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Inside analysis dot com. And now here's your host, Eric Kavanaugh.
All right, ladies and gentlemen, Hello and welcome back once again the only
coast to coast show all about the information economy. It's called Inside Analysis,
or is truly Eric Kavanaugh Here with one of my best friends in the business,
a guy who's been around we're a couple of years now and has made
his way into all sorts of different data scenarios, data products, data concepts,
data models, data designs. Mister Kent Graziano, I asked him before
we started, does that mean? Does that mean thank you very much in
Italian because of course gratimile, but it means grace or beloved or deer,
so kent deer gratiano. How was going out there, buddy? Good?
How are you doing rich? I'm doing good. So yeah, folks,
I had this idea to do a series called profiles in data, because it's
all about the people when you get right down to it. I mean,
of course the data is important, and the tools and the technologies and the
methods and how to do all that stuff. But at the end of the
day, even in this era of AI, it's still about the people.
And it's always going to be about the people. I mean, the jobs
are going to change. I think we're going to go through a very disruptive
period. It's already happening from what I've seen. It's it's already taking place.
There are lots of reasons for it, but the one thing you can
count on is the wisdom of sages, and so Kan't. I'm going to
throw you in that category as a data sage. You've been around a long
time, You've seen the ups and downs, and we're in a very different
world right now. You know, I'm going to throw kind of a curb.
I'll let you to start. I feel like these large language models represent
a very serious inflection point in the history of business, quite frankly, but
primarily of the data world. And i'll explain why. So in the very
earliest days of DM radio, I remember wrapping my head around ETL, all
this ETL moving around and around and around and around, all these batch windows
being hit, and I thought to myself, this is crazy, like that.
You're probably moving data that doesn't get used. You're probably moving the same
data multiple times. You're probably sometimes overwriting good data with bad I mean,
and there just must be so many things happening here on the data front.
But business just demands things. The business demands data, It demands execution,
it demands performance. Business people say we got to get our data in they're
okay, fine, let's move it in there. Oh we didn't get the
schema right, and then things changed, right like with snowflake, and you
know a lot about Snowflake. Maybe we'll start there. One thing that I
thought they really solved and really figured out was that schemas change because the world
changes. So you want to be able to change the schema of your warehouse.
Well, in the pre Snowflake days, that was a huge problem.
I mean, you know, you'd be asking for lots of trouble, a
lot of money, a lot of very unhappy engineers, like lots of bad
things would happen, and Snowflake's like, now, let's make it easy to
pull this thing down, change the schema in the spin it back up again.
It was it your take as well. That was a very big change
in the in the industry. Yeah, I mean the I mean Snowflake was
that you had to use the standard phrase a game changer, certainly in the
data warehouse world because of a number of the features that been voluntary architected into
the platform right and you know what you're talking about there, which made a
huge difference and still obviously does to a lot of people was their zero copy
clone. The ability to take an existing schema and with one command clone the
entire thing and basically make a complete replica of it with no additional storage costs.
That was the thing, because that was something I couldn't do in the
data warehousing world if I wanted to have a dev instance that had the same
amount of data as my PROD instance. I never ever in my career was
able to get that because the cost was too much and the time, you
know, once you started getting into terabytes of data to replicate that on storage
with the systems at the time, that could take days, weeks sometimes depending
on the system, and you really couldn't get which meant you couldn't actually innovate
with confidence that you weren't going to break something right right. You couldn't test
it. It's like, oh, the query works great. Yeah, well
you're got one tenth of the data in your QA instance as you do in
production. And then it goes to production and the users are screaming, going,
I can't live with this, this is this is too slow. And
now you're doing, you know again, additional iterations and refactoring to figure out
how can I tune it to make it better, how can I make it
faster? And you know, the users are already mad at you because you
gave them, you told them you had what they wanted, and they're like,
well, this is unusable, it's too slow. And the zero copy
cloning thing that was like, wow, this this is awesome. This means
I can actually not shoot myself in the foot before I move something into production
and have to do these cycles again, we could do real user acceptance testing
without this exorbitant cost or you know, waiting weeks for the infrastructure team to
put more disc online and for the DVAS to you know, restore something from
backup, assuming the backup tape was actually good, right, you know that
they could do all of that, So that that that really did change a
lot for a lot of companies out there. Then, and of course the
whole separation to compute from storage that allowed you to scale the storage if you
needed without scaling the compute, and scale the compute without scaling the storage just
made it so flexible. It just changed. It changed so many things.
Then you throw in there the variant data type that allowed you to load Jason.
So you talk about schema changes. You know, we actually got real
schema on read load in a Jason document into a table in relational database with
the table access it was SQL. And you know, as your data pipeline
is running and you got new data coming in, if there's a little change
in the scheme, it didn't break anything. I mean, I'm sure you
remember the days that you were talking about ETL. It's like the source system
changed. They dropped one column right, and didn't tell anybody because they're thinking
about their operational system, they're not talking about the data warehouse. And then
your informatica job blows up in the middle of the night and now everybody's screaming
the next morning because the data hasn't been refreshed. You told us that'd be
refreshed overnight and it's not been refreshed. What happened, Eh, you changed
the source. They changed the source system. Didn't tell us. Now we
got to go back and re engineer the ETL and change the tables and the
database, and you know, so much of that went away in the last
Well see, Snowflake was founded in twenty twelve, so last ten years.
