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Learn more at inside analysis dot com, insideanalysis dot com. And now here's
your host, Eric Kavanaugh. Oh yes, folks, welcome to the future.
Indeed, your host Eric Cavanaugh here on the only coast to coast radio
show in the US of ATA. It's all about the information economy. Inside
Analysis doing another one of our pre records for the holiday season, and I'm
really excited to have jig Nash Patel with me. He's with a company called
data Chat. He's at a couple of companies, acquired one by Twitter apparently,
which is pretty impressive. It's done a lot of interesting things, and
of course he's also at Carnegie Millon University. But we're going to talk about
really the trajectory of answers because that's what we all really want. Right We've
done data warehousing, we do data science. All point is to get to
answers that can help us solve business problems. And I get the feeling that
he's managed to find a way to short circuit some of that stuff, but
we'll find out. So jig Nash, Welcome to dm Radio. Thanks so
much for your time today. Tell us data Chats. Where did the idea
come from and what are you working on? Yeah, thanks Eric for having
me on and hello to your audience. A real pleasure to be here.
Data Chats started with an idea for my students and I and that's what we
do in research. We start to look at problems that are a few years
out. So back in twenty seventeen, when data science was really starting to
become popular, we noticed a trend that data science. The way was practiced
then and to a large extent, how it's practiced today, is to provide
the tools and mechanisms for data science programmers to write code from which basically allows
them to do their analysis. And what we realized at that time is often
the way people wrote their data science workflows in the form of what's called as
notebooks, was to fill one cell of the notebook at a time, and
each cell might do a specific task, like I might have gotten a bunch
of data and I want to extract the area code from a column that has
phone numbers information in it. And the way people would do that is they
would go and type in that question saying, how do I extract area code
information from a string that's a pull number, and to google our sack overflow,
find pieces of code, try to put that into the cell, and
then try to adapt that. That's how they would do solve each piece of
the data science puzzle one step at a time, said can we automate that?
Can we do something bigger with it? Yeah? And that's a pretty
interesting observation because what you're looking at is the actual de facto workflow. What
are people doing on these machines. How does that process look and you saw
into the future a bit here with the ability to leverage what we now call
large language models. It's just a variation of artificial intelligence. And as we
say on the show, the lms are really just pre divengines. They're predicting
what text they think you want to get based upon your prompt and based upon
what they were trained on, which by and large was this massive corpus of
text out there in the real world. So you kind of saw I think
into the future a bit, maybe explain that and what you've built. Yeah,
that's great. So what we notice is there's a lot of repetition in
what people tay to do. People are not asking totally random questions all the
time. They're often asking similar types of questions, if not exactly the same.
Similarly, when stackover, for our Google is returning answers back to them
in the form of yes, code, I've seen that, similar it's doing
it based on a retrieval mechanism. And Eric, as you pointed out,
the lms effectively learn a ton of information from everything they've seen, including code
and their synthesis machine that complement what searched us and they'll synthesize new code.
So we started to think about the automation that is potentially possible back in twenty
seventeen by having the human just ask the question and synthesize the code in one
form or the other. And of course at that time LLMS weren't available,
so we were using what by today's standard, or even by standard by the
standard just a year ago, might look very rudimentary, but basically using a
lot of traditional language of technology, passing sentences, trying to understand the grammar,
understanding the sentence, and then trying to produce the code. But with
the limbs, all of that changes in a dramatic way, and all of
that that we were dreaming at that point is now started to become real in
a very big way. Yeah, and we are at a very strange time
right now. Let me just throw out some context for our listeners here,
because you get accustomed to a certain standard or a certain paradigm, and we're
all very much accustomed to the Google paradigm. You Google for something, you
get a result, you go on about your day. Well, Google,
of course indexes the web. Yahoo started indexing the web with hadoop, right,
and the HDFS had doop distributed file system, which was then tried as
an analytic engine by cloud Eira and map R and hoar Works and all these
other folks, and that kind of went sideways after a while because there was
too much reverse engineering that had to be done. But there was a whole
ecosystem that built up around this stuff. So I mentioned this because I'm watching
as Google is straining, and Google has all this pressure from AdWords, and
now when you search for something, you're going to get ten to thirty different
AdWords listings before you get an organic search result, which is great for their
business. They make a lot of money on all that stuff, but it's
skewing what you find, and so it's making it harder to find the stuff
that you wanted. And then of course we've had all this SEO search engine
optimization stuff where people are trying to understand the system, gain the system if
they can. I played around with tactics to do all that stuff. But
my point is that what we have accepted as a standard, which is Google
and how Google retrieves information is really in flux right now. And it's in
part because of the ads, it's in part because the web is a fast
moving entity and lots of things are changing, and it's in part because of
these large language models that are severely disrupting how people go about ascertaining information.
