So
I don't think reports will go anywhere, but I think we'll
have, you know, at least 3 types of
experience with data. So I do I do believe in
application free future where you have a
question or a task and then you have a launcher and you
just, you know, articulate whatever request you have.
And in the background whatever applications, workloads, and data have
been engaged with each other to to basically come up with the
results. Right? So I do believe in this future. Right? So this is
the ultimate. Right? But I think we will have this intermediate
stage where we'll have a lot of copilots or
assisted insights in, in the context of
applications you're already using. So using your CRM systems, you will have
all kind of insights, suggestions, you know, data driven,
actions which which might come up with the system in your
workflow inside your context. Right? And you might have to have
this pure experience when you do go to analytic systems like BI
or something else where you do have your static dashboards,
day after day, same way that I go to, you know, to to my
CRM dashboards and see how pipeline is going and all of that. So I do
not them need to them to change. Right? I don't want to go to some
chatbot and and ask again and again the same question, like, what's the pipeline
conversion today? Right? I do want to have those static dashboards where I just,
you know, sneak peek and see if everything in line and
we we in the benchmark. So those three types of experiences, I
do not think they're going to to evaporate in
the future. Right now, we are mostly bound to the last type of
experience of being in the closed garden of our BI tools,
like this 3 modeled analytic experience and then we'll have this
phase where we do have embedded experience. Majority of the companies are
already suggesting some kind of improvements in the
space, some better, some halfway, let's
say. And and the ultimate goal is to to have this
launcher when for for majority of ad hoc
task of questions, you will have this improvised experience.
So a follow-up on that. You mentioned Copilot, and,
Microsoft has been the company that I've heard using that term most
often for some sort of digital assistance. It
to me, outsider looking in, although I I use the
tools, it it seems to have been a quantum leap,
this year in that technology. It just seems like last year, they were
talking about things that it might help with, and I've seen
all sorts of examples of this. But have you seen that? Has that been
your experience that in the last 12 months, these type of
assistants have just, you know, taken a giant step forward?
Mhmm. I will address this question together with the previous one, like, how
Illumax is is positioned in in this context. So I
do see many projects in the companies
which, and mainly, they're providing
copilots, for call centers or support centers
and mainly based on document summarization.
Right? So document summary is more,
lightweight and and risk averse use
of LLM technology where I can actually go and check the document
itself based on the resource. Right? So it's kind of and documents
are already articulated with lots of context in
business language. So it's kind of low hanging fruit and majority
of the companies go to the direction including, Microsoft.
Where Elamax goes Elamax actually,
tackles the market which is less,
less digested, the market of structured data. So you mentioned you
started your career in warehouse and, so warehouses,
databases, data lakes, business applications such as supply
chain, ARP, CRM, and all of that. All of that
con defined as structured data space. And despite the
name, it couldn't be less structured than it is at the
moment. Right? So you have If it is structured, it's not structured
the way you need it. Yeah. Exactly. So the nay namings are not meaningful, like
abbreviations, frank table, or for like abbreviations,
the, frank table or and this
transformation or alias. Right? So all those weird names especially under
SAP systems. I love that and and no
single source of truth. Right? In documents, you might have versions, but you do
still have some alignment to single source of truth. In data, you
can have many definitions even in the same
data source. And the thing is, if you put semantic
models like semantic search on top of them and it works by proximity,
you might have hallucinations and random answers every time you engage
with the tool. So this this is where we chose with
Illumix to to tackle the problem as,
basically, defining as a 3 step approach.
Right? The first step is getting data AI
ready. So there is no yeah. There is
no way of using generative I or AI analytics in general
if you do not have other data. But for analytics, which is
served to you as BI dashboard, it's actually feasible to do
manual data massaging. Right? Well, fun. Yeah.
Yeah. That's fun. That's near and dear to my heart as a as a data
engineer, data quality. Because
you can have the, you know, the fastest, best presentation, the
slickest graphics, and it could be totally lying to
you. And back, you know, even from the days of of
data warehousing all the way through today's semantic models and
dashboards, it's a the the quality
of the data store you're reporting against,
That that data quality, if you were to measure it, you know, there's a number
of ways to do it. But it's well north of
99% of that. And people see that, and they go, wow.
That that's super good. And it's like, no. No. It didn't. You can't do
predictive analytics off of something that's 99%
because that that 1% of bad data or
incorrect data or duplicate data will skew your results.
And what often, you know, the the layperson doesn't understand
is that if it lies to you and tells you you're gonna make a $1,000,000,000,
that's just as bad as it telling you you're only gonna make a
$1,000,000 if the if the truth is you're gonna you're at about 25,000,000.