I started with them in twenty fifteen, so it's been less than a decade,
and you know, they solved those really big problems that we had in
data warehousing for multiple decades. Well, I mean you kind of hinted that
the big downside of the old way, which is basically an opportunity costs around
innovation because it was fragile, because it was difficult to work with, because
changes were very hard, people didn't try to make changes. I mean,
it's like a kid who keeps getting reprimanded for trying new things. They just
stopped like, fine, I won't do anything right. So the psychology of
data use really took a whole new turn, in large part thanks to Snowflake.
Now, of course we have data bricks, we have a whole bunch
of other options of ways to do things. But the point is that now
there is more confidence to move forward to try changes to do things because you're
not so worried about bringing the whole thing to a crashing halt. Right right,
You're not creating instant technical debt by doing something right, and so you
can take a more agile approach. You can be more responsive to the business
and the business requirements when they want to change, with like yeah, without
having to worry that, oh yeah, we're gonna break it. And then
the tools have evolved so much too, between the data catalogs and the data
engineering tools. You know, the thing that we always needed was really good
ima analysis reports. But to get the impact analysis reports, you needed something
tracking the metadata around data lineage. You had to know where did that data
come from and where did it go? We pulled a data element in from
one source to a system, how many places did it go And if we
changed that, how many things is it going to impact? Well, you've
got to have the lineage to do the impact analysis. And now we've got
a lot more tools in er what we call our modern data stack that allow
us to do that so much easier, so much quicker. You're no longer
relying on a couple of data engineers to know the code inside and out.
And go, oh, yeah, that's right. I remember over in that
routine we did this, and we did this based on the user requirements.
But then they had us do this in a different routine and yeah, if
we touch that, we're going to break three different things. You don't have
to do that anymore. You can find it through the metadata. Yeah.
Well, and you hinted it something else tonight. Throughout the largest language models
to start us off here, and you think about the impact of these engines
now, I think there were numerous other shoes left to drop in this equation,
just one way of putting it, basically, because they are very powerful.
But you know, Sam Altman had that very curious comment he made a
couple of months ago, say, oh, the era of large language models
is over. Everyone was like, what, what are you talking about I
think, No, I don't know. This is just my personal theory.
But we've now seen that they've had some challenges people, And this is research
that I've done and people have talked to have said, there are things that
it did very well three months ago, four months ago that now has a
hard time doing. A good buddy of mine, who actually almost worked with
open Ai, said that he looked at their models three years ago or so
and said, you're gonna have all kinds of problems because you're not respecting entropy.
He said, you're gonna have short term memory problems, long term memory
problems. Basically, he blew it up at them, and they just said,
all right, we'll get out of here. We're going to do it
our way. We'll deal with that later. Yeah, we'll do exactly,
We'll figure that out later. And of course they rolled out and huge.
I mean, you know, the friends I have at Google, I can
tell they are stressed out. The company is very stressed out right now,
which is kind of hard to believe because it's Google, Like, I mean,
goodness, you're one of the biggest companies in the world. You change
the world with the stuff that you did, and of course what happened.
Microsoft goes throws a whole bunch of money at open AI, then the opening
eye board curiously kicks him out, and then such Inno Della hires him,
and like, wow, that was one of the biggest tennis matches I've ever
seen. Like what is going back and forth? Point being? And where
I'm kind of going with all this is, even though there are some challenges,
they're so powerful. You can load code and ask the machine to tell
you what the code means. You can load a whole bunch of documents and
ask it to summarize. So I think those are the two better use cases.
But kind of where I'm going is I see these llms, the engines
of them at least, and maybe it's SLM small language models to become more
prominent, really serving as a very powerful component in a workflow. So they're
good at ETL. For example, I scraped a whole bunch of data off
the CEES website because I wanted to get a clean list of companies from that
site. So I scraped a whole I did a just command a command C
command V into a Google doc and then I pasted that in the chat into
Gemini was barred back then, and I said, please give me a clean
list of just the company names and all this text, and there's all this
other text inside. It was like, okay, that just banged it out.
I'm like, all right, that's pretty impressive. You know that it
can take an instruction like that ascertain I only want company names, know what
those are in the context, and then bang out a whole list, And
then I asked that to give me a Twitter handle for each one. It
did that too, but half of them were bad. So it does make
stuff up. But nonetheless, I mean, think about if you'd seen this
blob of text, you know what I'm talking about, just all kinds of
other words in between places and tabs and commas, and is also kind of
crap. I had no problem just stripping all that stuff out. That's a
very powerful function, which would have taken a good ETL developer. I don't
even know how long to sort through and to figure out it would be hard
to do. Not for the llms. So what kind of impact do you
think they're going to have on the data management business? Writ large Well,
I think that what we're seeing is the next step in data democratization right by
wee can now with even what you described, you basically you took the data
engineer out of the loop, right, You were able to do something very
quickly on your own. And I think that's part of the promise and the
power of these things is to allow business analysts now to do things with data
without having to decoders. Right, they don't even necessarily need to know SQL.