So what do they do? They hallucinate sometimes, right, they make up
stuff. As my buddy Eugene Burke, he's working on a stealth project with
us right now, mentions the problem with the llms that they don't have an
epistemological barrier, meaning they don't know what they don't know, and that's when
they just make stuff up. And I've actually had a thread going with the
go to market guy, Adam Something from open Ai who came out and said,
point blank, look, remember the hallucinations are more of a feature,
not a bug. We've designed these engines to fuse information together and then deliver
generate new content. Okay, So for certain use cases, that's fine.
For a creative copy, for short stories, for articles that don't have to
be really precise in terms of what content is in them, that's fine.
But then you get into the enterprise space where it becomes very important to be
correct and to know exactly what you're doing. And it seems to me that
you with this data chat you are kind of filling in one of the missing
pieces that will allow for this next generation of technologies AI to really come to
fruition and get us to a place where we can trust what it's saying,
just like we've historically trusted what an enterprise data warehouse has told us. But
just if you would comment on where I'm going with that, what do you
think about that, and what do you think about how data chat could be
one of the pieces to rebuild this puzzle. Yeah, and you know,
pick a company your thread. I think of the technologies as two distinct collaborative
technologies. One is search. Search allows you to say I'm going to specify
something and give me a precise retrieval of information from the original database. So
that's Google Search. You see that in warehouses too. When you fire up
a sequel query, you don't get that precise result back. And you can
think of when we go to Google Search, probably what we want most of
the time is search. The second part what lms do is synthesis, which
is different. Synthesis says I'm going to blend together information that I know,
sometimes making stuff up to give you a coherent answer, which may or may
not be based on facts in the database over which I've learned. Search and
synthesis are two separate components, and synthesis is what has now become possible.
Right, so you allude very well to what Google's trying to do and do
search and synthesis and how that blends together in the search paradigm is tbd.
But that's kind of where a lot of the struggle is how do you blend
ELMS with search. We still want search for many aspects, as we might
all potentially agree with it, right, sometimes you just want to search.
Coming back to data chat and how that's connected is what we do is we'll
say point us to a database, a structured database, even a CSB file,
whatever is your data, and then ask a question. We will convert
that into an answer. Now, remember is it totally a little while back,
previously we were trying to figure out how to write the piece of code
that could produce the answer that was twenty seventeen. We've moved well beyond that
in data chat, where what we'll do now is you give us a question
and will produce you an answer. That question might be a search question,
it might be find me this thing in the database, find me the customer
that produced the highest return, highest gross value, and sale. And that's
a retrieval question. There might be a synthesis question or a prediction question that
might say, predict for me, which is the customer that's like to be
the most profitable over the next one or year. To the user, we've
simplified the interface with the UX is just typing in that question. You'll synthesize
that into appropriate code that might be part CQ, part machine learning code that
might be written in Python, bias automatically, part of it. Visualization in
JavaScript will translate across three different languages. The user doesn't see any of that,
but gets an answer now. And now the question is how do I,
as a user take that answer and do something with it. To do
something with it, I have to develop some level of trust. And this
is where the unique aspect of data chat comes into play, where because we
are academics and rooted in academia, we took the science part of data science
seriously from day one. Science requires at least two pillars. One is reproducibility
and the other one is transparency. What data chat does today is it will
give you back your answer, often in the form of a picture, but
along with that, it will give you back a step by step of the
components that went into producing that answer. Think about a cookbook, right,
you look at a recipe and you'd say, I've got something on my table.
If you're given a recipe for that, you now have an idea,
a very precise way as to how that was produced. If that recipe is
correct to the food you see on your table, that is transparent and reproducible.
The same philosophy in data chat. We won't give you back code.
We've invented a new language called Guided English Language, which is in English,
but it's a subset of English. And you can get into that whole theory
next if we need to. There's a branch of linguistics that says, if
I want to communicate with you precisely in natural language English, for example,
then if I just use grammar in the full richness of the language, there's
a high chance that I may say something that may be misinterpreted because language is
not precise. It's not wrong, sure, but there are ways of doing
taking portions of that language, reducing its structure so that you get precision in
that and be able to communicate and that's what we've done in data chat,
where you'll get back a step by step recipe in English, so it might
say I did X at this point, then why, And just like a
recipe that you see in a cookbook which uses very few verbs to be precise
and to avoid this miscommunication. For example, recipes will nearly always say saute
for that action. They won't do different synonyms of that, and the sentence
structure will be very simple on purpose by designed to get that precision. That's
exactly what we do in data chat. We'll give you back the step by
step recipe in this language uses all these properties of being precise and allows a
user to induitively understand what happened at each of those steps. We'll go further.