That's your real projection if you were to follow that line out and do the
extrapolation, you know, properly. And you can make
bad decisions with an overestimation just as easily,
maybe more so than if it's an underestimation. Yeah.
Exactly. So this goes to to, to the ground truth of
your results as good as your data is. And you cannot
trust, simple semantic search
to solve all these problems for you. And
so for us, the baseline, the first use
case is to get data AI ready or generative AI ready And we
do use generative AI for that from day 1. We actually generated company
from 2021. Yeah. It's funny to say now. It it was very hard
to explain to our investors back then what it actually means.
Yeah. You know, I I get it. I mean, if you build on a crooked
foundation, you you can't get anything straight, you know,
out of that. So that makes perfect sense to me. And it and,
please correct me if I'm mischaracterizing, the work that Illumix
does. But is it automated,
AI automated, data quality? Is that really what you're
after? So, basically, we automated full
stack of LLM deployment for structured data, and it takes the
AI readiness part. AI readiness, which means we have automated
reconciliation, labeling, sensitivity tagging Okay.
Like lots of lots of data preparation which is automated.
Gartner actually named us as a call vendor for that lately. We have
this layer of a context automation. Right? So so any
LLM, any semantic model needs context and this context and reasoning
usually rebuild by data scientists. To me, it's controversial
because, you know we had data modelers which didn't
understand business logic and now we have data scientists who do not necessarily
fully understand business logic and the model into black
box experience of context. Right? So ElamX
reverses process. We actually automate context and we wrap it
up in augmented governance workflow so business people or
governance folks can actually certify it. So it's auto generated
context for LLMs but certifiable by humans. We do
believe that we need to bring human to the loop, right, to to certify
it. Yeah. And the last I love I'm sorry. I have
interrupted you, like, 3 times now, and I apologize. I haven't met 2. I
thought you paused. So finish please finish your thought.
No. No. I'm saying, like, 3 parts. So you already did data governance and the
actual alarm deployment because you need to interact with the whole thing, and the interaction
to have to has to be explainable and transparent. You need to understand
how, especially on structured data, you need to understand how
the question was calculated based, sorry, how answer was
calculated based on questions and how, data was
actually sourced, what's the lineage, what is the governance and access
control through search your clients. So all of that should be on the interaction layer.
So AI readiness, governance, and the interaction layer explainability to
the end user. Absolutely. Okay.
Thanks. And I do apologize again for the
interruption. So my my characterization of it as something that's just
data quality is is way low. There's a little bit of overlap between
data quality and what you're describing. You're talking about taking this into
that next level that is specific to, generative
AI and perhaps other, you know, AI related,
AI adjacent technologies, machine learning leaps to mind and stuff like
that. But your the tagging, the categorizing,
and all of the things you're describing there, that is next level.
And it's very interesting to me that you're
using AI to get data ready for AI.
That's an interesting combination. Mhmm. It makes sense, though. Right?
You can kinda scale out human capability with AI. I
think that's you you kind of alluded that with Newman in the loop. Right? Like,
I think I think where you were kinda going with that, again, don't wanna speak
for you, but it's like the idea that AI isn't gonna replace
humans. It's just gonna make humans more productive. Yeah.
For sure. Augment us because frankly speaking, no one
wants to to model data, you know, as their
career. We want to solve problems. Right? And to solve
problems, we we have to to understand what the problems are
And letting AI to surface the problems as alerts and for us
to to resolve them as conflicts takes, you
know, 1% to 10% of the time that it should take,
where we are busy, you know, wrangling data still. And, you know,
it's sad to some extent because data is growing and we cannot keep up.
No. That's a good point. Even if even if there are people out there and
some of our listeners may really do like modeling data. Right? But, you
know, Dow, they can model about 10 times the amount of data or maybe
a 100 times more. Right? And then ultimately, the expectation of
what a, you know, what a person
can do in a set period of time is gonna go up just
because I I I think I think you're on to something there. Plus,
I also I would also, like, double click on the idea that you said earlier,
which I think was very intriguing, was this notion of
a lot of the apps that you use would kind of fade away. You just
have this virtual assistant. You know, I I think back to
there's a number of scenes in, you know, Star Trek The Next Generation where they
have a conversation with the computer. Right? Mhmm. You know, you they
don't they use the computer. They get stuff done. There's no
Microsoft Word. There's no PowerPoint. Right? Like, there's no, like, it's
just the the there is no application. The application is kind of invisible. It
becomes the computer. And I think that's a very
intriguing kind of way. And if you had told me that a year ago, I
would have been very skeptical. Now I look at it, I'm like, I
mean, it's it's it's almost inevitable.
