I'm Chris Karagio, NBC News Radio kc AA Radio, Loma Linda, where
no listener is ever left behind. The information economy has a ride. The
world is teeming with innovation as new business models reinvent every industry industry. Inside
Analysis is your source of information and insight about how to make the most of
this exciting new era. Learn more at inside analysis dot com, Insideanalysis dot
com. And now here's your host, through Eric Kavanaugh. All Right,
ladies and gentlemen, Hello and welcome back once again to another episode of Inside
Analysis, the only coast to coast radio show in the US of A that's
all about the information economy. Yours truly, Eric Kavanaugh here, and what
a treat. I'm actually dialing in from way across the pond. I'm over
in Vietnam, big thanks to FPT Software. Their headquarters is right here in
Hanoi, Vietnam. So I'm here for their big annual event what they call
their own cees, if you will, and they're doing some amazing things.
They're going to announce a big partnership later on tomorrow with Landing Ai, which
is a huge AI company. So just like lots of other firms, like
Data Bricks and Amazon and Microsoft. These folks are going all in on AI.
Google of course as well, is doing the same Oracle as talking about
it, IBM is talking about it. Everybody is going all in on AI,
and y shouldn't they. And for today's show, we're going to take
a little bit of a twist. I'm going to be interviewed myself. That's
right, yours truly will be the subject of an interview with DJ Murphy of
Ourx Global. They are the folks who recently took over the Big Data London
conference in of course the UK, and they're also planning right now Data Universe,
which is going to be a nice big event on data and AI and
analytics and so forth in New York City next April eleventh and twelfth. I'm
very excited about that because we missed the Strata conference and these folks have come
along to fill that void. And my good buddy and longtime friend and colleague,
Mike Ferguson is the one who got me connected with this group. He
was the conference chair over at Big Data London, and I can tell you
it was a huge success. They had something like twenty thousand registrants and fifteen
thousand people show up and they were very clever. They gave the tickets away
to the delegates, so you don't have to pay to go into Big Data
London. Anyone can go in and from what I heard, the audience was
absolutely fantastic. A lot of senior level people, a lot of practitioners out
there learning about different technologies. What's going on in AI and what is not
going on in AI? My goodness, it's everywhere these days between these large
language models, so called foundational models, and the transformers. Of course,
I've got a thread going with Jurgen schmid Huber. He's the guy who wrote
the papers on the transformers, and you'll hear about that in this interview with
DJ Murphy, and you hear a lot more. But if you want to
find out more about Data Universe, send me an email info at Insideanalysis dot
com. And with that, here's my interview with DJ Murphy. Take it
away. So I'll give you a quick background on DM Radio. I call
it the longest running show in the world about data. We've been broadcasting since
two thousand and eight. It was first just online and then about nine years
ago, I guess we cracked onto the actual airwaves and brought the show to
radio stations. It's throw at about twelve or fourteen markets around the country,
some big one San Francisco, La, Chicago, Atlanta, et cetera,
a bunch of different markets, and the whole point is to just talk about
issues related to data as a resource. So we talk about data, warehousing,
analytics, data ingestion, data movement, data cleansing, data quality,
anything to do with using data at some significant scale. And we typically get
three guests per show, and the whole idea was to create this roundtable style
discussion where each guest gets their own segment of about eight to nine minutes to
talk about what they're working on, what they see in the marketplace, and
then we all do the roundtables at the end, so it allows everyone to
get their perspective out within the context of this conversation, and then the roundtables
allow us to kind of tie it all together. So we're in year sixteen.
Next year is going to be our seventeenth year, so we talked to
lots and lots of folks, a lot of things have changed obviously over the
years. And then we have another show inside Analysis, which is really all
about the information economy, new jobs, new roles, new titles, new
business models, new ways of doing things. Anything along those lines qualifies for
that show. So it's a bit more broad and focus. And then about
four years ago, I guess I got nudged into creating a TV show out
of this stuff. We have a show called future Proof, which is really
it just repurposed clips from the radio shows. So we have a student in
Chicago the records, they do all sorts of different camera angles to make it
feel like a TV show. Let me just carve that out and put it
onto about six or seven markets now around the country, including Washington, d
C. So we just got our own time slot in DC on Channel ten,
which is like one of the cable companies there at Channel ten. We
got our own time slots. So we're pretty excited about that. That's crazy.