In one of the recent demos I saw, I think it's called Cortex
when the new sale featured, right, is like you can you can ask
a question say I want I want the uh, you know, the average
sales over the last three years for my top five regions, and it writes
the sequel for you, right, And you know that's starting need to come
out. And I've seen a couple of demos like that recently from the data
analytics perspective that now again you don't you don't have to know SQL. Uh,
you don't even necessarily have to understand the schema of the database in order
to get some basic analytics done using an ll m U, using a you
know it's prompt. And my son is in school now and he he mentioned
something about one of the careers that they've talked about in the career education stuff
that he's going through at college was a thing called prompt engineering, right that
you could you could take classes in prompt engineering. It's like, well,
what does that mean? That's okay? How to ask the question? So
I think we used to call that critical thinking, right learning how to ask
the question, But now you're going to learn how to ask the question of
an AI basically right, and how you ask the question will have an effect
on the outcome because again, the AI the LM can't read your mind and
it can only interpret so much. So being able to ask very clear,
concise questions using language that the AI recognizes is going to be the skill,
right, That's going to be the skill rather than knowing how to write,
you know, select some count star group by right in order to get it
in order to get the answer, which is so huge because and this is
leading up to maybe segment two. I'm going to give you one of my
big ideas and sear to think about it. But because now you've democratized access
to analytics, is what you've done. Basically, you had to know some
sqel or have a good idea at least how to use the technologies to be
able to build the queries. Now you can just ask it a question.
Now again, it does make things up, so you have to be careful.
You have to vet things. But we've always had to vet things.
I mean, there are times when the data engineering was done wrong. So
it's not like humans didn't make mistakes, and now the AI is creating some
new function called a mistake. No, like, it makes stuff up,
and it has some problems, but the fact that you can sit there and
just have such an interactive discourse with the data, to me is incredibly game
changing. And I think it's going to put even more excitement in the workplace
to use the data because a lot of times people don't use the technologies because
they're hard to use, because they're slow, because they don't trust the data
for example, and we do have the trust issue. But this stuff is
not hard to use. I mean, it can be hard to get really
good at your prompt engineering, but even that, just to ask a different
question, it doesn't get tired, it doesn't get annoyed at you. I
will admit I tried Groc a little bit. I thought Grock was kind of
annoying. That's the one that's in Twitter these days. It's supposed to be
sarcastic and goofy, And I'm like, is there a big market for an
engine that gives me sarcastic answers? I just don't think so. I think,
you know, maybe Elon smoked a couple of dubies before that one came
out, which is fine. I mean, the guy's done amazing things.
He's an engineer himself, and you look at the accomplishments this guy's had.
It's just absolutely shocking. But you do have competition out there. And I
guess what I'm driving at is by democratizing access to systems of record with these
llms, we are fundamentally changing the game about how people interact with data,
how they're able to consume data learn from data. I'm writing an abstract right
now, in fact, for the show I'll do with you on Thursday,
all about this concept of data literacy. And I think that just playing around
with these tools connected to your data sources, you are fostering better data literacy
because you're learning things about the data and you don't have to take a class
necessarily, just have to dedicate the time to hop online, play around,
click a few buttons, type into some questions and see the different answers,
and you have to work with it to see how it operates and to kind
of know sort of the contours of what it does. But folks, don't
touch out that. I'll be right back in a moment. We're talking to
a hero of data. Ken Graziano will be right back. Welcome back to
Inside Analysis. Here's your host and Eric Tavanaugh. All right, folks,
back here on Inside Analysis with Kent Graziano, who has been a data avenger
for years. Quite frankly, you've been out there on the front line.
I remember I met you at the Data Vault conference a number of years ago.