Yeah. Yeah. I agree with you. Futures here,
it's not evenly distributed as people say. So I
guess, you know, when you're attending conferences in Bay Area,
it's already it's already here. It happens. Right
and when you go to let's say Europe we
even just say you know just say a EU act in
Europe is is ramping up so it's all about
controls and and this is great So I do not think that regulation and
innovation, actually, jeopardize each other. I think
they should go hand by hand and, that's where I see
industry is going. So so East Coast approach, majority of our customers
are coming from East Coast US, Pharma,
financial services, insurance, highly regulated data
intensive companies. They have now,
sometimes even inventing standards for generative AI
implementations because everything is so new but companies
want to go fast. Right? So no one wants
to to downplay risks on one hand. On the other
hand, everyone want to, you know, to implement generative AI
and see the productivity cuts. It's, you know, it's evident productivity
cuts are already here with all those co pilots summarization,
what have you and this is where we are today. So I
think like again Bay Area running fast
and east is coming up with regulation. We will meet somewhere
in between. I believe in both. Well, if you kind of,
like, look at, like, historically, you know, when .coms first
started, right, there were a number of, hey. Look. You know, we're gonna sell pet
food online. Right? Like, and then it was
like, back in the dial up days, it didn't really make a lot of
sense. So it would just be easier for me to go to the store.
Whereas now, I mean, if you think about ecommerce, obviously,
Amazon is the £2,000,000,000 gorilla in the
room. I like, do I really
wanna think about, you know, dealing particularly as we get into the holiday season, do
I really wanna deal with the traffic at the mall or the store when I
can just click on something, either have, you know, groceries delivered
or, you know, I'm I'm okay waiting 2 days for
something to come up if I don't have to deal with them all.
Yeah. Totally. What's what's the difference between Black Friday
and Cyber Monday? No. It's not. Right? Like not really. Yeah.
Yeah. So it's like Not anymore. I remember Yeah. You
know? So we're recording this just before Black Friday. And,
you know, this whole idea of, you know, going to the store, get
the best deals, it's like, do I really wanna deal with the
crowd? No. Yeah. Although ironically, the name for the
podcast came on a Black Friday, while I was
at a Dunkin' Donuts, drinking coffee, waiting waiting
in line actually to get so there's a I'm a Krispy Kreme
person. So I'm Ah, okay. Yeah. So With you and
I, right, definitely. Right here. This is before we had a Krispy Kreme
near us. So it's I I have split sides, but yeah. Yeah.
Jeff's JT. He's a mess. From up north. So they are
they're Dunkin' Donuts. I've noticed this. They're Dunkin' Donuts, like, north of
Virginia. And he's in Maryland. I'm in Virginia. Then down
south, you rarely see a Dunkin' Donuts. I see more Dunkin' Donuts down
south than Krispy Kreme's up north, though, for sure. Yeah. But
I They're they're from Boston. That's why. Yeah. Oh, that's why. And then So at
Krispy Kreme's from Atlanta. And plus, it's funny. Right? Like, so I live in
Maryland Mhmm. Which depending on who whom you ask is either
north or south. So that's right. That's true.
Interesting. Interesting. We're a quarter state for sure. Yeah. That that's
that goes safe for Virginia. But I wanted to follow-up on, you know, you've
been we've been talking about all the cool stuff. I'm
gonna try and say this correctly. Illumix. Is that correct? Am I getting it
right? So Illumix name
from Illuminating the Dark Side of Organizational Data.
Illuminate like illuminate. Illuminate. I like that. And x x
for the x factor. Excellent. X for the x
factor. Yeah. What? And I'm not asking you to I'll
just ask a question. What are the risks in in what you're doing?
And, you know, what are the risks you're aware of and how are you addressing
those? Yeah.
So I think the biggest risk of 2025
is going to be, a TCO, total cost of
ownership. So already today,
it's, it's very hard for organizations to to
monitor where the generative AI tokens are spent.
And the benchmark say that 80%
of LLM tokens actually spend on customization
of off the shelf models. And that's not a good news because
which means ROI is is pretty low on on the actual
production use of generative AI in in enterprise.
And I think it doesn't get any better because the
customizations techniques which are used today gains a black box
performed by super expensive data scientists and
they're not very scalable for data that you don't want to, you know,
to schmooze around. I think it's cost prohibitive actually to bring data
to AI. You need to bring AI to data. So so putting
data in some graph structures for graph, frog, and all of that, it's to me,
it's cost prohibitive. So this is why I think that, the Telumex
position for 2025 is actually favorable because we bring this
transparency. We do create this, a virtual,
a semantic knowledge graph, which is transparent to certify, which is
created for business people. Based on business
logic. We do use extensively industry ontologies and so on so forth.