Yeah, great, These are all things that we can use to promote
the show and obviously get speakers, get luminaries, advisory board members, really
whoever. That it's fantastic, what a platform it is. And I'm we're
so really so happy that you're able to help out on this. Yes,
it's great. Are you based in Chicago, No, I'm from Chicago,
but these days I'm based in Pittsburgh. Okay, I asked. We were
just my family was just out there last weekend. My son is a freshman
at De Paul, So oh, congratulations. Yeah, I know, it's
right where I am. We're in New England, so it was kind of
a trip for us. But I love it. I went to school in
the Midwest, so familiar with Chicago and loved and I was really happy when
he decided to let's agree that. Yeah, it's great. It's an awesome
neighborhood, love the area and so forth. So yeah, so you kind
of got into this just a little bit there. Do you do you have
sort of a basic message or fundamental themes that you hit when you you know,
each time you get on the air, whether it be radio or TV.
I mean, what, uh, yeah, okay, go ahead.
The standard approach I'd take is that we do topics so I can send you
if I haven't already, I'll give you a list to the the ed cal
And we actually just folded the last five years a bet Oftertal calendars into one
Google sheet, so you can kind of go back in time and see what
was last year, what was two years ago, three years ago, all
that stuff and all the guests. So if there's anyone you ever want introductions
to, just let me know. Connected to probably almost all the people who've
been on these shows. But we do specific topics, so we did yesterday.
We did a show on the modern data stack versus hyperscale data warehousing.
Right, we had the guy from OC and Tom we had an analyst on.
I know a lot of these independent analysts too, so we can get
those guys involved however you want. I know, Stephanie said that you're not
doing a call for speakers this time. It's all going to be curated,
which is probably smart. You know, go with people you know and people
you can trust to do a good job. So I'm happy to make some
introductions there. But basically the shows all have roughly the same calculus, if
you will, in that we define a topic, we go out find speakers
who can speak to that topic, and then I just tell guests think of
two or three key points you want to make on this topic. We'll discuss
them in the pre call, which is about fifteen minutes, and then I'll
use those as guideposts and navigate through a natural conversation. So we never want
a thing scripted, obviously, but we do want to know what we're going
to talk about. And then what we find is that there's always something interesting.
So a lot of times we'll pivot halfway through because we just pick up
on this interesting thread and what I figured out and this I was hoping this
would be true and it's been very true. Is that when you get several
experts in the same domain on the same show, they get excited about that
because usually they know each other. I mean, I can't tell you how
many times I've had people go, oh, I've always loved your work so
and so, and it's great to be on the show with you and stuff.
And what's interesting is that, to me, one of the real values
is just the experience itself. So we're kind of in the experience management business,
and that we just give really cool audio visual experiences to experts in data,
to experts in technology. We get them all excited about stuff and then
go on about our day, right right, Why data, Way back when
when you were sort of putting this idea together, why did you land on
data or you know, what was your experience at the time. Sure,
so I worked for a company called Damon Consulting back in two thousand and one,
and they were at data Warehousing consultancy, so doing ETL jobs, extract,
transform, load, filling your data warehouse, et cetera. And the
analyst in me just thought, I mean, this is going to be huge
because there's so much information. And it's funny because I knew it would be
big, and I've been blown away by how big it got. I mean,
I didn't even realize because I didn't back then see where the nexus would
occur between data and AI, right, And that's really the bottom line is
that to train models, whatever model you want to train, whatever you want
to do with AI, you need some kind of data as the foundation to
train it, to use it, to execute it. So data is going
to be crucial. Well how do you get to how do you understand that
data? How do you get to data literacy? So I saw that back
in two thousand and one at Damon Consulting, and then I went on my
own for a couple of years and I figured what I wanted to do is
repor a consulting firms in data because the consultants are the intersection between the clients
and the technologies. Right. So they have to understand business speak, the
business problems, the challenges, also the data, and then also know which
technology is to use and when. And that obviously is a very challenging situation
because you can look at a vendor's website and still have very little idea what
they do. I mean, I've had many experiences where I'm like, oh,
you sound like these guys like are you competitors? Like no, we're
partners. I'm like, all right, did not get that from looking at
your website. So you have to get into the weeds a bit to understand.