That was fun. That was before the big COVID, And you know,
COVID gave us a lot of time to think about stuff, for sure,
and to sort of reevaluate what was going on. I'm sure there's a
bit of a COVID surge in terms of innovation because people are time to think
about stuff and do cool new things. And with that in mind, I'm
going to throw my big idea over to the sage graz Siattle. I want
to get your thoughts on this, so I keep thinking about how executives use
data and how we've used data historically, and if there are these long processes
of building reports and doing all these different things, I think the llm's kind
of turn all that on its head. And if you do it right,
I think what's going to happen is companies are going to get their own idea,
to get their own private model. Maybe it's a small language model for
a particular industry like legal or healthcare or retail, because they have their own
lexicon, and so it's good to kind of weed out the semantic issues that
you'll find with a large language model. That's kind of the point of the
small language models. So you get that, and then you train, you
get your vector database. You basically take your curated, trusted data and you
start feeding it into the vector database as you're embeddings. These are your anchors
of truth. And what I think is going to happen is this is sort
of the Valhalla, if you will, is that you get enough of your
corporate data in your embeddings. You've got your anchors of truth now, and
then you set up whether it's COFKA topics or some kind of stream of changes
coming through like CDC, basically into the vector database and what's going to happen
is the executives are just going to sit there and have this amazing intern with
tremendous amounts of knowledge at their fingertips. They'll be able to ask any number
of questions, you know, how how are we doing this month? What
can I do to change? Who's really excelling in my sales team? Like
all these kind of questions that you can ask and just get information pouring into
them. And it's going to be very useful because it's connected to your data
warehouse, to your CRM system, to your sales sporce, to your clickstream
analysis, whatever it is that you've got, that senior executive is tapped into
it and can now ask any question he or she wants. How viable is
that? Do you think? And is that the future? Yeah? I
think you've hit on something there, because really this is, you know,
the extension evolution. Maybe it's the the fruition of all the ideas that we
had around data warehousing and decision support systems and advanced analytics and business intelligence in
general. Right that to have that at the fingertips of an executive, that's
that's where the value comes in, Right, is you've got to be able
to use the data to make effective business decisions. And I think that what
you're describing could be the differentiator that allows an organization to get their competitive advantage.
And specifically when they're looking at their internal data right and training a model
on their data, if they do that right, that's the thing that's going
to give them the competitive advantage. Because so many things have been commoditized,
right, and we've got lots of data out there that's being shared via data
marketplaces, and sure, you're probably going to want to pull some of that
in. But it's the combination of that external data with your internal data,
your proprietary data, that's the thing that's got to give you the competitive advantage.
And if you've got like you said this uh basically online on demand uh
executive intern data intern that's putting all this together automatically right through through a small
language model that the executive got a question they just you know, like like
Alexa, Well they'll probably give it. They'll have to give it their own
name and say, hey, uh, I heard from my sales guy that
we're having a problem in the western region. Give me give me a summary
of what happened in sales in the last three days in the West Region and
boom, it's like and eventually it should be able to give us. You
know, well, what's the implication of that, Oh, the trend.
The trend is if the sales, if what's happening in sales in the West
Region continues for X number of more weeks, you're going to lose this much
profit, right, right, These are the kinds of questions that we built
data warehouses to answer. Yes, it's kind of where I'm going with this.
Absolutely, I don't I don't think that data warehouse is going to go
away, but it's to me, it has to get simpler, I think.
And you know, so you look at the marketplace. What happened.
Data Bricks went out and bought Mosaic mL for like one point four billion or
something like that when this was just taking off, and Terra Data and some
of the others were still kind of laughing at the jen Ai stuff, which
I thought was a bit foolish frankly, But those guys were laughing at the
cloud originally too, if you remember, Yeah, well that's wrong, that's
pretty funny. It didn't work either, right, Well, I don't know,
man, I just I keep thinking about the disruptive power of these engines.
And again, yes, they get some stuff wrong. So do the
old systems. The old systems got stuff wrong because their data was wrong or
the model was wrong, and it takes even longer to find it and fix
it in the old systems. Well, that's a different other thing, right,
that's the other thing. So it's like, here's what fascinates me is
trying to figure out how do these models figure out what to choose when they
give you their answer. Now, kindy that got bought by click I took
a demo of their engine. It's pretty interesting because it connects to an LLM
and basically you use it for like frequently asked questions for your manual for some
new product that you bought, like the new iPhone or something, and you
load the whole thing in there. It will automatically give you frequently asked questions
that it suggests and the answers, and you curate that and then when someone
uses it, you'll get the answer you asked. But they'll give you a
drop down that'll show you where it got those bits from, So it'll say
the first sentence came from page three, paragraph two. The other one came
from here. I'm like, now that is compelling because now you're getting no
annotation, right, that's the annotation that we were taught in our language.
Our language classes, right in our English classes are learning how to do research
papers. It's like, don't put anything in there that you can't prove where
it came from, right, And it's and whether there's endnotes or footnotes,
all those annotations are there, and that's yeah, that's part of what we
need. That's I guess the part of the QA on these things is can
we can we see where this data came from rather than it even though it
might be a black box that generated the result, it's still traceable to the
source and say, these are the references, this is where these numbers came
from, This is where this concept came from, this is this is how
we're coming up with this particular recommendation. And I think that's critical. That
that is critical, you know, to differentiate between a hallucination and a good
answer that you actually want to run your business off of. Right. Well,
and that's the thing, right, is that to run your business off
these things, you're going to have to have some certitude about what they're telling
you. So that is a concern, But I just I feel like there's
going to be downward pressure on pricing data warehousing. But at the same time,
you've got so much automation now, it's so much easier to do things
than it was in the old days. I mean, I guess that one
of the biggest hurdles to overcome here is just old fashioned mindsets. What do
you think? Absolutely? Oh yeah, I mean that's the Even when I
started off with Snowflake, the most frequent question or might actually comment I got
when I was out, you know, being the evangelist and talking to people
about the separation compute from storage and zero copy cloning and all the things that
we talked about. And the first thing is like, oh, that'd be
that'd be awesome if it was true. And then they say, okay,
well, well how do we how do we index our queries? It's like
you don't, Well, then this thing can't work. It can't work.