And I think the the most interesting part about generative AI is
we do not necessarily going to mimic processes that
the humans performed. Mhmm. We're going to invent
those processes. Right? So new new processes and new workflows. So
right now, a generative AI is deployed like like
analytics is deployed, which means you you have to
label your data, check the quality, usually manually, and then
you have to to prepare the test set which is fed
into customization of the model and then you actually provide the
context to on every question. So this is
very old fashioned or, you know, 40 years old
machine learning technique to to actually train generative
vi. So this is why why I'm saying that, many companies are
probably going to to mimic what Equinox does in the sense
that you have to you have to be focused on domain
specific knowledge, reason, ontologies, and knowledge graphs. You have
to onboard your customers automatically via metadata because
metadata has the factor all
activities in organization documented for us. We're
just under utilizing them, right? And then you bring your
business people, your domain experts, your governance teams to the
loop because you can simply cannot bring this business acumen,
to, you know, to data. You have to bring data to to those people.
That's an interesting thing because I've seen the the particularly is this this this
statistic around 80% of the tokens are being used to
manipulate the data. I have a microcosm example of that
where I use AI to augment my blog post, my blog
that I create, and I finally took
a closer look at this because I was spending a lot more on
the OpenAI API than I really wanted to. And I'm like, well,
what exactly am I I'm using a product called Fabric.
And I'm like, wait, what exactly is the source of this prompt? And I look
at it, and I'm like, I can't. It's a lot. It's a long prompt. And
I'm like, I really don't need that. Right? So we are gonna do a deep
dive in a show on Fabric at some point. Not not the Fabric Andy
works with, but there's an open source thing called fabric. There's
a I'm sure there are lawyers right now that are doing their
holiday shopping based on how much money they're gonna make off of this
dispute. But, the the short of it is, like,
I realized, like, well, no wonder why I spent so much money. I'm sending all
of this in my prompt plus the content. So I
actually in the verse before you joined in, Andy and I were talking, and I
was like, I actually got a really good result based on a more optimized
prompt. You know? And, you know, strictly speaking, it's
not I I like your approach of bringing the AI to the data rather than
bringing the data to the AI because that is expensive.
You know, I I think that bringing the AI to the data will be less
expensive. How less, I think, remains to be seen. But I like that approach,
right? Because that's typically what we've done, you know, and we've seen
huge upsides to that, whether it's from Hadoop bringing the
compute to the data rather than vice versa. I like that
approach. And it's backed by historical precedent. Right? So it's not
completely gonna be this crazy idea. It's just a very sensible
idea. Yeah. Yeah. I believe the future was already
invented. Right? So it's just the inclination of technologies we already have.
It's been healthy about it. So, we had
machine learning practices which are very healthy like feature
exploration, feature definitions and then we had neural net brute
force and then majority of companies used combination of both,
right, to to to be optimized. This is what I think what's happening with
generative AI. So this, you know, wild west of brute
force or great spend is going to be replaced by methods
which have, like, this automated context filtering or pre
processing and then use like fraction of your budget to to actually
run the query. Yeah. I remember hearing about a lot
of this in the late nineties. And, I worked for a company who
was a big SAP shop. I see you have a history with SAP. Yeah. And
this lady and and and so we were an we were the IT department. So
we were in the basement, but the analytics team back then was in
a closed in space inside the basement. So it was
like even more like, you know, I was the web developer, so I didn't
have a window, but I could see the window about 50 feet away.
But, like, when you when when you went
into this, like, you know, further enclosed space deeper into
the the the the the depths of the IT department,
there was the database team. And and and and in the back of that area
was the analytics group. And I remember this lady telling me
that she was working with these things called OLAP cubes. Oh, wow.
Yeah. And I was like, what is that? And then she went on this thing
and, you know, I'm remembering a conversation, oh my god,
almost 30 years ago. But I just remember walking away with,
like, that sounds either crazy because she's talking about,
like, you know, figuring out patterns. Right? So, you know, will
rainfall patterns in Australia affect not just the agricultural
side of the chemical business, but also the plastics purchasing
versus rainfall in the Amazon versus this and all of
that? And I just remember walking away from that conversation as I as I
as I as I leave the depths of the IT department back to my normal
kinda, basement. Back to the regular basement from
the sub basement. I remember thinking that is either the craziest thing I
ever heard or the most profound thing I ever heard, which
now with the, hindsight of time, it turns out it was the most profound
thing. Yeah. You you can think about it as
semantic layers of, you know, that era. Right?
Mhmm. Right. And I think You know go ahead.
I'm sorry. Sorry. I think it's delayed between the
between the connection. So I think around the same time I was
doing my bachelor and my project was about multi dimensional
theory. So multi dimensional geometry,
of these neural nets. So basically, you model neural nets as multi
dimensional graph and it does operational research calculations.