So I focused on doing that for a couple of years, and then
I wound up getting the Data Warehousing Institute as a client, and then I
wound up working for the Data Warehousing Institute as an employee and help them launch
their webinar series back in two thousand and five, which has been going ever
since. And with that we took the same formula, right. I love
having formulae for creating content because that way it's consistent and people know what to
expect. And so the form of the back then was, Hey, you
guys have this whole roster of consultants who come and teach at your shows.
They're not employees, but they're partners. I said, let's get abstracts from
them of what they're working on, what they want to talk about, and
then we'll shop those abstracts to the vendor community to to matchmake basically, because
TWI is a big list and these vendors they all want demand gen. Right,
that's when they want content and they want demand gen. It's pretty much
the two things they're always going to want. So we just created this beautiful
marshaling area, if you will, for conversations around data and technology and that's
been rocking ever since. And then after that, I actually pitched DM radio
to them. They didn't want it, so I was like, wow,
I'm going to do it, you know, whether or not you want it.
So I jumped ship and wound up partnering with Source Media, which is
based in New York. Media company you know, I know, Source Media,
Yepica, Banker, all these guys. They were my partner for the
first I don't know, four or five years or so, and then that
went away, and I worked with the Diversity for a while and now we're
just kind of on our own. But that's where the data journey began,
was like twenty two and a half years ago and it's just going full full
speed ahead. Well you were way ahead of that. I mean, it
is all about what you foresaw at that point. So let me and you
talked about AI and how they relate is that. I actually had two questions
here. One was like, what's the hottest topic you're talking about on the
show right now? I get the feeling that it's AI, right, Is
AI sucking all the oxygen away from other important topics or is it just that
important? Or how are you managing you know, those discussions both. Yes,
it is absolutely sucking the oxygen out of the room, without question,
also sucking the money out of the right. Really, because I was talking
to a guy who is a really sharp guy I would recommend as a speaker,
Brian Raymond from Unstructured Io. And what they do. They call it
ETL for LMS, so extract transform for large language models, and all this
stuff is really geared around embeddings. And embeddings, as you may know,
are in human terms, they're like your memories, They're like what you've learned,
right, So we all walk around with this whole stack of embeddings in
her mind, and as you can imagine the embeddings from your earliest days are
the most powerful because they're formative. Right as you're born into this world.
You just have all this capacity to learn and figure stuff out. And your
embeddings it's like your schooling, Right, those are embeddings, Like children go
to school and they get embeddings all day. And I'm kind of driving this
point home. So with AI, it's the same kind of thing. They
have these large language models, which are apps literally fascinating. I mean there's
like fifteen or sixteen of them now, and they are these modern era deep
learning neural networks basically, and so what they can do is fabricate text.
And you probably know this, but all they're really doing is predicting what text
they think you want based upon your prompts. Right. The problem that they
have is that they will make stuff up. And they make stuff up because
that's what they were designed to do. They're basically designed to fuse vectors of
information into a string of text or into an image according to your instruction.
Well, how do you avoid the hallucinations is you train the models on your
data, on your trusted corporate data. So one of my fun topics here
and I'll even throw this out as the potential session is large language models represent
a second chance for data. And what I mean by that is we have
spent forty fifty years now doing all sorts of calisthenics around trying to organize data,
analyze data, integrate it, curate it, do all these things in
lots of systems. And there are, of course databases, there are analytical
systems. There are data catalogs, which are a fairly recent entrance to the
market, well fairly recent fifteen years or so, twelve years, and there
are all these wonderful things. But it's a mess out there, and there
are all these different ways of doing things. And if you were to look
at the if you were to appreciate the corpus of data for any organization,
it's a mess. It's all over the map, it's in all sorts of
different formats. It's in your email, it's in your powerpoints, all these
different things. Well, Brian, what unstructured does is they will take your
powerpoints, your PDFs, your word documents, your email, whatever, They'll
extract the semantic meaning out of that and then publish it as an embedding in
a large language model or in a vector database basically, so they are tackling
that side of the equation. Right. This is not a perfect metaphor,
but if you think left brain right brain, let's say, let's say the
left brain is your ability to talk and to have vocabulary into no words.