You can treat that step by step recipe itself as a program. Right,
I gave that to you. That's transparency. Second part was reproducibility.
We'll give that step by step recipe to you as a program, even though
it doesn't feel like a program. Imagine getting your cookbook in pressing the play
button on step number one to sort a garlic, and that just happens.
And that's what we'll do. We'll take the recipe and allow you to play
step by step see how it's been cooked, so that you can reproduce that.
So we get transparency to make it easy for anyone to understand using the
same paradigm of simple English and recipe. And then you sability by saying you
can actually play it step by step to see how it was cooked to produce
that final answer. Yeah, this is interesting. So what you're talking about
doing is enabling the long coveted conversation with your data. That's the mission.
So you've got a database, you can use SQL to query the database.
That's one way. Then you need to know how to write SQ code unless
you have a tool that does it for you. I mean, I'm old
enough to remember a company called Progress Software, it's still around these days.
It had a tool called Progress Easy Ask. This is like sixteen years ago
or something. And what they would do is very interesting is you would type
in a natural language query and it would create the syntax of SQL for you
so you can see what it was doing. You're doing something similar to that
where you're using a natural language query and then you're underneath the covers using a
variety of different things. This guided English language for example, to spin up
bits of code to solve for that particular question. I'm going down this road
because it's it's very interesting. It's similar to what we're seeing with the large
language models and enterprise versions of them. And here's my personal theory what we're
going to see here, Every serious company of any significant size will have their
own AI model. They're going to pick their poison. They're going to choose
Google or Microsoft with open AI, or perhaps Anthropic or someone else is going
to come along and do AI models, and then they're going to start to
train them. And what I've learned in this recent journey of trying to research
this stuff is you can either embed data into the model, which is probably
not the best idea, or you embedded into a vector database and then use
that as your anchors of truth. Is how someone described it to me.
And that's sort of enabling in this, of course concept of rag of regenerative
like augmented what is it retrieval augmented generation right where you're trying to it's like,
Okay, I got an idea what I want. I want to go
to the world and on them see what it is. They'm going to be
all right, make sure that is what I really wanted based upon my priorities
or my data which is in my probably vector database or something like that.
Long way of saying, what I'm really interested to see how this How what
happens here is that these llms or even this technology, you've got data chat
if curated properly with the right embeddings, the right embedding strategy, the right
rag strategy. Why do I need to do all that heavy lifting on the
data warehousing front anymore? Isn't this gonna get to the answers for me?
Well, the answer right now is maybe not. We don't know because it
still has these accuracy problems. But if we solve those accuracy problems, that's
a serious deal. And it seems to me that you are embarking on a
very similar journey in allowing for this interaction with the data, and you've even
got a dynamic app building component to it as well. But what do you
think about all that? Yeah? I think this is these are there's a
whole bunch of questions in there. Let's break it apart. First, is
we've been looking for this holy grail of how do we get programmers not to
be the gatekeepers to getting answers from data is one example. Microsoft used to
have something called Microsoft English that they disbanded. Problem with all of these tools
is twofold. One is they never quite worked even in a single task,
say generating sequel. The bigger problem, however, is today in the modern
world post data sciences creation, which is like now were about a decade into
this data science thing. Data science is a lot more than writing sequel.
Squel gives you a way of getting a report out from the data, but
data science then requires and you could use SQL for feature engineering portion, but
to really get deeper insights, you have to feed it into machine learning models,
which is a different language. You know you're going to do that in
Python and stuff like that. Then to visualize that you're probably going to put
that into a different language too. So we've never really solved the data science
in an automated fashion, which is what data chat does. As this said,
on behalf of the users that same recipe. Part of that might have
been sequel, Part of that might be some Python code that we wrote.
Is JavaScript. We go across three languages and you don't have to know either
of those languages. The other big aspect is who is the user progress and
all of these other tools. Even today, a large amount of effort is
being spent by companies like data Breaks and Snowflake and everyone else to write seql
code. But the user then is a programmer, right right. We'll tell
you what. Let's pick this up after the break. We got a break
coming up here, folks. Don't touch that down. You were listening to
Inside Analysis. Welcome back to Inside Analysis. Here's your host, Eric Tavanaugh.