So it's exactly the same. You you model your universe in a
graph. Back then it wasn't MATLAB. We didn't have any, you
know, neural nets Right. Structures or graph structures and so you're
modeling in MATLAB in this weird language,
a graph which has a neural nets on there. And
this is exactly like modeling all of cubes. Right? A
multidimensional representation of your reality. Now,
unfortunately, we have a new technologies which,
which are semantic and context. Right? Large language
models and graphs, which do the same thing but much
more efficiently. Yeah. So this is amazing. Like, I
think it goes back to what you said. You know, The future's already here. It's
just not widely distributed yet, which I think is a William Gibson
quote, or is it a Esther Dyson quote? I forgot.
But it's one of those 2 kinda luminaries. Yep.
You you said what I was going to say, you know, and it
was, you know, more of what off of what Frank
said is it turns out that we're just
doing more nodal analysis and vector
geometry as a result of that. That's it did all start
with multidimensional and and grow from there. And
that's where these algorithms, like nearest neighbor
originated, was in that math. So
Yeah. Yeah. Great minds. Exactly. Exactly.
Alike. Exactly. Now you're
complimenting me. Thank you. I I feel I feel better
when smart people in the room agree with me.
No. I'm on the right path. You know, I employ
millennials. So so having people with experience in multidimensional
geometry and all of cubes, it's just a miracle to me to to start
with. You know? People now like Python, neural
nets, we do actually, the average age in in in
Lumex is around 35, 37, something like that. So we do
have like also pretty experienced folks, you know, but new talent,
they, they they're not familiar with all all of that.
And I think it's actually a disadvantage because,
when when you do know different patterns in architecture Yeah.
You can model them with new technology. Right? Make them more
efficient, but you already know what works and what doesn't, and it
helps. That yeah. That's a great point. The old
experience, you know, the experience that we have from doing this for
decades is that we see the patterns that have
repeated over time, architectural patterns and design patterns. And,
you know, and we know that they've
I I love that how you said that. The, you know, the future's already been
invented. We we realize that if we reapply some of these
patterns, that there are use cases for them, not just now, but
also in the future. So totally get you.
Too, you know, like,
you know, it it is painful to think that, you know, we've been in this
industry for decades. Right? It is a little hurts a little bit. But,
like, also, if you're listening to this, you've not been in the industry for
decades, and you're thinking like, woah. You know, what are these what are these
old geezers now? I would point out when I was
a young kid in the industry and, you know,
client server was like the new hotness. Right?
And, you know, the whole notion of going back to,
you know, cloud and and and and and, you know, terminal
and an old mainframe geezer basically said to me, like, this is just
this industry has a cycles. Right? It's like the fashion industry. This goes in
style. This goes out style. And it was like, I had that moment
of, like, wait. I think he's on to something, but he's just an old geezer,
so I won't listen. So, you know, so so
if you are a young buck, like, or,
buck is a male deer, right? What would be a Yes. A doe. A young
doe. So if you're a young buck or a young doe, I grew up
in New York City. So all of this wildlife thing is brand new. I'm here
for you. I'm here for you, Frank. So, you
know, listen to, like, some of the things that these, you know, more
experienced colleagues will say. Yeah. You know,
if you don't believe it right away, just put it on the shelf in your
mind because you're gonna need it later. It'll come up at some point.
And it's like, if you look at kind of, you know, everybody ran to the
cloud. Right? And cloud is effectively like a
mainframe effectively. Right? The same philosophy. Right? Centralized
computing somewhere else. Right? And then your browsers become
the terminals, terminals with fancy graphics, but terminals nonetheless.
Now I think you're gonna start seeing it kind of we're about due for a
seismic shift backwards, right, as people kinda move
repatriate data and things like that. Particularly, I think driven by AI
because of the cost of some of this. You know, I had this debate,
you know, the other day. It was like, you know, if if one of these
super clusters with, you know, a 100, 8 100,
all of this, if it costs, say, $500,000,
right, I could probably do the math, and that probably means
about, you know, there's a certain break even point,
and it's probably after about 7 or 8 fine tunings or full
on trainings where it's just cheaper to have it. Just buy it.