Your right brain is everything you remember. It's the base gates, the repository
of information you use to answer questions. And that's what we're seeing here with
these large language models. So what you want to do is use the LLM
for its text generative abilities, for its image generative abilities in that case,
but you really want to make sure that it's leveraging your trusted data. So
what does that mean. It means all these companies have to find a way
to get their data into embeddings, and what I'd suggest is only put the
good stuff in. It's like, you know, I'll break my own rule
and letting our kid watch TikTok. We try to make sure we know what
he sees. But those are all little embeddings that are coming in. They're
picking up all sorts of little things, and it's like, you want to
be careful about what goes into that that matrix. Right, So he is
a good guest, and he used this term that I just absolutely love.
He said, these embeddings are anchors of truce. I was like, oh
man, yeah, that's good, right, because you want they're basically moorings.
They're gonna they're gonna bend the wave, like bend the light wave toward
them such that it'll pick up the interesting things and give you good content on
the other side. Right, so and then other you know. So,
just the other day on a radio show, I learned half of what I
know on these radio shows. We had a guy, Bill French, who
talked about how Google's next thing is they have built a vectorization layer underneath their
entire architecture such that if you're using Bard it's not it's not in everyone's account
yet, but I think it's coming. You can type at drive and then
at Gmail and it'll automatically point to your corpus of pre vectorized data in your
g drive and we'll pick that up right after the break. You are listening
to Inside Analysis, expected Welcome back to Inside Analysis. Here's your host,
Eric Tabanac. Okay, back here Inside Analysis, and here is the continuation
of my interview with DJ Murphy of RX Global and Data universe. So here
unstructured is trying to make their money telling you, hey, we can vectorize
all your content. Google is like, if you're on GCP, we already
did it for you. Don't worry about it. Like what I mean,
think about how crazy that is? So long story short, Yes, LLLMS
and AI and the foundational models are absolutely sucking in all the energy. And
it was Brian Ramy who told me that there's like thirty billion dollars in investment
in AI related to LLLMS, like the last nine months or something. So
that's clearly because you know, the vcs are always trying to predict the future,
right, and they always they always move in packs. There's usually a
first mover and then all the rest go in behind. And you just saw
this with Amazon throwing four billion at Nthropic. How much that's a lot of
money to throw at something. So they all see it, and I see
it too, I'll tell you honestly. Even though the hallucinations are a problem,
and again that's when you don't have good enough moorings, don't have enough
angers of truth, that's when you get the hallucinations. My personal opinion,
there will probably be people who disagree with me on this. I don't think
there's anything that's not in the crosshairs right now what gets done because when you
get down to brass tacks, access to data. So data retrieval, data
persistence are crucial components of every application. No matter what you do, any
software you use must have data Otherwise what is it doing. You know,
it needs data as it's fuel. It has some sort of form of data
as its output. So what's not in the crosshairs. You can use these
llms to write your emails, you can use it to write articles. I
mean you have to fine tune it. I think if you're going to use
that responsibly, you have to be careful about that. But even in my
opinion, and this is very controversial, I think a lot of the value
that's being wrought from data warehousing environments, which is very expensive stuff, can
be delivered via these new foundational models. So, in other words, in
dead warehousing, they put tremendous amounts of effort into extracting data from source systems,
transforming it into a schema that can fit into the warehouse, then managing
the warehouse doing analysis on that aggregated data. Well, you know that's very
expensive, it's very time consuming, and the bottom line is you're in terms
of what it's actually used, what actually generates value for the business is probably
I'll be five percent of that data. So in other words, you're theoretically
wasting ninety five percent of your time in this old model. Now, the
one person if you look at the big companies, you got Terra Data,
you've got Alterics, you've got Data Bricks, you've got Snowflake. These are
all the sort of big data warehouse, data analytics vendors. Of those,
the one who seems to really know what's happening here is Ali Gatzi of Data
Bricks, because he's the guy who threw one point two billion to buy Mosaic
whatever it was, six seven, eight months ago. And the week that
deal cleared, I had Navien Raw on Damn Radio, so I interviewed the
CEO of Mosaic like that week and he's very smart guy, as you can
imagine, and he was saying that you know, right now, it is
the marketing is the content creation stuff. But I'm here to tell you that
that is just the beginning. And then interactive AI is what they're really talking
about. Because I'll throw out at you and then I'll be quiet for a
minute. If you think about the complexity of these models learning to reflect back
fully syntactically correct, accurate pros, it's pretty complex, well machine to machine
conversations, even though it's very difficult for most people to understand what they're doing.