All right, folks, but having a fascinating conversation with jig Nesh Patel
with data chat. He's done many other things too. And I'm getting all
excited because you know, I've watched the world of data warehousing, I've watched
the world of data science. I've been amazed that those are a very different
world. These people often don't even talk to each other, which makes exactly
zero sense to me. But I think that the economic pressures bearing down on
us right now, plus the power of these new AI models is going to
force is going to catalyze, basically, and it's force organizations to get a
lot smarter about what they're doing and how they're doing it, and paying attention
to the value that they get from all this stuff because what's happening. And
I have an old buddy, Dave Wells. He was the education director at
the Data Warehousing Institute years ago, and he used to always refer to something
he called under the hood technology, and his point was that you don't have
to know how your car engine is working. To know that it works and
to have faith in it and to drive it to work in drive it places.
You don't have to know all that stuff. The same is becoming true
in this data space. And it seems to me, Jignest that what you're
doing is really pushing that all forward because you're saying, look, we're to
handle the assembly of code where you're going to choose which language is necessary for
this particular use case, and just take care of that stuff for you so
you get the result that you want. Because all anyone wants are the answers.
That's the all point, the all point of doing data warehousing, and
the whole point of doing data science is to get to answers to more and
more interesting and compelling questions. And that's really where you're focusing your attention.
So continue where you were. I know why, I'll cut you off dealing
the last segment. Go ahead. Oh that's great. You exactly got it.
Is the whole point of collecting data is to get value from it.
You get value from it when the data actress in your organization, including an
especially the non programmers who can make business decisions, can directly ask the questions
in a way that is trust that they can trust. And that's really what
data chat does right. And there's a whole bunch of technologies that now makes
it possible. But so far, even when we've gone into warehousing, we've
said, okay, we'll all this money into getting an enterprise data into a
warehouse. Then what we'll do is to allow every data actor to get access
on it. We'll put a dashboard up for you, or we'll tell you,
hey, why don't you become a sequel programmer. Oh, by the
way, if you're a SEQL programmer, good luck, you can do data
science. You also have to become a Python programmer. And oh by the
way, if you're a Python program if you want to do some visualization stuff,
you have to become a programmer and some visual language maybe Tableau, maybe
JavaScript or something else. So it's become impossible for data actors that are not
necessarily programmers to do that. And that's kind of what we are trying to
do with data chat is saying, forget about the language will derive code for
you in different languages. We just focus on the question and now that the
programming language is English, and it's just not English to seqel, it's English
to plus Python plus JavaScript for us really more powerful than what you hear at
the industry talk about saying will generate sequel for you. That's just a small
part of the puzzle for the data science. We've lost all of these three
and allow anyone in your organization, not just programmers, to really directly derive
the value of data without saying I need to find a ticket with the Tableau
team to create a dashboard for me to use it right right, right,
right right. So, and the other interesting thing here is to your point,
a SEQL query is going to access bits and pieces of data that are
important for telling your story. But they're just component parts. They are not
the mosaic, they are not the picture of what's really happening. It's a
part of what's happening. But you know, I even joke about this on
the show that you know, data warehousing was born forty odd years ago.
When processors were slow, pipes were thin, storage was expensive. All of
those factors have completely changed. Now processors are incredibly fast, we can parallelize
them, the pipes are fat, storage is cheap, so all those dynamics
are inverted quite frankly. And what we did back then is we had to
strip out all this context just to get the stuff through the thin little lines
into a model that we can then analyze an understanding query, like an O
lap cube for example. And then you have to reconstruct all that context,
either in your report about it or a dashboard, or in some way you
describe the situation to your board or your stakeholders or whatever. The point is,
we had to strip out all this context, and it seems to me
that now you really don't have to do that as much. And what you're
enabling is this conversation with a data set. Now, I'm curious to know
what kinds of data sets can you tackle. Can I connect you to an
ERP for example, or a clickstream analysis system? What can I connect data
chat too? Specifically? Yeah, so data chat works with tabular data,
so you can connect us to a warehouse, you can connect it to CSV
files, s park files, so that sort. And sometimes people might take
data that might come from ritual data sets like images, convert that into features
and that becomes tabular data that you could point to and build models on.
So have you focused on the tabular data at the point in a journey.
At some point in the future, that may change, but that's the big
portion of the market that we are addressing right now. Yeah, that's that's
interesting, that's good. And then just to help our audience understand these large
language models, they use vectors, so you get embeddings and they vectorize,
which basically means they convert text to numeric values. And it's my understanding the
reason you do that is because it's very easy to use existing technologies to compare
and contrast and find similar numeric values across this expanse of embeddings, and that's
how they kind of operate. So your approach is that we're to focus on
the tabular data because it's very well known, it's very common. Everyone understands
Excel and spreadsheets and all that kind of stuff. So you're really tackling what
is a major component of the data world, right Yeah, And that's what
the business analyst. Ultimately you're going to need to connect that to some tabula
data on which you make decisions. Right, more than ninety percent of decisions,
believe it or not, even today get made in Excel. Yeah.