Yeah. Yeah. Yeah. Totally on that. And also, you
know, salary skills are the most expensive part. So you
want to spend it on your business specific problems and
not generic problems you can solve with software. Right? So
it's always like that. Yeah. Yeah. So,
I do think that, basically capacity to process data
is is going to be a challenge. Right? And this is why we
see that, that majority of,
of I would even say countries not
only specific enterprises, kind of gear
up with, with GPUs, FPGAs,
whatever hardware you have. Right? So do you see it in
middle east, in emirates? They they have national generative
vi grid and they're building it for, you know, not only government companies
but also private companies. We see the same in Europe
and I would assume, you know, US based telcos
are going to to provide those data centers with GPU soon
enough, right, for, you know, for everyone to purchase as an
alternative to the public cloud. Yes. And we'll
see it. So this is for starters. And second one, the second part where
you don't need, this, you know, heavy machinery,
you might just have your variables processing
parts of whatever generated AI on your end before sending to the cloud
because you do not necessarily need to to process everything in a central
manner. We basically have pretty powerful machines on
our hands or in our hand, you know, as
glasses as well. We can see that, and it's
going to be part of the processing. So the processing is going to be distributed.
You bring AI to your data, where your data is. You do
not shift your data all the time. It's not, it's not
cheap anymore. And we'll have this, as you mentioned,
those central repositories of mass processing
and those distributed powerhouses which are
small enough to to process data on on edge.
I think you're right. I think you're gonna see a set of data being processed
in one place. I think it's gonna be everywhere. There's gonna be some
and and I think that that introduces some interesting, consequences. Right?
So my wife works in IT security, and I can immediately hear her voice in
the back of my head. Contrary to what you think, ladies, we do
listen. We just don't always pay attention. But
I can hear her like, well, if compute's happening everywhere,
gee, couldn't like that be poisoned anywhere.
Right? I think I think that's going to be the next kind of thing. Right?
It's and it's again, it's a pattern. Right? Advancement.
Bad actors take advantage for that. Problem happens. And
then then that's the new thing. Right? So it's almost like you're you're building like
a, like a like a like a layer cake. Right? Like, you know, the cake
goes down then the frosting. The cake is the innovation. The frosting is
security, and then so on and so on. So Yeah. Yeah. Yeah.
So it basically back to the semantics. What we started is
semantic ontology as a baseline for generative AI.
It has multiple benefits. Single source of truth, of course, has the
benefits for accuracy. But also, if you're passing every
question to this semantic ontology context,
it's almost impossible to poison it because we're going to either
match to part of your logic or Right. Right. We're going to
miss. So it's it's another layer of security if you think about
it. So, so yeah.
That's an interesting point. All new. Yeah. All new ontology, all new
semantics have governance meaning, it has
accuracy meaning, it has also security meaning.
And also if you want to have single source of truth you have to to
have means to distribute it to those edge devices or
to to bring it back to central location and without ontologies, without
semantic layers, simply it's impossible to do that. I was gonna
say, like, the the the infrastructure, not just the computer infrastructure, but the
logical infrastructure to distribute this stuff,
it's probably not a trivial problem. That's the first thing that popped in my mind.
I was like, you know, like, oh, yeah. You're right about the distributed
activity on this data, but, wow, what does that
look like? What do updates look like? Like, the whole like, it's a it sounds
like a growth industry to me.
Definitely. Yeah. Yeah. I don't it's, it's
what we call, engineering problem. Right? So
creating ontology is data science or generative AI problem, but
distributing it, maintaining it, thinking it's its engineering problem.
Engineering problems tend to to have engineering solutions. Oh, Oh,
that's a good point. That's a good way to look at it. I like that.
I like that. So did you wanna do the, premade questions?
Because we haven't we've gone a few shows without them. If you're okay with those,
Ina, we can we can ask them. If not, that's fine
too. Of course. Yeah. Sure. Mhmm. So they're not they're not complicated.
They're more kinda just general questions. I pasted them in the chat.
But the first question and and you've had a a pretty
significant career with SAP and and before that. How'd you
find your way into this space? Did you find data or did
data find you? I
found my way to data by being frustrated
user. Right? So I started in engineering
and it was evident to me that
using data as engineer is not enough. You have to go to
data management. You have to fix those things because otherwise
I will I will going to be frustrated for the end of my life. Right?
So I went to data management analytics to to solve the problem
and I discovered that, as you mentioned, every experience
has a footprint. So my experience with graphs and with
operational research and multidimensional geometry and all of that is so
useful for data management. And it was actually exhilarating.
That's true. Like and I like that because, like, every experience does leave
a footprint. Like, you know, that that's cool. I'm gonna I'm gonna pull that out
as a special quote for the episode. That's a great quote. Yeah. So
our next question why we do these? Yeah. Is what's your favorite part of your
current gig? My favorite part of being a
founder is is
unlimited ability of experimentation,
right? So majority of my day actually say no
to things, not to experiment, which is which is hard, which is not fun part,
right? But, still, we can
make decisions and we can do
new stuff every day. So as a founder,
it's been very, very different than enterprise setting. And don't don't take
me wrong. Like, SAP is a huge place of growth and had
very, fulfilling career at SAP, you know, building
stuff, founding p and l's, running big organizations,
but but been able to to actually, you know,
start anything new. And, like, right now, we have this customer
and they want to to try Illumax on in
parallel on the newest, you know, newest BI
tool with semantic layer or and on the oldest
warehouse on premise at once. I'm like, okay. Challenge accepted.