They're much less complex than that, which means a foundational model should be
very powerful in terms of optimizing your current information and systems architecture in my opinion.
So what does that mean. I mean you're already seeing a couple of
companies do interesting stuff here. Hammer Space is one. I was on the
Data Unchained podcast just earlier this week, and they're doing They are the company
that is training Lama too, So the new model that's coming out of Facebook,
they're actually doing the training of the model with their data, and they
do this hyper efficient data movement where they only move this piece of data or
that piece of data, which is a stark opposition to ETL. In the
ETL world, you extract, transform, and load huge sums of data into
your data warehouse. And the point I'm making here is that I think we're
going to see crazy efficiency gains using foundational models in AI to get to the
nuts that you want, so you don't have to shape the whole tree,
gather all the nuts, move them across town to a warehouse, then package
them and then ship them out to people. No, you're just going to
use teeny tiny little little drones to deliver eight acorns to this address in ten
acorns to that address. Wow, it is moving. Well, it's certainly
moving faster than I have any sort of conception of I'm glad there are folks
out there like you that have some sort of conception of it. And that
actually leads me to kind of what I wanted to ask next about business users
and kind of the state of the expertise or literacy on average, Like what
you see in across businesses. Are people ready to use this stuff and leverage
these advancements? Yeah? Yeah, even the ones that are available now,
and not even what's coming down the pike. I mean, just rough estimate.
I'm a big fan of the Peretto principle and right the eighty twenty rule
probably, and there are always breakdowns from there. Right, it's like the
twenty percent of the twenty percent, so probably four percent of workers are all
in now. They totally get it. They're working on it, they're focused
on it, and they're they're excelling. Overall, I'd say twenty percent are
open to it, they've kind of tested things out, and the other eighty
percent are probably just too busy doing other stuff. Right, And that's the
problem, right, is Like, one of my favorite people in the world
is a guy who got me in fact, a guy who signed the form
to get dm radio launched financially back in two thousand and steven. I guess
it was David Greeno. He had this term, and I just love these
expressions because they communicate so much. They're so pithy. He referred to the
tyranny of urgency. I was like, it's painful to hear that, you
know, And that's what most people are dealing with, is the tyranny emergency.
They got to get through their emails, they got to get through all
this stuff they have to do today, you know. Then they got to
get the kids to school and all this stuff. And so that's what's sort
of driving the reluctance or the inertia on being able to leverage these technologies.
But I think that is that's going to change pretty quickly. I think it's
going to be you know, thirty seventy within three months. And you know,
Microsoft is a reason for that. Google is a reason for that.
You know, the fact that you don't have to go out and like buy
some software and install it, well, that's big news. I mean,
honestly, if you if you want my honest opinion on one of the most
important things to have right now in the software world for survival. It's called
install base. Like just people who are already using your stuff. Right.
So I got an email from some girl at Google yesterday saying, Hi,
I'm your account executive. I've never heard from any account executive at Google first
time ever, and she's like, oh, prices are going up and this
is why, et cetera, et cetera. But she pointed out that if
you're logged into your Google account, you're just going to get all this new
functionality, right. So the fact that you don't have to go out and
buy something like you used to in the on prem days, that's a big
deal. So that's going to reach the tracks to usage. Now the question
becomes, you know, how useful is its? Have they sorted out the
guardrails? I mean, that's one of the issues is the guardrails on this
stuff, and so what are those Well, to a certain extent, those
are embeddings. You know, you can do things different ways. There's this
RAG model that came out of Facebook and it has to do with how you
actually persisted. So retrieval augmented generation is what it's called. It's a strategy
that helps address both LM hallucinations and out of date training data. So basically,
you use, you go into the language model, you call out your
stuff, and then at the last minute you're like, all right, let's
check and make sure that's right, and you know, let's throw another guardrail,
if you will, into the picture. And so the guardrails are going
to be really important. But the problem is everything is moving so fast,
so it's like people just want to go, go, go and use this
stuff. You do have to be careful that you know it's not hallucinating,
it's not making stuff up because you know, for example, chat GPT.
The first time someone searched me that I saw, they showed me what it
said and it was very accurate. I was like, wow, that's pretty
impressive. The second time I did it, it said I'd written three books.
I was like, are you sure, they usually don't write a book
and then black out, but you know, yeah, so it's just you
know it that is going to be a real serious issue going forward because you
know, think about it, We've had to deal with fake news and now
we have this issue of hallucinations where these powerful engineers just make it stuff up.