When people can't make a decision and Excel because the data is too large or
something else, they'll start to make it fit within Excel. And so,
in spite of decades of us working on data warehouses, faster, sequel,
paralyzation, all of that stuff, sadly a lot of decision making still doesn't
happen with all of these tools because ultimately, the decision makers are not necessarily
programmers, and they are not necessarily even if you point them to a data
breaks or a snow if they can give them an access to the database,
what are they going to do? Systems are complex, right, And that
is the challenge. It's like the bottomneck to making fast, agile decision in
a correct fashion based on the truth and the data. Today is not how
fast your database engine goes, it's how fast the human goes, right,
And it's all about human empowerment in a safe manner that allows you to move
forward faster. Yeah, that's interesting, and so let's talk about again,
someone uses your technology, they use an English language question of a particular data
set, and under the covers. What you're then doing is parsing the meaning
of the words of the syntax, then mapping that to the data, and
then using a combination of languages. And this is very interesting to generate your
visual answer. So it'll do SQL, but it'll also do Python on some
other various languages too, like JavaScript or something. And what's interesting there is
that I try to explain to folks, languages and programming are a lot like
languages in human languages, meaning they have different syntax. They are not just
a different set of words for the same set of objects. It's really a
different way of viewing the world. And they each have their own strengths and
weaknesses. You know, SQL is very good at getting discrete bits of data
from a database, but Python is very good at weaving things together. I
remember learning about ten years ago that in the risk management world, where I
spent many years doing webinars for the Global Association of Risk Professionals. In risk
management, Python is very popular because the risk managers have five, seven,
twelve different systems that they're supposed to be monitoring well, the signal from any
given one of them might be hard to understand or what it might be such
that it's difficult to know and there's a problem. But when you aggregate them,
when you look at the big picture, that's when you can start understanding,
oh, wait a minute, if this is going up and that's going
down, we've got a problem. And so Python is very good at that
capacity to kind of weave stuff together. Is that one reason why you chose
to use these different languages under the hood to be able to tackle just about
any natural language query you come across. Yeah, great question, going back,
and this is a perfect way to connect to the earlier half of we
talked about sequels. Very good at retrieval stuff and putting together reports from that
retrieved stuff. That's what it excels at. It's good at ETL where you're
transforming the data a very structured way. Python which is a whole ecosystem,
and the machine learning part of that is what's super exciting, including in the
example you mentioned, it can build models on the data. The models can
then explain patterns in the data. It can synthesize beyond that nlm M and
that is a model. So you need data, you need to be able
to extract it and search it, but you also need to synthesize models.
And that's NOTQL. Sql can't build models. They can't build machine learning models.
It can barely build a regression model. Forget about the more complex models
that you need to build. You say, what's interesting in my data?
Right? Right? It can do predictions. So all of that comes from
modeling data plus model has been like oil and water. And that's why people
have to learn both SQL and Python vs say you have to learn neither.
Let's take care of all of that stuff for you and you and you will
reveal what the recipe was. Right. That's the transparency side. And this,
you know, one of the big challenges that the lms are going to
have in terms of getting enterprise penetration for very serious decision makee is the lack
of transparency. And if you don't have transparency, then you don't know how
it got the answer. And if you don't know how I got the answer,
you know and you got the right answer. It's a very difficult thing
to do. And I remember watching as the data bricks folks were demonstrating how
they were curating these models and just thinking to myself, man, you know,
we always say, don't boil the ocean. So if you're going to
go out there and try to like throw all your data into a large language
model and they, oh, we'll just curate it, will you, and
it'll be done by the end of the thirty third century or something, it's
going to take forever to do that. So you need bite sized chunks to
be able to handle. And that's why folks are talking about frequently asked questions
or customer service for example at chatbots. There are some really good use cases
for these kinds of technologies, but they are discreet and they are not the
sort of bigger, broader, what's really happening in my business kinds of questions.
And that's what you get when you synthesize, to your point, the
sequel the data that pulled out, but the model that I can build on
top of the data, and just to explain to our audience, maybe models
they're also recipes, right, they're like, okay, look for this,
once you find this, do that, once you see this, do this,
once you see that, do that, and it's sort of a process
then shows you what the world looks like Okay, it's not really what I
wanted to try. Again, you train the model, you play around with
the model. But that's kind of what the models are, right. There
are complex recipes for how to interpret data and then generate an answer. Right,
yeah, perfect. And it's even more exciting now because it's imagine I'm
working on a problem and maybe it is predicting sales. Now, maybe that
we say there's a model for it. Right. It might be a regression
model, or it might be some of the more complex model. But that's
not enough. What you really want is to say, on my data,
which probably looks different than someone else's data trying to solve the same problem,
Right, how do I build a model on the fly that fits to my
data to answer the question? And that's what we do. We'll build a
model on your data on the fly, use your specifically created model for you
to answer the question that is really relevant to you. So interesting that requires
all kinds of rocket science, data science, see wizardry to build models.