There's no way it can work because you don't have Indexes's like, no,
this is a completely different architecture. And it was getting that that mindset change.
Same thing when we went from Waterfall to agile, right, trying to
think about how do we do a project in iterations, versus spending six months
writing up the requirements document, getting everybody signed off, and then spending two
years building it, or spending a year and a half doing an enterprise data
model and then having to hand it to a DBA to convert it to a
schema in a database, and three years later you've got a data warehouse that
has nothing in it because nobody cares anymore. Right, we had to change
the way people were thinking. I think what we're talking about here, it's
the same thing. It's the you know, we've talked over the last couple
of years about companies about data literacy and data culture, and it really is
the organizational culture and expectations that you've got to find a way to shift those
right and get out of the well the classic and this is this is every
industry. It's not just it, it is every industry. Well, we've
never done it that way before, or the reverse, Well, we've always
done it this way, and I'm comfortable doing it this way, right,
so I want to just keep going this way. I actually, early on
Snowflake, we worked with a large retailer in England and they after their evaluation,
decided to switch from one of the existing big data warehouse companies to Snowflake,
and several DBAs literally resigned and went and found other jobs. And this
is in Europe, where you know, people don't change jobs hardly at all.
Because they didn't want to spend the last five years of their career learning
Snowflake. They wanted to stay with what they were comfortable with, which was
the older system. And they found another company in London that needed a DBA
that had their expertise, and they figured they were just they were going to
go over there and sail off into the sunset, that that was going to
be less stressful for them than to stay where they were and learn this really
exciting new technology that they literally left the organization rather than change the way they
were doing things and thinking about things differently. Yeah, that's not uncommon.
I was. I was floored. I was like, you do they did?
What? Okay, I'm done, I'm out, No, thank you.
That's uh, that's wild, I mean, and that that's I feel
like, that's where we are right now because of these lllms, I mean,
so much is being shaken at the moment, and people stuff to do
their jobs. These have to get stuff done. I mean, I've played
around with these things and I'm really amazed at what they're able to capture.
Even though they do hallucinate, even though they do make things up. It's
really impressive what they can find. And you just have to sit there and
wonder, Wow, all of this stuff is embedded in these models, and
it's just deep in there. It's gonna take us years to find out what's
in there because there's so much stuff in there already, right, I mean,
like, how do you even how do you begin? Yeah, and
like everything is, you got to start one foot in front of the other.
But you've got to be willing to try. And I think that's that's
the big that's the big lesson in all of this, and certainly, you
know, even the last ten years with the you know, going from a
dupe to cloud data warehouses like Snowflake and then data bricks, is you've got
to be willing to take a look at these new technologies and think about it
critically. Is like how can I take advantage of this technology rather than go,
oh my god, I'm going to lose my job, right, They're
not going to need me anymore. It's like, well, yeah, they're
not going to need you anymore if you don't learn how to do new things,
if you don't learn how to use these new technologies, you know,
figure out how how to apply yourself to the new technology with the knowledge that
you already have, you know, whether it's you know, in the data
world, we're talking about data management. You understand things like schemas and SQL
and all that. Well, how can you apply that in the ll M
world and the SLM world? You know, how can you be of value
to your organization helping them make these transitions to the newer technologies for the advantage
of the organization. Yeah, that's very interesting. I mean you mentioned earlier
you schema on read, and now with these llms, it's almost it's almost
like you can do that to a certain extent, right and schema, but
not in the way that any of those sequel people ever dreamed of, right,
right, So that we now have an a an AI that can read
the schema right right, that that somebody doesn't have to look at the er
diagram and know how to write the joints. Now, the downside, you're
I guess the caveat and all of this and you mentioned it kind of in
one of your earlier statements about having your data warehouses and all that is.
This presumes you've got a good, a well structured data architecture, that there's
metadata at least that tells you how the data is related and what the data
means. The semantics of the data you talked about, you know, the
you know, having a lexicon of say a law firm. You know you
got that terminology there, well, you know the underlying data. There's got
to be a relationship there between that legal legal ease and the data in order
to be able to ask a question and create a prompt that will get you
the answer you want. And so it presumes it it's your data structure that
is documented and that it's high quality, right, because the the ll M
is not going to discriminate between a bad row of data and good row of
data unless you've somehow programmed it to say, you know, ignore all the
roles where data is null. Right, So you're you've put a data quality
check into the prompt itself. Right. I know I don't want to do
analysis on things where the sales date is blank, right, because obviously I've
got a problem. But that you would have to know enough about data quality
and know enough about the data to ask a question that way. So otherwise
you got it somewhere. Somebody has to still do you know the hard part
that we've always done there with with building that data platform and ingesting the data
and curating the data to get you use that word curating curated data that we're
going to use to feed all these things, right, And I think that's
going to be that the next big push is figuring out these pipelines to feed
the l ms. Do you have to get your your sort of foundation in
place, but then it's going to be updating these things and then monitoring over
time. But folks, don't touch out. That'll be right back. You're
listening to Inside Analysis. Welcome back to Inside Analysis. Here's your host,
Eric tabnaugh show. All right, folks, back here on Inside Analysis talking
to the one, the only. Kent Gratziano is going to be at Data
Universe April tenth and eleventh in New York City the Javit Center. Don't miss
it, folks, It's going to be fun. Yours truly will be there
and I'll throw one of my other curve balls at you. Can't just because
you're such a good guy and I know you can hit good curve balls.