And when I was watching the Data Bricks conference, there was a girl
doing a great demo about how they are allowing the training of these models.
And that's what you do, is you train it basically. And here's one
of the sort of dirty secrets, if you will, of old deep learning
modules and old AI and new as well. I had a guy in the
show. I'd have to look up his name. He's a very smart guy
to Boston somewhere, and he's one of the experts on deep learning and AI.
And he told me a stat that blew my mind. He said,
when you're training this stuff, and that's when you have human beings just saying
yes or no. You may have seen the thing is as a blueberry muffin
or a dog. That whole thing if you come across that, because like
the AA engines have a hard time person figuring it out. He goes.
As it turns out, very few people are good at that task because it
requires you to be very focused and to always answer correctly. It's like those
stupid captures that you'll get when you're trying to log into a system and like,
Okay, which of these pictures has a motorcycle in it? Like?
Really, when do I And that's a fun story too. Did you know
that you're actually training models when you do that? I did not know that.
Yeah, that's Google and they're doing they they had this crowdsourced effort basically
to train models to identify stuff. So you're when you do the capture,
you're actually helping train a bit model for the future. Do you think that's
become its primary purpose and not not the security of whatever? You know?
That's a very very good question. Honestly, I absolutely detest those captures.
I mean, I understand why they're necessary, but they always forced me back.
I don't know's it's a pain, and they're they're vague sometimes to the
to the point of of you know, them using it to maybe that's what
they're experimenting with as far as the training goes, right, But yeah,
so that's kind of interesting. So like aside from AI, where are do
you see trends or anything where people are investing their time as far as professional
growth goes for businesses and employees within these businesses, what they're kind of looking
at, well, clearly clearly they want to educate themselves on AI. That's
that's going to be the biggest thing. But well, data is data again
is the important thing. So data lineage, data quality, data movement,
data pipelines. That's a very very hot topic because data pipelines are these as
the name would suggest, connections from sources to targets. So what used to
be just et L extract transform loads is now much more sophisticated. Where you'll
pull from this system and what they do is they'll use a graphic interface where
you have different nodes on a pipeline and one not will be to access the
data. One no will be to check the quality of the data. One
note will be to transform the data into a different scheme of for example,
one note will be to enrich the data, so add some information from another
source. Basically, data pipelines are really really important. Real time is really
important. You know, that's a big deal. There are other technologies too
that are still important. Even though they're kind of on a back burner,
blockchain is still interesting, interesting things going on in that space around durability but
also immutability is a big one. But the blockchain has had trouble, you
know, finding its relevant use cases. But I think it's it's still out
there. It's still an important thing to have, and we'll probably come in
handy. And then just analytics in general, being able to number crunch.
Hyper scale is an interesting topic in and of itself. We had Chris gladmin
of oc AND on our show yesterday, and they're based out of Chicago.
In fact, he's from lamonts where I used to be the reporter and editor
for the Lamont Metropolitan newspaper. That was my first job right out of college.
They stumbled into a newspaper job. But they're doing hyper scale data warehousing
where we're talking, and they're using these new envme cards which are so much
more powerful than the old ones. So he was talking about how when you
retrieve data it's in packets, it's like, you know, four bits basically,
well, these they can do two thousand, so you're talking about five
hundred x. You're talking about orders of magnitude greater and they're just doing crazy
stuff, these huge, huge data warehouses, petabytes of information, you know,
so hyper scale is an issue. Cloud is an issue. Of course,
security is always going to be an issue, you know that. And
with that, will take a quick break and we'll pick up the conversation once
again. Right after these messages, you were listening to Inside Analysis. Welcome
back to Inside Analysis. Here's your host, Eric Tavanaugh. Okay, folks
are back here on Inside Analysis in Media Race as it were talking to DJ
Murphy about hot trends like hyperscale and cloud and security. And here are my
thoughts on security and passwords. I think passwords are the biggest pain in the
freaking universe and I'm still not entirely sure why they haven't solved them. They're
starting to with the biometric stuff. You started to see more apps on your
phone leverage that, so, you know, security, even though the audience
is pretty small, it's always important. And governance, governance at the other
really big important topic. How do you govern access to these data assets because
in the old days you had two options. Basically, you could govern it
at the database layer, meaning you're allowed to access the database or you're not,
or you could govern it at the application layer, meaning okay, user,
your your role is X, you're a manager or whatever, I'll govern
it that way. Both of those are very inefficient ways to do it.