We're saying, don't worry about it, we got that covered for you.
Wow, data models for on your data on the fly, and explain that
to you intros you can understand and that's that's the magic, and that's that's
it's all centered around human productivity is the goal. You know, I'm gonna
throw a bit of a curve ball question at you and folks, Jig did
not get any of these questions in advanced just so you know, we don't.
We don't roll that way. But is there some calculus or some math
underneath all of this? The reason I asked that is I remember from calculus
class. The key is that you had to learn to identify the pattern of
the problem and then know which formula to apply, and then of course you
have to do the work to apply it and to see if it reasons out.
But there are X number of formula basic formula that you're using calculus to
unravel problems, and it sounds to me like there's something like that underneath the
hood with your technology. Is that correct? Absolutely? And this is well
known in literature. So we try a bunch of different models, and the
number of models you can try and your data is not just like what model
classes like what it sets of equations to try, but also parameters for each
one in that space is extremely large. All kinds of intelligence built into a
platform that will look on the fly at your data, look at what you're
trying to do, and say, you know, I don't mean to try
the billion options because to your point, that will come back in the twenty
fourth century, right if we just we will then use intelligence built into the
platform to understand the context from the data from your questions, to build a
small number of models, not just one small number of models on the fly,
raise them against each other to see which one there's better an answering your
question, and then give you the answer from that, and all of that
will be fully automate for you. Yeah, So that reminds me of what
data robot would doing a few years ago before they ran into their very strange
problems. But what I loved about data robots approach was this auto mL.
So you would load your data in like let's say it's mortgage loan data,
and you want to just see a regression mode you understand, okay, where
did loans go bad versus where did loans stay good? And what they would
do is automatically run that data against like seven or eight different algorithms and figure
out what one is best. So you're doing the same thing that they're doing
in that sense, but you're actually dynamically spinning these things up because again,
when you consider the permutations, when you consider all the different ways you can
get covariance between functions, right, there are so many possible answers. So
if you can automate that early process and get to a handful of models,
that's a tremendous advance in being able to get to that specific model that's going
to answer your question, right, Yeah, absolutely, And you bring up
an extra excellent point. Industry has been focused on very small things. So
data robot was trying to solve part of the data science problem, but still
targeted towards largely programmers, but not SEQL, not visualization, not that.
And really the point is you want to do all of that and do that
in a way that is accessible to everyone. Yeah, you know, we're
going to pick up after the break. This is a fantastic conversation. Don't
touch that doubt you are listening to Inside Analysis. Welcome back to Inside Analysis.
Here's your host, Eric Tabanaugh. All right, folks, back here
Inside Analysis. What's fun is we're moving through the whole evolution of data management
and data warehousing into data science and AI and capturing all this stuff, and
I promise you folks, it's all turning around. Right now. We are
at the ultimate inflection point in this industry, which is very exciting. It's
this is very very good news because all the plumbing and the hammering away.
Let me tell you, it's not exciting, okay. Like the poor data
engineers who are building these pipelines and like waiting for the data to get there,
they're pulling their hair out if they have any hair left. It's a
very difficult job to do. But a lot of that is going to be
solved with this new era of tools and technologies. It I'll bring jig nsh
Ptel back in from data chat. You know that I completely understand the value
the synergy. In fact, there's a German concept of gestalt, which says
that the sum is greater than the whole of its parts. And when you
get these things together, really cool stuff can happen. I'll just throw one
last thought at you. I came up with this idea a couple of years
ago when folks were saying, oh, it's AI versus data warehousing, Like,
it's not AI versus data warehousing, and a way, data warehousing is
like your left eye and AI is like your right eye. And when you
get those two, then you get depth perception because if you only have one
eye, you don't have depth perception because you have no way to triangulate where
things are right. And so what Jignesh has been working on here with data
chat really, I think is the next step in that process, and it
really is that synthesizing component that will take your natural language query, figure out
what the data is telling you, then build a series of models that they
compete with each other to figure out the answer, and then you get the
answer and you open the commander to show them the whole process. Right,
go ahead, Yeah, and that's exactly right, you know, coming back
to the different the journey that the industry has gone through. First of it's
all about can we even afford to keep all of this data together and make
it That was the data warehousing the cloud are doupe world that then trusted it
into big data stuff, which is all about moving that often to the cloud
so that you didn't you could make that economies of scale even better. And
then came the data science world, which is about saying, hey, sqel's
pretty dumb, it doesn't It only knows data, not models. It's two
models and all this really clugy gluewar and companies that just did one versus the
other? And yet now and that's all twenty ten right solution, and as
we look forward, it's about neither of those. These are all just foundational
blocks on top of which the goal is to enable the humans to make decisions.