What I'm going to talk about one of my talks is the death of journalism,
as I call it, And what I'm really referring to is the fact
that there's no media company who can stand up to these large language models and
the power of this AI in terms of personalization, in terms of covering everything
that could be interesting to someone. And I think what's going to happen here
is that the smart media companies are going to figure out how to do what
we mentioned. They're going to get their own model. They're going to train
their model on their voice by loading all the past articles as embeddings. And
then what's going to happen is journalists are going to be more like curat and
editors, and you're going to be spinning out stories from facts. You have
to have your fact sets. This is really really important fact sets. And
that's systems of record. So I think like sales tax systems, for example,
I think a municipality has to collect sales tax. They're collecting all this
data from all the different stores and you determine what products are sold. Most
have barcodes so you can be able to do some analysis. Wouldn't that be
a fantastic service to subscribe to as a business person. Let's say I run
a small like an easy Mart or something like that, to be able to
see who's buying what. And the example I give is squish mellows. Like
one day you're just looking at your report, like, what are these squish
metal things that everyone's making tons of money on? Look into this, Oh,
there's some new toy for kids that kids freaking love. So now kids
have to have every squish mellow under the sun babies, it is. It's
a new It's exactly what it is. It's the new beanie baby with lots
of different sizes. My point is that it's not going to be so much
individual reporters going out spending all day writing something, as it's going to be
dynamically generated bits of information from systems of record, spoken in text that is,
from a large language model that can be altered. Because now, like,
it's like having a reporter that you can ask questions of all day long.
No, wait a minute, tell me more about this. Tell me
more about that. No reporter can sit there and answer questions for everyone all
day long you just can't do it. So I think there's going to be
a whole movement of data engineering around media, of connecting to these large language
models. And you still have reporters and journalists who will sort of review this
stuff and write some of their own original content for their voice. But to
me, there's no stopping this train. It's like a high speed train coming
at all of us. What do you think? Well, my one question
there is like, but where how do we collect the facts to go into
generating these stories? I like the idea of being able to ask the questions.
And you know, right now we've got all kinds of clickbait headline,
so it's like, you want to ask the question. You read the article
and it doesn't quite tell you. It's like, well, right, where
what was the vaccination rate in New York State between March twenty twenty one in
April twenty twenty two, broken down by county? And you might want to
get that detail because you live in a particular county in New York. You
want to say, well, what really happened? The headline says vaccination rates
dropped fifty percent over what it was pre COVID or something like that. And
you want to get the details, but the article doesn't quite cover it,
but you want to ask those questions. But again back to what's the source
of the data though, where's that information going to come from? If you
don't have you still have to have I think the reporters in the field.
Somebody has to go out and interview people somehow, and it might be via
zoom like this, right that you find, Hey, here's a person who
has information about something that just happened, and so we get them on a
zoom call and we talked to them, and then the zoom AI transcribes that
transcribes that conversation, so nobody's actually having to type it all up, right,
And then that could be a source, I guess for the LM.
But somewhere there's still got to be that interaction with the external world somehow,
right, Yeah, And I think that's going to be these systems of records.
So taxes is one, because every county collects taxes. There are other
sources for government, like the Federal Register for example, wherever actions are taken.
So I think like your ERP system, basically that's tracking the movement of
goods and the sales and all this kind of stuff, salesforce, That's all
I was mentioning. You want to tap into all these different systems and get
a feed from them to be able to ask questions of your business and so
of the government. You could do the same thing. And I just think
about how much there is to be gleaned from these from these environments of ours.
And you think about even policy documents. Right. A buddy of mine,
Jim Harris, was saying he was talking to a friend who works for
I think a Canadian government organization, and he was like, Oh, I
need to fire this person. How can I do that? I don't know
what the paperwork is going to look like. And he said, just asking
LLM. And it's like, okay, he asked, and it's like okay.
Step one you have to file a letter of grievance and say hey,
your job is not performing well at et cetera. Step two is to wait
two weeks. Step three is do this and all these things like, because
if you've fed all this documentation into a large language model, it can give
you a good summary. And so I mean, wow, think about,
like in the court system, how you have all these different motions you can
file and you have to found the motion on something based on this, based
on that case law for example. I mean there are these stories of an
attorney who just used whatever the LM gave him and got in trouble. I
almost think that's law. Yeah, it actually referenced, it didn't exist,
right, and and that's a problem. Right. But a small language model
that's trained on all the case laws, it's going to be a very different
story. And now you can check things. And I mean you can even
work that into the workflow to say, as your RAG model, check and
make sure that these are actual cases at the So again that gets back to
the workflow stuff. Well that I mean we've paid lawyers hundreds even thousands of
dollars an hour sometimes to know the nuances of these codes and these policies.