Certainly, the database one is incredibly inefficient because you only want your database administrators
touching that. And you know, if you have four thousand people who are
allowed to use this analytics software, you know, having Bob go in there
every day and take take out Susie, Okay, she will go out.
I mean, you know, automating that, that process of governance is huge.
So I want to turn to the event a little bit. You're a
broadcaster. You want to get your message out to as many people as possible.
How important do you think like a micro sort of situation like a face
to face event is, or like the propagation of knowledge or or whatever.
I mean, you're obviously in this you want to you're helping us out and
you're advising us on this. How important are the are the face to face
events? Oh? They're hugely important, you know, and we're seeing that
come back now, roaring back. Obviously with COVID, everyone went into hiding
for a period of time and for lots of different reasons for good reasons,
obviously, but virtual will only get you so far. And I'm a virtual
guy, so I do virtual stuff all the time. I love virtual it's
really important. But person to person, shaking hands, talking to people,
sitting down, it's always important. It's always going to be important. And
these these focused events are are really crucial, you know, because I've gone
to a bunch of different conferences over the years. I've been involved in promoting
conferences and speaking of conferences, et cetera. And the sort of broader consumer
technology ones aren't so interesting to me because I'm a data guy. So you
have to have a very focused conversation. And this is what trade press is
all about. And you know, frankly, trade press in my is dying.
It's been dying for a number of years now because largely of the Internet.
Because trade publications used to make all their money on print dads in magazines
and now those are gone. So take an industry and just rip the financial
carpet out from under their feet. That's a problem. So you know,
a handful of being able to pivot to webinars, which are one of the
biggest ones, and obviously events, but absolutely events are huge They're very important.
The session design is always important, you know, making sure you've covered
all the bases of you know, who wants to learn about what? But
I mean no doubt in person events are awesome. Do you think there's a
takeaway for this particular event that's going to be important for the people that attend
granular knowledge, you know, workshops like have a combination of sessions. Round
Tables are always fun. I love roundtables, and I'll have to moderate round
tables in part because you get the chemistry of the group involved, right and
with a single presenter, it's just one person or even two, But when
you have a round table, especially if they're you know, open minded to
how that gets managed, you can sort of build up a momentum and a
chemistry that you can't get in other sessions. Now you have to have people
who moderated effectively obviously really know what they're talking about. But that's that would
be one of my suggestions is have numerous roundtables that are really interactive and dynamic
right where you go in And I always tell people, I always joke we'll
talk about we're going to talk about, but once the bell rings, all
bets are off Yeah, it's live conversation, so you know, we'll see
where things go. So I'd be sure to do that. And then you
know, meet and great sessions and pre cocktails. That's always that's the best
part of a live event, isn't it. That's what I tell my wife.
I go to these events for the free food and the free cocktails and
like joking, half joking, but it does help as people, you know,
especially at the end of the day, like the six o'clock thing,
the seven o'clock thing, you know, that's when everyone has been talking all
day business. They want to go, you know whatever, drink wine or
even a coke or something and just chat about what's going on. All that
stuff is really important. As someone advising on the program, what do you
hope that this event will deliver to the community that you communicate to on a
daily or weekly basis that may they may not be getting Elsewhere's hard, that's
a hard fact, you know. So Mike Ferguson, he's the guy who
suggested that you all talk to me, who's incredibly knowledgeable. Guy like getting
a dozen folks of that caliber too. I won't call them fact checkers because
I don't like all fact checking thing. But they what's really cool is that
when someone when these vendors know that there's someone in the room who knows all
their weaknesses, boy do they behave they just know? We learn that from
experience that you know, if you get a round table. When I get
the end, I always want an analyst on the call if I can.