Yeah. And next Wow. Yeah. And next day, you know, engineer
comes with we have this academic data set and they have these benchmarks.
Let's beat them. I'm like, yeah, let's do it. It could be cool stuff.
Right? Lovely. So, you know, you know, it's to some extent,
so we don't need to justify it, you know, business wise and but but in
majority of cases, we can. Cool.
We have a couple of complete the sentences. When I'm not working, I
enjoy blank. I used to
enjoy doing jogging and yoga when I'm not working.
Right? So right now when I'm not working which means when I'm not
traveling I just spend time with my family. Whatever
is the plan for the weekend if it's just you know Netflixing,
or cooking or hiking whatever is the plan I just
join So sometimes just, you know, plan it. But spending time with my
family has become, indulgence and I'm
very focused on that. Cool. Very cool. Our
next is I think the coolest thing in technology today
is blank. I think the coolest tech is
thing right now is not in tech. It's actually the
pull from CEOs of companies
for technology. This is something which didn't experience for decades.
So we were pushing cloud and big data and machine learning and deep learning. We
were explaining to business stakeholders why do they need that. Mhmm.
And now, so you're all coming and saying, okay, I want to have
chatbot experience for x y that, so just
build it. This is actually I think this is the coolest
part because it's kind of a removes majority of the friction that
we had to to deploy technology in the past.
Interesting. On our 3rd and final complete the sentence,
I look forward to the day when I can use technology to blank.
So many things. You know, travel has
been so frustrating lately, and, I
don't think what happened because it's like kind of technology goes
forward but airline, you know, travel technology,
hospitality technology in general, I don't feel it bridges a
gap. So I really look forward to the
future where I can just have this comment, this prompt
of plan, this conference in Dallas on
x and the system already knows all by preferences and
just done. Oh, boy. It would be it would be fantastic.
Yeah. That that the travel experience as I I've had to
travel quite a bit, like, for the past, like,
couple months, and it's just like, oh my god. Like, it never was
great, but awful is not a word I remember. But it's post
pandemic, I think it's gotten way worse. It's like there's just so many small things
that you could be done a lot better. I'm I'm a 100% with you on
that one. So true. So our our next
question is to, ask you to share something
different about yourself.
Sharing something different about myself. I think I'm a controversial
person in general. So, so some people,
so some people agree with, you know, with the degree
of, of living in the future. So I,
I, you know take myself as person who is very much in the future so
all this seed happening and I might be a little bit you know ahead because
I see the technology being developed in my mind is already there, it's already
used right? So and so where this is
where I see myself controversial because you know in majority of the
cases, then you sit over family dinner
and say, you know, we're still paying our bills
online when we have this notification. Right?
So everyday technology has
developed a lot. And when I'm speaking about this application
free future and, you know,
automated, x y zed. Sometimes or many
oftentimes on everyday level, we are still not there and
this is where people think that I'm too visionary or too
too dreamer on that. Interesting.
No. I'm with you on that one.
Growing up, I was the technical person in the family. So
Yeah. They don't they don't know what you're talking about. Right? I I I love
how the, you know, or, you know, they all they
all get confused until the printer breaks and then suddenly
But you're the smartest people in the room. That's why you're the smartest person in
the world. Alright. So where can people find out more about you and
Illumix? I love socializing on
LinkedIn. I don't know that many people think LinkedIn became a
marketing tool. I still see tons of valuable
discussions and I just absolutely love keeping in touch
on LinkedIn and and see the latest and greatest and I also share quite a
bit. So LinkedIn would be the the most
straightforward way in Atokaropsala on LinkedIn.
We do have blogs and I actually write many of
them. So if you go to illumeg.ai/blocks,
you will see lots of materials written on semantics,
on ontologies, on generative AI governance. So those
topics which are close to my heart, and we communicate quite
frequently on that. Very cool. Very cool. Very cool. So
so Audible is a sponsor. And if you
would, like to take advantage of a free month of
Audible on us, you can go to the datadrivenbook.com.
I just tested the link. That's why I was looking over here for anyone watching
the video. And it works. Sometimes it doesn't. And
we ask, our guests, do you have, do first, do you
listen to audio books? And if so, can you recommend 1? If
you don't listen to audio books, just a a good book.