Well that you know, you don't have to do that anymore. To me,
that's just a huge change. It's I think about just you your manual,
like your you know, your code of conducts or something. For these
big companies to have like big thing or even the loss that they're passing these
days a thousand pages. Oh we can't. There was a famous politician who
said, oh, what's the point of reading it if you don't have two
days and two lawyers, Well now you can. Now you can just feed
that sucker until on them and ask it all kinds of questions. What are
the strangest things in here, what's the most expensive, what's the least expensive?
Point being that is a massive gains changer for day to day workflow.
And it's not just text generation, it's not just image generation. I mean,
those are two categories of use case. But to me, the analytical
side is the most interesting because it does have all these different ways of looking
at things because of all the parameters. Right, Yeah, no, I
think I think you're You're not wrong on that. I'm still thinking about the
journalism experience example, though, as to how that's going to work, Like
you can see the other end of it. But I still say you have
a sourcing issue. You've still got to have the humans involved somehow to get
the basic information for it. Though. I guess, you know, these
days you could, I guess you could scrape everything off of Twitter because there's
uh, you know, we talked about citizen data scientists in the past.
Here with democratizing data, we've got citizen journalists out there. Unfortunately, you
don't know necessarily how accurate the reporting is, but I guess if you have
something happening in the world and you've got you know, twenty or thirty Twitter
feeds that are reporting it, that are that are not just re reposting what
somebody else posted, but are actually taking pictures themselves. And now we've got
ais that are now able to start doing things like analyze right, pictures right,
and say what's in that picture? Say these these five these five posts
all have pictures supposedly of this event, and it should be able to overlay
them and go, yeah, those pictures, they're unique pictures, but they
are of the same area, and you know, you get the kind of
the landscaping overlay of this line here that yep, that's that same building in
the background, and be able to verify all of that, right, Well,
that is a very very interesting point, and so kind of to that
end, I thought of this a while ago. And cameras, all these
digital cameras, they have their own metadata, right they have. Some of
them have GPS, so they know where you are, they know what time
of day you took the picture. They and they have the medidated. I'm
sure that's encoded in the file, like we talked about parquet files. Right,
it's got the medidata baked in there so you can vet and see uha,
this one was doctor that one wasn't. But to your point, you
could aggregate on demand when there's a big event, like when there's a riot
someplace, or when you know there's a smash and grab or something, to
be able to dynamically pick up little bits and pieces of that. I could
see an AI engine writing a story there was a row at Bob's bar last
nights and you know, twenty seven people were involved, Like you could get
some juicy details and Bob Jones was taken to jail because you get his mugshot
or something. I mean, it is possible, But to your point,
you have to have some source. So Twitter becomes a source, social media
becomes a source. Official records like police reports and things of that nature becomes
sources, and those those things have to be accessible. That's a's the ither
thing that the whole data sharing thing, whether it's a government source, linconomics
data you were talking about, the tax data you're talking about, it has
to be accessible somewhere so that so that the LLM can get to the data,
or you could build a data pipeline to pull it in. That's right,
No, that's exactly correct. And I think there's gonna be a lot
of pressure on those systems because when this stuff starts to take off. I
mean, apparently that's why Elon instituted that rate limiting stuff for a period of
time, because they realized that someone was sucking all their data out and using
it to train other models because here it was a free source and you could
use it. So he was trying to stop that. It's just crazy the
things we're dealing with these days. But don't touch out. The podcast bonus
segment is up next. We'll be right back. All right, folks,
time for the podcast bonus segment Here on a fantastic inside analysis with our profiles
in data Sage Kent Gardziano. We've been talking all about the Data Universe conference.
Coming up is the April tenth and eleventh dates just a few weeks away
now. Will do a fireside chat with yours truly about privacy, which of
course conjures up images of governance and security and data management and ethics and all
that fun stuff. And there's also the Data Vault conference coming up in the
beginning of May, Is that right, Kent? Yeah, yeah, w
W DVC. It's gonna be the tenth anniversary that flies when you're having fun,
Oh yeah, when you're not in Stove. Vermont's beautiful, beautiful location.
It is a little harder to get to than New York City, but
in al for but it's it's worth it. It's worth it to get there.
Well, it's funny speaking of getting there. When we drove, when
you and I met there, which is like I guess the year before COVID
twenty nineteen maybe nineteen, and if we drove and you know, higher forces
were watching out four of us because I'm trying to look at the snap like
to understand what this is, like the straight thing we get and we realize
it's a fairy, Like we better get that fairy because and we were there
just in time to get the last one and across the southern end of Lake
Champlain. Yeah, I guess so, because we would have had to drive
all the way rount it would have been a really you'll be glad to know
there's a bridge there. Now it's there, Okay, there there is a
bridge. It's a it's a Whitey drive through the countryside there in in the
far east border of New York State, the very southern end of Lake Champlain.