Whether it is using data warehousing, sequel technology or data science technology.
No one cares. What you care about is did you get the answer that
could move your business in a different direction That would have been very hot for
you to determine without the data being present. So of course you need the
warehouse, but you don't necessarily need to deploy an army of programmers now that
has to do an el pipeline and a melt uning pipeline and then presentation stuff.
It's like, can you bring it up to the front where the humans
are in charge and the humans don't have to delegate to other humans to get
even simple stuff done. It's the true empowerment of the data actors, the
human data actors in an organization is to free them from having to go to
school for six, eight, ten years to become programmers, because that's just
a mean to an end. The end is to get insights. Yeah.
No, that's exactly right. And it's almost like you're delivering what a lot
of people back in the day thought Tableau would, right. I remember the
first I actually saw. I think I saw the first ever public demo of
Tableaux because I worked at the Data Warehousing Institute and five and there was a
conference in Seattle where they were just part of one event after the show,
like one of these parties that you have and give way free beer. And
I watched, and I saw the first ever. I was the first person
in the room because I worked there, and I watched, I was like,
oh, that's going to be good, you know, because no one
has done this yet. That was just one tiny little piece of the puzzle.
But when you can synthesize all these various engines to generate again what we
want, which is the answer, right, And it's like there is such
a tremendous opportunity cost to doing things the old fashioned way because, as you
suggest, if you have to ask for permission from this person to do this,
wait for that person to do that, Oh, that's going to be
a week. He's on vacation, like all of these pauses just rush creativity,
crush innovation, and just kill the spirit. Whereas if I can just
answer or get a question answered in this sort of visual format and then understand
how it got there, now you're teasing the brain. You're getting the creativity
rolling, you're getting those juices flowing in your mind. That's when good things
happen. That's when you figure things out and go, oh my goodness,
this is I know what we have to do. Now. It's not this
product, it's that product. Okay, Bob, let's go do X y
Z. That's the so called aha moment that only happens if you're in this
fluid conversation with the data. And that's what you're enabling right exactly, and
the whole vision behind data chat. Take those words data chat first. That
chat portion always meant had two meanings to it. One is, I,
as a human, should be able to talk to my data and get trusted
answers that I can verify and reproduce. But the other part of that is,
Eric, I could chat with you about an insight that I've found,
and this is a human to human chat that it enables give you that visualization
that came to along with the recipe. And now, because we have a
common grounds for saying how did I arrive at this insight in a fact driven
way, you have a better human to human conversation. And you might say,
you know what, step number three in this recipe looked at all the
data. Oh, but you know what we should take away twenty twenty and
twenty one because there were aberrations and let's go read this stuff. So let
me go change that step in the recipe. So it's just as powerful in
terms of what we do is enabling an individual to talk to the data directly
without any intermediateary. But it's also about what we as humans can do because
now we are coming at it in a factual, data driven fashion, in
a truly data driven, factual fashion. Right, we are not guessing what
are we talking about? You can look at a Tableau dashboard and what does
it mean? You're just gonna assume it. This means what it means?
Can you interrogate it? Can you say did it miss a step? No?
But that human to human chat portion is a big part of a vision,
and it all connects if you have this way of explaining how you came
to your answer in the first reproducible way. It enables both these modalities of
communication, and that's what is super exciting, right And you know, I'll
end with a bit of an analogy here. So online and social media and
via email, people get to these big fights. It appens all the times.
Where all this hate is online is because people are kind of going at
each other. But if you just sit down and talk to a person,
you'll always be amazed at how much you actually agree on. And a lot
of that friction results from misunderstanding, misinterpretation, and really from superimposing your beliefs
onto someone else when you don't really know what they think. You just know
what they say or what they've posted on a chat, and you can't read
sarcasm in chats and things of this nature. It's hard to So these are
very serious problems in communication that as solved if you just talk to someone and
what you're doing is enabling kind of conversation with the data, but then also
with your counterparts and the organization, so you can collaborate on working through what
we just saw here. That's a whole different story from debating whether or not
it's true. And then Okay, next month's meeting, we'll try to figure
it out again. Well, you just lost another month, and we can't
afford to lose months these days, folks. I'm telling you like that the
pressures from the economy, from global forces, from artificial intelligence, all this
stuff is really bearing down and forcing change, forcing companies to get real about
what they're doing. And I think you've got a pretty powerful technology here to
enable those data driven, data focused conversations where you have the transparency, you
have the recipe so you can see under the hood what it was doing.