And three vendors like, it's amazing how constructive the conversation becomes because number one,
they don't take pot shots at each other. Number Two, they don't
talk about stuff they don't have, you know. I mean I often joke
that talking to especially a bigger enterprise software executive, is like talk to a
politician. I mean, they have their messaging down, they have their talking
points, and you got to find some way to get them off with that
message, to get some gritty detail. And that's usually a curve ball of
some kind or you know, some question that pops up and you have to
wait and let it kind of flow out, as opposed to you can't badger
someone, right, this is all it's all friendly media. I don't do
sixty minutes type stuff, right, I'm always doing friendly media. But I
want to get to the meet. I want to get to the hard facts
that people need to know, like, oh, you want to use this
technology that that'shed out knowledge. You know, they don't actually work well or
they are maybe they're very complimentary. You don't know that stuff until you've talked
to someone who has used it, who has built the solution, you know,
who's worked with it. That's where you find the bugs and the sort
of rough edges on things. And that's that's what I would love people to
get more of, is you know, what are the rough edges in fact
that could even be an interesting show or an interesting conversation, is you know,
what do people think is the case versus what really is the case?
AI? And it's like we learned that with that New York Times guy who
interviewed chat GBT when it first came out. And I try to tell people
he is responsible for that conversation going sideways because he misunderstood what the tech is.
He's treating it like a sentient being, trying to, you know,
get it to say colorful things, which it clearly did. But again,
all it's doing is reflecting back from the corpus of techs that it was trained
on what it thinks you want to hear. So you know, prompt engineer,
for example, is absolutely do workshops on that all day long on prompt
engineering, because that is it's really crucial to get what you wanted or something,
to give very precise instruction, and it will take that instruction like.
That's the thing is that it's like a little assistant who will do everything you
ask it to do as best as they can do it, you know,
And I always tell it please and thank you always. It won't filter out
your worst impulses. It'll just always be nice to the machines. You know,
they may wake up one day, I don't know. There you go,
There you go, Eric, thank you very very much. What I'm
going to do is I'm going to go through and do this up sort of
as a Q and A. I will shorten your most of your answers tragically
unfortunately, but I am going to send it to you before we publish,
just in case I've edited something the wrong way or whatever. And let's take
for shut. Okay, thank you so much. I appreciate your recording it.
Send me the transcript when you have a chance. And this has been
great, and hope to have a chance to meet too. And that concludes
my conversation with DJ Murphy from our ex Global Read exhibitions. To folks who
are putting on the Data Universe conference in New York City April eleventh and twelfth,
be sure to check that out. We're working on the speakers right now,
working on the themes, working on all that kind of fun stuff.
Should be a fantastic show, and look for yours truly to be walking around.
If you want to offer some advice on what you think the show should
cover, by all means, send me an email info at insideanalysis dot com.
You know, I'm always keen to find out what folks are looking to
learn, and these days it's going to be a lot about those foundational models.
It's going to be a lot about how to train large language models.
It's going to be a lot about what else is in the crosshairs. And
to be honest, folks, I don't think anything is not in the crosshairs
of these foundational models. So we're going to find out. But at the
moment, according to Navin Raw of Mosaic mL and several other experts I've talked
to, yes, it's mostly marketing copy. It's mostly copy for blogs and
articles and emails and all sorts of other things. So a lot of that
stuff's going to be going on, but I'm telling you other things are in
the cross heirs. Interactive AI is going to be huge. Interactive AI is
where we're going to get much deeper in terms of letting AI run systems and
optimize information architectures, optimize how things get done in the real world. That's
coming soon to an enterprise near you. Respect the legacy. Southern California's KCAA,
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NBC News Radio. I'm Chris Karrazio. Secretary of Saint Anthony Blincoln made a
pair of unannounced visits in the Middle East. Today, he met with Palestinian
Authority President Makmudabas during a surprise stop on the occupied West Bank. Then later
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works to keep the conflict from spreading. Two US senators who are back from
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the Israel Hamas War. If Hezblah opens up a second front in the North
against Israel in a substantial way, then we should hit the Islamic Republic of
Iran. South Carolina Republican Lindsay Graham and Connecticut Democrat Richard Blumenthal set on CNN
State of the Union. The non binding resolution calls for putting i Ran on
notice that the US will come after them if any other militias backed by Iran
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sons Rik and Donald Trump Junior, testified last week in the trial that accuses
the Trump family organization of inflating their wealth to obtain better loans. Daughter Ivanka
Trump will testify Wednesday, despite her attempt to legally block it, telling the
court she would suffer undue hardship if forced to testify in the middle of a
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any further comments about confidential communications between the judge and his staff. Judge Ngaran
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influencing the trial. I'm Scott Carr, I'm Chris Karagio, NBC News Radiohilistinea
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