But there's actually a bridge that goes over now, so you don't you
don't have to wait for a ferry. There's actually a real bridge that got
put in there. I must have been during COVID, because I you know,
when I went after COVID and drove they say, oh, hey,
this is great. And I used I used Google Google Maps, and I
was just following the map going it looks like there's a bridge on the map.
I say, okay, I'll go that way, sure enough, and
it goes into southern Vermont. Well. And it's funny because, you know,
in terms of following bots and just doing what the bot tells you.
I heard someone give a real good example and say you're already doing it when
you use your map speature on your phone, just trusting what it tells you
to do, and like, oh, here we go. And I think
that is going to be a very interesting other change in our industries, these
little AI agents doing little things, just scanning around looking for stuff. I
mean, you and I are to talk about privacy, and of course that
again brings governance into the picture and ethics what you what you should do ethically.
I think that there are all these conversations happening now because we need them
now, because guess what, this is a big deal. And you know,
I'll tell you one of my other big visions here is these companies like
Amazon and Google and Facebook and LinkedIn and others have done very well by using
our data to train their algorithms, which of course they then use, they
say, to give us better stuff. I've never yet bought the argument I'm
going to get better advertising, like you know, I don't think I've seen
better advertising exactly. It's like, you know, really, is that that's
the that's the caret the end of the stick here, as I give up
all my data and then you I know that you're gonna give me good things
that people are trying to sell me, and I haven't seen that. But
I will say, there is some cool stuff on you know, some cool
gadgets these days. So I've come across some of those on Facebook and some
other places, so there is some benefit to it. But I want to
be able to own our algorithm, like everyone talks about being able to own
your data and get some money like secondary income. I mean, I think
that the numbers aren't quite there to really make that interesting, Like how much
data will it be? Is that? Like how much Spotify pays small bands?
I hear people joke, Yeah, I got my check for a dollar
forty nine in the mail, and how exciting is that? There is this
scale problem, But I do feel like still all the power is centralized with
these big organizations, and I'd love to find some way it's kind of pull
that out and get some more power back in the individual's hands or even in
aggregate you know, Like I wrote about what I referred to as a consumer
facing data leg of information about transactions. So you think credit card companies sell
their exhaust data to investment banks so they can optimize what they buy and the
stock market and other reasons like that. I think when they sell our data
to some third party like that, they should also provide an anonymized version for
this consumer data leg where you and I and anyone else could kind of log
in and just see interesting data about what's happening in the world. How many
apples are being sold, how many cars are being sold. That's interesting information
for business people, and this kind of gets back to the journalism thing,
because, especially for business people, what I want more than anything is information.
I don't really want attitude or spin or narrative or all that stuff.
What I want is facts about what are people paying for this, what are
people paying for that? How long does it take this company to do that
job, how long does they take that company to do this job. That's
really useful information, and it's just facts. It's just basic facts that come
from transactional systems. So that's my big vision for the future. But what
do you think about that? Oh? Yeah, I'm with you because you
know, Dan lind said, who invented data vault? He he used to
say, you know, the data vault is a historical repository of the facts
from your source system. It's not a single source of truth. It's a
single source of facts because you know, truth becomes malleable to a certain extent
with your interpretation, right. You know, But to to create the information,
for to be information and to be informative, it has to be based
on facts. So you've got to start, You've got to set that foundation
right. And so having a you know, a we don't have to have
a single source of facts like we used to. That was our goal with
data warehousing was the single source of truth. You know the technologies. Well,
not everything has to necessarily be in the data warehouse, provided you've got
the data pipelines and you know the right business use cases for it, but
you do have to have a source of fact in order to do this,
because you don't want you know, guesswork going on, you know, unless,
of course you're dealing with customer sentiment, which is different. But then
even that, getting customer sentiment off of say, off of x or Facebook
or LinkedIn by people posting about your product, those are facts. This customer
said this, right right, And from that fact and a lot of other
similar facts you can start to evolve then an understanding of customer sentiment about your
product, right right. That's so cool. I love that as single source
of facts. It makes a lot of sense because, and I'll throw one
last teaser out here, I came across as stat as I was preparing for
my talk on the death of journalism, and the biggest challenges are bias.
Quite frankly, there's bias. There's also something in might like there's misinformation,
there's disinformation, and there's missed information, and to me, that's the biggest
one. Go ahead, you get I'll give you another one, poisoned information.
Poisoned Yeah, yeah, Dan alerted me to that one. There's been
a couple of articles about that, about poisoned data. So think of it
as reverse hacking. Instead of somebody hacking into your system and stealing your data,
they hack into your system and inject bad data so that your algorithms come
out with the wrong answer. Wow, that's crazy. Isn't that crazy?
That is crazy. Well, it's been so much fun talking to you.
Can't You're such a rock star in the industry. I've always looked to you
for advice on what's happening. I look forward to seeing you in New York.
And it's a couple of weeks. Folks up online of Data Universe and
we'll see it a couple of weeks. You've been listening to Inside Analysis.
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