That's very powerful. What do you think? Yeah, absolutely, And I
think it's the confluence of all these technologies that are coming together. We have
the processing that was simply not available ten years ago to run any of these
modeling things in an economical fashion. And I know that some of these things
need. We've got the data because we've been collecting it now for twenty years.
Nobody throws up a data and that's awesome. And now we've got the
business drive like every CEO has heard of LLLM. Every CEO is investing in
that as one of the top priorities in the company. So the opportunity exists
so super exciting time for all of us who are in this data world,
which is taking different forms databases, big data. Now everything is AI.
But at the end of the day, data plus algorithms plus processing power that
becomes available the synthesis of that is what we are living and it's fascinating.
Well, and you know, we've got about one minute left here. The
other interesting thing is I want more and more business users, including myself,
to better understand the different algorithms in what they do, Like a K means,
for example, what exactly does that do? I think the more business
people can wrap their heads around how those things operate, the easier they'll be
able to map their own thoughts to their own world of data. What do
you think? Real quick? Yeah, I agree. I think it's going
back to your car example is that you don't need to be to know how
to build an engine or fix an engine to understand a car to drive a
car, and that's totally fine. But if you're a race car driver,
even though you're not fixed engines, you probably need to at des understand a
little bit of how the engine works because guess what, you'll get a little
bit more out of that now, a lot of automation is making that easier.
So I think there's always value in learning something even though you're not going
to be using that in a direct fashion, because it allows you to appreciate
at a deeper level. So fan of learning things absolutely, Yeah, well,
folks, that's what we're doing. That was a great quote I heard
from the CTO of Boom Media the day. I asked him, you know
what's the most important thing in this era of AI, and he said learning
and he meant human learning, Like we need to learn about these things and
what they do and how they operate, and be very crystal clear about what
we're absorbing in terms of information and insight from these systems. And folks with
a fantastic conversation with jig Nash Patel of data chatook them up data chat dot
AI. We'll talk to you next time. You've been listening to Inside Analysis.
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as a bizarre anomaly. We profess to be an electoral democracy, yet we
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PACs trying to put Republicans in Congress got about half of their one hundred and
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corporate front that backs gop CAN candidates, Club for Growth, got nearly thirty
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say DNA from the Bureau of Economic Geology. This is Earth Day. When
did our ancestors leave the trees and begin to lead a life on solid ground.
Some real life CSI has given us a big clue. Lucy, the
three million year old skeleton of one of our oldest known human relatives, was
recently on a museum tour of the United States. During her visit, the
University of Texas, scientists examined her skeleton with geological CT scanners, similar to
medical cat scans, but with higher resolution. Fossil bones often break as they're
buried, but Lucy's upper arm showed something unusual. It was compressed with sharp
fracture lines and tiny bone fragments intact. The team called in an orthopedic surgeon,
who confirmed that this injury in modern human only occurs when they extend their
arms to try to break a fall from considerable height. So they began to
look for other fracture evidence of a fall and found it in her ankle,
knee, pelvis, and ribs. They then look to modern chimpanzees, which
are about the same size as Lucy. Chimps nest in trees at heights of
up to thirty five feet high, enough that a fall could result in the
same type and degree of fracturing. Found in Lucy. Further studies using the
ct data showed that her ratio of arm strength the leg strength was more like
chimps than humans. Both finding suggests that three million years ago, our ancestors
may have lived a significant part of their lives in trees. I'm Scott Tinker
and this Spaniard date with Earth. Earth Date is produced by the Bureau of
Economic Geology at the University of Texas at Austin. Earth Date is researched by
Julie Hennings, written by Harry Lynch, and distributed by Mark Blunt and Casey
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I'm Chris Gragio. Former President Trump now says he won't testify in his
fraud trial in New York tomorrow. He posted his decision on truth Social today,
adding that he's already previously testified. Trump was set to make his second
appearance on the witness stand to be questioned by his own attorney, says the
final witness for the defense. The office of New York Attorney General Letitia James,
filed the two hundred and fifty million dollar lawsuit against the Trump family and
the Trump Organization for allegedly inflating financial statements by billions of dollars in an effort
to receive more favorable loans. Secretary of State Anthony Blincoln believes Israel does not
want to harm Palestinian civilians, but also should do more to avoid civilian casualties
and allow humanitarian aid. Speaking on CNN stead to the Union, Blincoln said
Israel needs to close the gap between intent and results.