I do listen to audiobooks. I also podcast, more
frequently recently. I I'm not sure this book is
already on Audible, but, if not, it's going to be
in Audible soon enough. So it's Nexus by Yuval Noah
Harari. It is audible. I have it in the library already.
Yeah. Amazing. So it speaks about the truth
in the age of generative AI. Right? Interesting.
What's the truth? What's the ground truth? And I was
actually in the lunch party in SoHo, New York, you know when Yuval
was speaking about you know how how technology
and what we see right now is not very different from what we experience
in you know middle age like when when Gothenburg
and printing was was a new thing and like what was
printed actually was you know rumors
and juicy stuff rather than scientific books and this
is where what we see right now in, you know, in chatbots and internet, on
social overall. So it's it's interesting parallels that he's
taking about what's what truth is in generative
AI age where what truth were was, like, 20 years
ago or even, like, 500 years ago. Yeah.
We're the we're the same species with the same problems and the same drama
and the same drivers. Like, it's just our tools have changed, whether
it's a printing press or, you
know, celebrity gossip or whatever or fake news
or anything like that. Plus, I also think the, you know, there's an old phrase
like who watches the watchers. Right? Like Mhmm. Who decides what's
misinformation and who decides what's true? I think. I think
because misinformation could be, you know, there there's
a image of me robbing a bank. Right? Like, you know?
Mhmm. Mhmm. I thought, Frank, I thought when the US
Marshals put you into the witness protection program, they said
we couldn't bring up you robbing a bank any any longer.
Misinformation. You gotta be careful because, like, one of the things I I wanted the
flow was so good. I didn't wanna interrupt it. But, like, one of the things
was I was experimenting with fine tuning an LLM locally.
Mhmm. And I'm basically trained it on information about my blog. My blog's
been around since 1995. Right? Or my site has been around since 1995.
One of them hallucinated this really great origin story for my
website. It was awesome. It was awesome. I'm like, I like that
better. So basically, it said that Always. Always.
It was really good. It was basically that Frank's World started as a
show, a kids TV show in the nineties on
the BBC or channel 4. I forget. Like one
of the big British channels. And it was about a talking
trash can named Frank that would teach kids about the importance
of, recycling. That's my favorite part.
And it was and it was the best part was that it was it was
the first professional project of the guys who did Sean the sheep and Wallace and
Gromit. Yeah. And I'm like so I
I I pinged the guy I worked with. Has this ever been a show?
Because no. Not that I ever heard of. And I looked over it. I couldn't
find it. But and then what I did was as an experiment, I fed
that that whole paragraph that it came up with into
notebook l m. Mhmm. Notebook l m
took that and ran with it. There's, like, a 20
minute audio, and it is the funniest thing because it basically
talks about the early environmental movement. They said it was the Britain's
answer to, Captain Planet. Like, they made up all the
stuff. And now it's documented. So now someone is going
to pulling to pull some information. And if you have Right now it's out there.
Right. And I guess to your point earlier about Lumix, like, if you start
building a crooked foundation, right, like, that eventually as
it moves on, it's gonna so, I mean, who knows, like, couple of years
from now, like, Wikipedia may say, like, there might be a
Wikipedia article about this TV show didn't exist. We're talking about it. We're feeding
the machine. That's fascinating.
Yeah. And it was a so a little bit on the books. I have to
mention it, like, in a couple of sentences. So, in US
a legal entity actually is a citizen. It
has social number. Right. So, technically machines
can create legal entities. They can vote, they can,
you know, they can create information and this information is,
you know, created with social number, with identifiers. So it's actually real
information. It's not fake news. It's created by social number.
And so this is how you create, like, this new truth. Right?
And, and how do you control that? So it's an interesting aspect of what's,
what even is defined as ground truth.
That's true. Everybody needs to define it. I think that's gonna be the question of
the 20 That's a big deal. Mhmm. Yeah. Well,
awesome. It's been great. We wanna be respectful of your time. This has been an
awesome show. Yeah. We'll let Bailey finish the show. And
that's a wrap for today's episode of data driven. A massive
thank you to Ina Tokarev Saleh for joining us and sharing her
fascinating insights into the world of generative AI, semantic
fabrics, and the ever evolving relationship between humans,
data, and decision making. If you're as inspired as we
are, be sure to check out IllumiX and follow INA on LinkedIn for
more thought leadership in the AI space. As always, thank
you, our brilliant listeners, for tuning in. Don't forget
to subscribe, leave a review, and share this episode with your data
loving friends or that one colleague who insists they don't trust
AI. We'll convert them eventually. Until next
time, stay curious, stay caffeinated, and remember,
in a world driven by data there's no such thing as a trivial
question, just fascinating answers waiting to be found. Catch
you next time on Data Driven.